PyCaret_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "736d961677af48459236cfc4a27c47e5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_780a00ac92ac4efcbe5fb5c873c8eb97",
              "IPY_MODEL_ffaaac9c620d43139617b0dece1763d0",
              "IPY_MODEL_4336bf10c14d4c94a2bd5380bc4c9acc"
            ],
            "layout": "IPY_MODEL_419f396041754b18b4bf15fea8dc5d86"
          }
        },
        "780a00ac92ac4efcbe5fb5c873c8eb97": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e2f33375c26f4081adc660ddc87e0f44",
            "placeholder": "​",
            "style": "IPY_MODEL_aca67b6208af4e14920d1794218c49c2",
            "value": "Processing: 100%"
          }
        },
        "ffaaac9c620d43139617b0dece1763d0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e6d92ceb3db24b3d9e08544e3b7a0110",
            "max": 69,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_b60cc5e2e49e449dbf67319bfddb2738",
            "value": 69
          }
        },
        "4336bf10c14d4c94a2bd5380bc4c9acc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f48ac55177684624baeae207e1fc83c5",
            "placeholder": "​",
            "style": "IPY_MODEL_4b4a94b356d14d0384a74caf5db0f6de",
            "value": " 69/69 [00:41&lt;00:00,  1.71it/s]"
          }
        },
        "419f396041754b18b4bf15fea8dc5d86": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "e2f33375c26f4081adc660ddc87e0f44": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "aca67b6208af4e14920d1794218c49c2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e6d92ceb3db24b3d9e08544e3b7a0110": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b60cc5e2e49e449dbf67319bfddb2738": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "f48ac55177684624baeae207e1fc83c5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4b4a94b356d14d0384a74caf5db0f6de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d552ed22fa7d4bff9281e1db7186b5e7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_a9dcf9df647f4cd68a04650d7fa6e39d",
              "IPY_MODEL_e40178ae092b403980a3de17f0534594",
              "IPY_MODEL_1f1cf46fe27541e9a5952ba95afb8b23"
            ],
            "layout": "IPY_MODEL_67c8b9714fb84ae281bfb1a0b979f350"
          }
        },
        "a9dcf9df647f4cd68a04650d7fa6e39d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1a552384c9bb4dc7bdd22298b5c9bd75",
            "placeholder": "​",
            "style": "IPY_MODEL_807c5a3bd4fb42bca02bf90531218120",
            "value": "Processing: 100%"
          }
        },
        "e40178ae092b403980a3de17f0534594": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_199914b070b64ce2b4cf3391e3707db1",
            "max": 69,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_fe4cbc2bd50b4cf9ae96d48abd3b7c1a",
            "value": 69
          }
        },
        "1f1cf46fe27541e9a5952ba95afb8b23": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_18eb6201e0aa4adf9832606b5ec27d68",
            "placeholder": "​",
            "style": "IPY_MODEL_4ec1aed20fce490389a06e8d23f3d109",
            "value": " 69/69 [00:19&lt;00:00,  3.29it/s]"
          }
        },
        "67c8b9714fb84ae281bfb1a0b979f350": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "1a552384c9bb4dc7bdd22298b5c9bd75": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "807c5a3bd4fb42bca02bf90531218120": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "199914b070b64ce2b4cf3391e3707db1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "fe4cbc2bd50b4cf9ae96d48abd3b7c1a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "18eb6201e0aa4adf9832606b5ec27d68": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4ec1aed20fce490389a06e8d23f3d109": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "fe12c58a8d8b4334a69555bd5f8f9b22": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e3bde63d11f946acad4867b85b24dbfe",
              "IPY_MODEL_d0d39b88563c404e88bc0fdde4395610",
              "IPY_MODEL_351c3aa064ec4c9395a6bf9d8b565dab"
            ],
            "layout": "IPY_MODEL_9b1657c5c0f743a8bcb7353d055721ba"
          }
        },
        "e3bde63d11f946acad4867b85b24dbfe": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bbe09e543ebb4cb2a50f22b199c5796d",
            "placeholder": "​",
            "style": "IPY_MODEL_bb3b1caa5e06445590cbfa1479cb0155",
            "value": "Processing: 100%"
          }
        },
        "d0d39b88563c404e88bc0fdde4395610": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4c5c5ba1e5734a13bec4dd4c57dc9681",
            "max": 69,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_8859fb0160a44cc28655789d7a478f8f",
            "value": 69
          }
        },
        "351c3aa064ec4c9395a6bf9d8b565dab": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7dd20595731c41a89338bbb775a41636",
            "placeholder": "​",
            "style": "IPY_MODEL_80a2059d0fcf4565ab4b05656c8999db",
            "value": " 69/69 [00:41&lt;00:00,  1.71it/s]"
          }
        },
        "9b1657c5c0f743a8bcb7353d055721ba": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "bbe09e543ebb4cb2a50f22b199c5796d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bb3b1caa5e06445590cbfa1479cb0155": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4c5c5ba1e5734a13bec4dd4c57dc9681": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8859fb0160a44cc28655789d7a478f8f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "7dd20595731c41a89338bbb775a41636": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "80a2059d0fcf4565ab4b05656c8999db": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b5060a53419c4af3b8e9e29f264382d1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_95214e15316349498e233119a96078c9",
              "IPY_MODEL_7fceae272a634494b1abf17fa2ecc530",
              "IPY_MODEL_f086b10a652d40559104c508c71b0548"
            ],
            "layout": "IPY_MODEL_236ebdabe666429cb2d238266bffa5ab"
          }
        },
        "95214e15316349498e233119a96078c9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e25079bea25a452ca447696a91b230d2",
            "placeholder": "​",
            "style": "IPY_MODEL_cfde94bcf49b4a22ae83795d5e206d3b",
            "value": "Processing: 100%"
          }
        },
        "7fceae272a634494b1abf17fa2ecc530": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3cb5942890ef456fb14f113f5395248d",
            "max": 70,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f0558c812cc14ea4b30dec2048ad516e",
            "value": 70
          }
        },
        "f086b10a652d40559104c508c71b0548": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1559bb6df224465f858dca4f1117609b",
            "placeholder": "​",
            "style": "IPY_MODEL_7d9d7d0028714e85a78b0936ad9df162",
            "value": " 70/70 [00:34&lt;00:00,  1.59it/s]"
          }
        },
        "236ebdabe666429cb2d238266bffa5ab": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "e25079bea25a452ca447696a91b230d2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cfde94bcf49b4a22ae83795d5e206d3b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3cb5942890ef456fb14f113f5395248d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f0558c812cc14ea4b30dec2048ad516e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "1559bb6df224465f858dca4f1117609b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7d9d7d0028714e85a78b0936ad9df162": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5eddcf2ca6694774a0d590220dc6cf02": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_1905609a80544e579941712f6420761e",
              "IPY_MODEL_cc8d80d570a541e6ad0211721f5a5c3c",
              "IPY_MODEL_560a9df2bfba4176819bfce61d475cae"
            ],
            "layout": "IPY_MODEL_60f9dd8ba7e84cccb6dd9f010e9a0059"
          }
        },
        "1905609a80544e579941712f6420761e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c7ff12f5968a4feebdffd07dc23cd063",
            "placeholder": "​",
            "style": "IPY_MODEL_f739af95a6e040ea9f24d9cdd9a43b37",
            "value": "Processing:   0%"
          }
        },
        "cc8d80d570a541e6ad0211721f5a5c3c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2e89b948f0d34ac48d0989b35f17ec20",
            "max": 4,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_8c3d830d01ad4e6584ac9222fcc9cd45",
            "value": 4
          }
        },
        "560a9df2bfba4176819bfce61d475cae": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0a9afc85966a474e8badf65716db37cf",
            "placeholder": "​",
            "style": "IPY_MODEL_e17ef2b06265473d8f7e6a3474ae5a48",
            "value": " 0/4 [00:00&lt;?, ?it/s]"
          }
        },
        "60f9dd8ba7e84cccb6dd9f010e9a0059": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "c7ff12f5968a4feebdffd07dc23cd063": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f739af95a6e040ea9f24d9cdd9a43b37": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "2e89b948f0d34ac48d0989b35f17ec20": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8c3d830d01ad4e6584ac9222fcc9cd45": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "0a9afc85966a474e8badf65716db37cf": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e17ef2b06265473d8f7e6a3474ae5a48": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "bfa59bdea60e416b8afd543fb54376a0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_0cc2080657bc42f6802ade5eebc54715",
              "IPY_MODEL_96f2f713b5ae4494b1a181743003b9de",
              "IPY_MODEL_4e96dbbe73c54a6f86af1104c9c51195"
            ],
            "layout": "IPY_MODEL_530ca4060154410891d7c26f5d6c919a"
          }
        },
        "0cc2080657bc42f6802ade5eebc54715": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e240c2a0817e4a10b8220a1fbbe46cdc",
            "placeholder": "​",
            "style": "IPY_MODEL_581d880bbddd4c048db9c0b7d3dc3ecc",
            "value": "Processing:  86%"
          }
        },
        "96f2f713b5ae4494b1a181743003b9de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f4e3267e86f04ffa9dacdf35c0ba0335",
            "max": 7,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_0a7241334c314ecd9b66fd1620b7587e",
            "value": 7
          }
        },
        "4e96dbbe73c54a6f86af1104c9c51195": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a197de60da7b4988894b6e282f63fbc1",
            "placeholder": "​",
            "style": "IPY_MODEL_1b078964b6b94be4b12476a5eba44725",
            "value": " 6/7 [00:03&lt;00:00,  1.83it/s]"
          }
        },
        "530ca4060154410891d7c26f5d6c919a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "e240c2a0817e4a10b8220a1fbbe46cdc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "581d880bbddd4c048db9c0b7d3dc3ecc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f4e3267e86f04ffa9dacdf35c0ba0335": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0a7241334c314ecd9b66fd1620b7587e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "a197de60da7b4988894b6e282f63fbc1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1b078964b6b94be4b12476a5eba44725": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f98d15710a314fd488fe926f7eff6c42": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_a58f5466f5fa4765b39b0b802a41fb0c",
              "IPY_MODEL_e4c02345ba44410f95c506bf17eb5198"
            ],
            "layout": "IPY_MODEL_e35ee408320f40018d56d2971883f2d4"
          }
        },
        "a58f5466f5fa4765b39b0b802a41fb0c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ToggleButtonsModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ToggleButtonsModel",
            "_options_labels": [
              "Pipeline Plot",
              "Hyperparameters",
              "AUC",
              "Confusion Matrix",
              "Threshold",
              "Precision Recall",
              "Prediction Error",
              "Class Report",
              "Feature Selection",
              "Learning Curve",
              "Manifold Learning",
              "Calibration Curve",
              "Validation Curve",
              "Dimensions",
              "Feature Importance",
              "Feature Importance (All)",
              "Decision Boundary",
              "Lift Chart",
              "Gain Chart",
              "Decision Tree",
              "KS Statistic Plot"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ToggleButtonsView",
            "button_style": "",
            "description": "Plot Type:",
            "description_tooltip": null,
            "disabled": false,
            "icons": [
              ""
            ],
            "index": 13,
            "layout": "IPY_MODEL_9aede8dbf11a4c62a24c09a71d4a85aa",
            "style": "IPY_MODEL_2d7e559b3b4c4d96b415c2e84930f551",
            "tooltips": []
          }
        },
        "e4c02345ba44410f95c506bf17eb5198": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_ab91a7e928d149d69eb90b6a86bedec8",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 800x550 with 1 Axes>",
                  "image/png": "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\n"
                },
                "metadata": {}
              }
            ]
          }
        },
        "e35ee408320f40018d56d2971883f2d4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9aede8dbf11a4c62a24c09a71d4a85aa": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2d7e559b3b4c4d96b415c2e84930f551": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ToggleButtonsStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ToggleButtonsStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "button_width": "",
            "description_width": "",
            "font_weight": ""
          }
        },
        "ab91a7e928d149d69eb90b6a86bedec8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c29526e92bc843d0abf9d761ea4f0e20": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_2a0b9974e7a6494b9c29c2db19e05d9f",
              "IPY_MODEL_52c5598c42a34139997570849868f64e",
              "IPY_MODEL_a9af3ec259c34ecfbc6f4da2090b6f91"
            ],
            "layout": "IPY_MODEL_4d67a00fcd2142b18bd52c661ab29dbd"
          }
        },
        "2a0b9974e7a6494b9c29c2db19e05d9f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b232e7bf721d46feb84f54cd272d88d0",
            "placeholder": "​",
            "style": "IPY_MODEL_22a005feec0b472080acc45f6f26ed9f",
            "value": "Processing: 100%"
          }
        },
        "52c5598c42a34139997570849868f64e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a5c3d20a39ea461c81ef285398e3d081",
            "max": 82,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_b97535e070454ae48a9df9da642e3020",
            "value": 82
          }
        },
        "a9af3ec259c34ecfbc6f4da2090b6f91": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_efb18980b95a42f8ae3f599215b87c5c",
            "placeholder": "​",
            "style": "IPY_MODEL_5a9e637708f645d4828260cbcdec4580",
            "value": " 82/82 [03:47&lt;00:00,  1.03it/s]"
          }
        },
        "4d67a00fcd2142b18bd52c661ab29dbd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "b232e7bf721d46feb84f54cd272d88d0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "22a005feec0b472080acc45f6f26ed9f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a5c3d20a39ea461c81ef285398e3d081": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b97535e070454ae48a9df9da642e3020": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "efb18980b95a42f8ae3f599215b87c5c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5a9e637708f645d4828260cbcdec4580": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d871a38d36a846799417b39a65e53b4c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_2009f88ed8f4461d8e0eb0831ff8ba79",
              "IPY_MODEL_d0b7cbd000534e518f8b8e5b39b62cbc",
              "IPY_MODEL_8247267110714c71b74bdd7b3cb5424b"
            ],
            "layout": "IPY_MODEL_e4e519d2695f4fba9360ae89637fb382"
          }
        },
        "2009f88ed8f4461d8e0eb0831ff8ba79": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1987a03e88bc4996b2813d448dbce65a",
            "placeholder": "​",
            "style": "IPY_MODEL_46c6bf976d574385a33f229ae6b2ddb4",
            "value": "Processing: 100%"
          }
        },
        "d0b7cbd000534e518f8b8e5b39b62cbc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7d6b587c5cad4a26adcfa660f1f50721",
            "max": 81,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_8b557138e0fa4d5ea6fae0076642d2c5",
            "value": 81
          }
        },
        "8247267110714c71b74bdd7b3cb5424b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_edfbb5e8b86045119a711ada35f85bc7",
            "placeholder": "​",
            "style": "IPY_MODEL_264f083602464f99b5a03b8d58ca0e43",
            "value": " 81/81 [03:48&lt;00:00,  1.58s/it]"
          }
        },
        "e4e519d2695f4fba9360ae89637fb382": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "1987a03e88bc4996b2813d448dbce65a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "46c6bf976d574385a33f229ae6b2ddb4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7d6b587c5cad4a26adcfa660f1f50721": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8b557138e0fa4d5ea6fae0076642d2c5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "edfbb5e8b86045119a711ada35f85bc7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "264f083602464f99b5a03b8d58ca0e43": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5b4430f8da1d4ea9979b69c1d747de2e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_fd298e1616b946ae8b6846b41ecddbdd",
              "IPY_MODEL_c11e3d4431ca437c9e8c2aa43aa84d1b",
              "IPY_MODEL_d334813a435b4df2b6e2bdafbe0461a5"
            ],
            "layout": "IPY_MODEL_0084e1bc95224e8394d7b6ce3f436dca"
          }
        },
        "fd298e1616b946ae8b6846b41ecddbdd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a45814b9475345c4939d9571752fb1c7",
            "placeholder": "​",
            "style": "IPY_MODEL_d292529637834042ae9d6053856599de",
            "value": "Processing: 100%"
          }
        },
        "c11e3d4431ca437c9e8c2aa43aa84d1b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b615bd35beba45efa8525ebd249aeaa8",
            "max": 4,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_1faae79f3620496eac1b120016dc7c85",
            "value": 4
          }
        },
        "d334813a435b4df2b6e2bdafbe0461a5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8b5dacea268b43f6928f15a99a361494",
            "placeholder": "​",
            "style": "IPY_MODEL_b3f553c6c25e41d68d6718bd2042511a",
            "value": " 4/4 [01:54&lt;00:00, 26.73s/it]"
          }
        },
        "0084e1bc95224e8394d7b6ce3f436dca": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "a45814b9475345c4939d9571752fb1c7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d292529637834042ae9d6053856599de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b615bd35beba45efa8525ebd249aeaa8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1faae79f3620496eac1b120016dc7c85": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "8b5dacea268b43f6928f15a99a361494": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b3f553c6c25e41d68d6718bd2042511a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8cecba0f52e345dd9cc715446f547011": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_296d985ade9243e29ddbf445af2f0455",
              "IPY_MODEL_9c13c62849a44fdb9b550e84a090739f"
            ],
            "layout": "IPY_MODEL_b12a8ad95640488783f58029be2dcef2"
          }
        },
        "296d985ade9243e29ddbf445af2f0455": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ToggleButtonsModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ToggleButtonsModel",
            "_options_labels": [
              "Pipeline Plot",
              "Hyperparameters",
              "Residuals",
              "Prediction Error",
              "Cooks Distance",
              "Feature Selection",
              "Learning Curve",
              "Manifold Learning",
              "Validation Curve",
              "Feature Importance",
              "Feature Importance (All)",
              "Decision Tree",
              "Interactive Residuals"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ToggleButtonsView",
            "button_style": "",
            "description": "Plot Type:",
            "description_tooltip": null,
            "disabled": false,
            "icons": [
              ""
            ],
            "index": 5,
            "layout": "IPY_MODEL_5e3c341c2bd540cea2beaeb40055d25e",
            "style": "IPY_MODEL_8bd76591ec8f4ac4972c4bd829e20a48",
            "tooltips": []
          }
        },
        "9c13c62849a44fdb9b550e84a090739f": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_3a4445ea3338451f83317715c4317f0c",
            "msg_id": "3ea368ad-6e6e-4cc0-b990-e96f120af4e7",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 800x550 with 1 Axes>",
                  "image/png": "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\n"
                },
                "metadata": {}
              }
            ]
          }
        },
        "b12a8ad95640488783f58029be2dcef2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5e3c341c2bd540cea2beaeb40055d25e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8bd76591ec8f4ac4972c4bd829e20a48": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ToggleButtonsStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ToggleButtonsStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "button_width": "",
            "description_width": "",
            "font_weight": ""
          }
        },
        "3a4445ea3338451f83317715c4317f0c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0a4aeedf8c194dfaab444bb3c8a8ed45": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d1c299bc977848c5b75cdac264dff908",
              "IPY_MODEL_71278a650ef549f486bb6108bafff2db",
              "IPY_MODEL_232ce3dd8cd04bfaa8afcd5bc4336d27"
            ],
            "layout": "IPY_MODEL_f4e6067f11b14721986f42ff615667e9"
          }
        },
        "d1c299bc977848c5b75cdac264dff908": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9f04cee40a3d410bbce674101f677882",
            "placeholder": "​",
            "style": "IPY_MODEL_9463426efd98485fb03f14cfe8cffdcd",
            "value": "Processing:   0%"
          }
        },
        "71278a650ef549f486bb6108bafff2db": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_081e4069d34f4ce692a58ba24efc5afa",
            "max": 3,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_2542d5e997d04091b6080723e1a769c6",
            "value": 3
          }
        },
        "232ce3dd8cd04bfaa8afcd5bc4336d27": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_74c6dfc3936849b3b2c5cf0c40c14f8e",
            "placeholder": "​",
            "style": "IPY_MODEL_28741abca4bd406e9dceb790c3d908d5",
            "value": " 0/3 [00:00&lt;?, ?it/s]"
          }
        },
        "f4e6067f11b14721986f42ff615667e9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "9f04cee40a3d410bbce674101f677882": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9463426efd98485fb03f14cfe8cffdcd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "081e4069d34f4ce692a58ba24efc5afa": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2542d5e997d04091b6080723e1a769c6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "74c6dfc3936849b3b2c5cf0c40c14f8e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "28741abca4bd406e9dceb790c3d908d5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0554f901739e49e4978c853963c03fa1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d7a02cfa6b8f4b65a8adcfd280de6481",
              "IPY_MODEL_c3c8e069ffa642208110f6517ee4c730",
              "IPY_MODEL_242f764f764c4ded9d4971b81a74517f"
            ],
            "layout": "IPY_MODEL_196ce086dc3f4a159ae50c6327cde0a9"
          }
        },
        "d7a02cfa6b8f4b65a8adcfd280de6481": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_32ededba96df4c7cadd8ceafbe249a6b",
            "placeholder": "​",
            "style": "IPY_MODEL_0353a22efa0343e4b8ba0300f9c45b8b",
            "value": "Processing:   0%"
          }
        },
        "c3c8e069ffa642208110f6517ee4c730": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9dbad6929b5d4b5186e43d59bb8dd540",
            "max": 3,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3ba0bffce33946058dff732fb07cc244",
            "value": 3
          }
        },
        "242f764f764c4ded9d4971b81a74517f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_711f7bc3de024479ac1deecc2c582c4b",
            "placeholder": "​",
            "style": "IPY_MODEL_050a1dc86ed74d6dbd476501c590e51a",
            "value": " 0/3 [00:00&lt;?, ?it/s]"
          }
        },
        "196ce086dc3f4a159ae50c6327cde0a9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": "hidden",
            "width": null
          }
        },
        "32ededba96df4c7cadd8ceafbe249a6b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0353a22efa0343e4b8ba0300f9c45b8b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "9dbad6929b5d4b5186e43d59bb8dd540": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3ba0bffce33946058dff732fb07cc244": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "711f7bc3de024479ac1deecc2c582c4b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "050a1dc86ed74d6dbd476501c590e51a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e3ec36fc346d44dda3b9bb7a81ec6dcb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_4678778a5f4a4975994302b2c7a226a6",
              "IPY_MODEL_9599a19bb03f4fe88d85f36144f85274"
            ],
            "layout": "IPY_MODEL_d61be4f2df3b408fb7b23093f8abe118"
          }
        },
        "4678778a5f4a4975994302b2c7a226a6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ToggleButtonsModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ToggleButtonsModel",
            "_options_labels": [
              "Pipeline Plot",
              "t-SNE (3d) Dimension Plot",
              "UMAP Dimensionality Plot"
            ],
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ToggleButtonsView",
            "button_style": "",
            "description": "Plot Type:",
            "description_tooltip": null,
            "disabled": false,
            "icons": [
              ""
            ],
            "index": 0,
            "layout": "IPY_MODEL_147cb5148b144f52b6e375d124c83c05",
            "style": "IPY_MODEL_909c5e14e95d463eadce7d9c9f588d2c",
            "tooltips": []
          }
        },
        "9599a19bb03f4fe88d85f36144f85274": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_aa19e82be23a4a36b207c414639f1451",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1200x600 with 1 Axes>",
                  "image/png": "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\n"
                },
                "metadata": {}
              }
            ]
          }
        },
        "d61be4f2df3b408fb7b23093f8abe118": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "147cb5148b144f52b6e375d124c83c05": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "909c5e14e95d463eadce7d9c9f588d2c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ToggleButtonsStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ToggleButtonsStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "button_width": "",
            "description_width": "",
            "font_weight": ""
          }
        },
        "aa19e82be23a4a36b207c414639f1451": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dr-mushtaq/Python-Notes/blob/master/PyCaret_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Introduction**</p>"
      ],
      "metadata": {
        "id": "9d3sIjNFNkA8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Automated machine learning (AutoML) enables you to automate machine learning processes. There is a wide range of automation tools, created for different purposes. In general, machine learning tools are typically categorized into three categories of benefits—improving productivity, enforcing standardization, and promoting democratization.\n",
        "\n",
        "This article explains what is AutoML, key advantages of AutoML, and what you can actually automate in machine learning. Including a review of seven top AutoML tools [12]"
      ],
      "metadata": {
        "id": "q7AUygoDNsfC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Automated machine learning (AutoML) is the use of automation, machine learning, and best practices to make ML accessible to a wider range of users. It is designed to help organizations speed up their training and use of ML models despite limited or a lack of access to ML experts, such as data scientists.\n",
        "\n",
        "AutoML enables organizations to build and deploy models using templates, frameworks, and predefined processes. This speeds time to completion and helps ensure that models are as functional as possible[12]"
      ],
      "metadata": {
        "id": "xP4-8aHlN6Nq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Advantages of AutoML**"
      ],
      "metadata": {
        "id": "fSzeHb64OJ_B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are three main advantages to incorporating AutoML. These are:\n",
        "\n",
        "- Productivity—automation reduces the manual resources needed to monitor and perform repetitive ML tasks. This frees teams to focus on model refinement and packaging.\n",
        "- Standardization—automated pipelines help reduce the chance of configuration errors and ensure that training and tests are performed uniformly.\n",
        "- Democratization—AutoML lowers the barrier to entry for organizations with little to no ML expertise. This increases competitiveness and can increase innovation."
      ],
      "metadata": {
        "id": "1Hya33KoQ-AX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What Can We Automate in Machine Learning?**"
      ],
      "metadata": {
        "id": "lctIO_HjRH25"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although it might be nice to fully automate ML processes, that is not what AutoML does. Rather, it focuses on a few areas with high repetition. These areas include the following."
      ],
      "metadata": {
        "id": "8c5eX3EZRMPW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hyperparameter optimization**\n",
        "\n",
        "Hyperparameters are values that define how a model is trained and directly impact the accuracy of training. For example, the rate of learning, number of hidden units or layers, activations functions, or the number of epochs.\n",
        "\n",
        "Hyperparameter optimization is a process of running your algorithm with different combinations of hyperparameters. This is done to determine which set produces the most accurate result. This process is performed with search algorithms, such as grid search, random search, or Bayesian methods.[12]"
      ],
      "metadata": {
        "id": "8WFkHnk4ROD-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model selection**\n",
        "\n",
        "Model selection is a process of narrowing down candidate models for your training. It applies models to data to determine which model produces the optimum combination of performance, maintainability, and complexity. It is impacted by what resources are available to you and determines how your ML pipeline is structured.\n",
        "\n",
        "Model selection is automated similarly to hyperparameter optimization, by running through multiple models and comparing the results. It often involves the same sort of search methods as hyperparameter optimization as well. However, it may also include the use of more extensive filters, including Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC).[12]"
      ],
      "metadata": {
        "id": "44ZNgoSjRWj2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature selection**\n",
        "\n",
        "Features are the data points used by your model to classify datasets. Feature selection involves defining how many features are going to be used, which features, and how defined those features are. The features you select directly impact the complexity of your model and its performance.\n",
        "\n",
        "Feature selection is automated through the application of algorithms during testing. For example, by using wrapper, filter, or embedded algorithms. During testing, features and combinations of features are evaluated to determine how accurate classifications or predictions are based on those features."
      ],
      "metadata": {
        "id": "5YjopMD_Rd7t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top AutoML Tools**"
      ],
      "metadata": {
        "id": "JNCfzaPVRlJA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Run:AI** is a proprietary platform for automating machine learning infrastructure. In terms of AutoML the platform offers controls for automating resource management, as well as workload orchestration for your entire machine learning infrastructure.\n",
        "\n",
        "You can use Run:AI to pool GPU compute resources, set up GPU quotas, and continuously change resource allocation. These features enable you to actually optimize your compute resources, and ensure even highly intensive deep learning models consume resources at scale.[12]"
      ],
      "metadata": {
        "id": "JUH4y5dXRtUF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. AutoKeras**\n",
        "\n",
        "AutoKeras is an open source library based on Keras that you can use for classification and regression of images, text, and structured data. It enables you to use pre-built blocks to construct a model, leaving you to focus on high-level architecture. AutoKeras supports use with Python 3.5 and up and TensorFlow 2.1.0 and up.[12]"
      ],
      "metadata": {
        "id": "K2XtOW87Xw5L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Auto-WEKA**\n",
        "\n",
        "Auto-WEKA is an open source library that you can use to optimize your hyperparameter selection. It uses Bayesian optimization to select a learning algorithm and hyperparameters from those available in the WEKA package. Auto-WEKA has the same requirements as WEKA and includes a graphical user interface (GUI) for ease of use.[12]"
      ],
      "metadata": {
        "id": "qGUfbgPQX6-5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. DataRobot Automated Machine Learning**\n",
        "\n",
        "DataRobot is a proprietary platform that you can use to automate and optimize model creation. It is designed for end-to-end support of model development, training, and deployment.\n",
        "\n",
        "DataRobot provides a range of features, including for data formatting, feature engineering, model selection, hyperparameter tuning, and monitoring. It also offers pretrained models, a data catalog, and a user friendly GUI with visualizations of the entire training and deployment process.[12]"
      ],
      "metadata": {
        "id": "n1y4nf9YYDj_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. H20 AutoML**\n",
        "\n",
        "H2O is an open source platform for ML that is distributed and runs in-memory. It supports a wide range of ML and statistical algorithms, including deep learning, generalized linear models, and gradient boosted machines. While H2O is not automated, it includes a paid add-on, called H2O AutoML.\n",
        "\n",
        "H20 AutoML enables you to train and tune models with automatic feature selection and extraction, hyperparameter optimization, and use of ensembles (multiple models for greater performance). You can use H20 AutoML from a web GUI. It integrates with Hadoop, Spark, and Kubernetes."
      ],
      "metadata": {
        "id": "2zCAvYLRYPZO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6. MLBox**\n",
        "\n",
        "MLBox is an open source Python library that you can use to automate many aspects of model training. These aspects include data preprocessing, feature selection, and hyperparameter optimization. It also includes predictive models for regression and classification, such as LightGBM, Stacking, and Deep Learning."
      ],
      "metadata": {
        "id": "4fL8WAYLYU9u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7. auto-sklearn**\n",
        "\n",
        "auto-sklearn is an open source toolkit based on scikit-learn that you can use to perform model selection, feature engineering, and hyperparameter tuning. It includes features that enable you to leverage Bayesian optimization, ensembles, and meta-learning for more accurate models and training.\n",
        "\n",
        "When using auto-sklearn, you can restrict the time and memory limits of scikit-learn, restrict your searchspace, and control preprocessing. You also have the ability to inspect training statistics and results and perform parallel computations."
      ],
      "metadata": {
        "id": "PT4NnnAaYafT"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YX1XA2lOe_hC"
      },
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Table of Content**</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRvGGuOwfGui"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        " 1. Import Library\n",
        " 2. Installing Pycaret\n",
        " 3. Import necessary packages\n",
        " 4. Dataset\n",
        " 5. Classification\n",
        " 6. Regression\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2tTizNTf7o3"
      },
      "source": [
        "Data preprocessing is an integral step in Machine Learning as the quality of data and the useful information that can be derived from it directly affects the ability of our model to learn; therefore, it is extremely important that we preprocess our data before feeding it into our model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZ0HWWxbvs86"
      },
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Import libaray**</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOHSx5sKvwti"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from prettytable import PrettyTable\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "#from pycaret.utils import enable_colab\n",
        "#enable_colab()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cvDMiUrcxdQz"
      },
      "source": [
        "# **PyCaret**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQizT2BkkvfW"
      },
      "source": [
        "PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently.PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes the experiment cycle exponentially fast and efficient [1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbPQUl3Tk-y3"
      },
      "source": [
        "PyCaret is simple and easy to use. All the operations performed in PyCaret are sequentially stored in a Pipeline that is fully automated for deployment. Whether it’s imputing missing values, one-hot-encoding, transforming categorical data, feature engineering, or even hyperparameter tuning, PyCaret automates all of it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_JOM0_SsocA"
      },
      "source": [
        "PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is known for its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end ML prototypes.\n",
        "PyCaret is an alternate low-code library that can replace hundreds of code lines with few lines only. This makes the experiment cycle exponentially fast and efficient[4]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Chq5x0vRtHVH"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1qj-WAAtNj7hDl6-o0Jg3gGS4jR1IaCGz)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQ-H819ny8GQ"
      },
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Installing Pycaret**</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4d68BKRx1Tu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8234a1f6-5d4a-468e-e509-8992f5bc560e"
      },
      "source": [
        "#capture #suppresses the displays\n",
        "# install the full version\n",
        "!pip install pycaret[full]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pycaret[full]\n",
            "  Downloading pycaret-3.3.2-py3-none-any.whl.metadata (17 kB)\n",
            "Requirement already satisfied: ipython>=5.5.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (7.34.0)\n",
            "Requirement already satisfied: ipywidgets>=7.6.5 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (7.7.1)\n",
            "Requirement already satisfied: tqdm>=4.62.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (4.67.1)\n",
            "Collecting numpy<1.27,>=1.21 (from pycaret[full])\n",
            "  Downloading numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pandas<2.2.0 (from pycaret[full])\n",
            "  Downloading pandas-2.1.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)\n",
            "Requirement already satisfied: jinja2>=3 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (3.1.6)\n",
            "Collecting scipy<=1.11.4,>=1.6.1 (from pycaret[full])\n",
            "  Downloading scipy-1.11.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.4/60.4 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting joblib<1.4,>=1.2.0 (from pycaret[full])\n",
            "  Downloading joblib-1.3.2-py3-none-any.whl.metadata (5.4 kB)\n",
            "Requirement already satisfied: scikit-learn>1.4.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (1.6.1)\n",
            "Collecting pyod>=1.1.3 (from pycaret[full])\n",
            "  Downloading pyod-2.0.3.tar.gz (169 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m169.6/169.6 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: imbalanced-learn>=0.12.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (0.13.0)\n",
            "Collecting category-encoders>=2.4.0 (from pycaret[full])\n",
            "  Downloading category_encoders-2.8.1-py3-none-any.whl.metadata (7.9 kB)\n",
            "Requirement already satisfied: lightgbm>=3.0.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (4.5.0)\n",
            "Requirement already satisfied: numba>=0.55.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (0.60.0)\n",
            "Requirement already satisfied: requests>=2.27.1 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (2.32.3)\n",
            "Requirement already satisfied: psutil>=5.9.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (5.9.5)\n",
            "Requirement already satisfied: markupsafe>=2.0.1 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (3.0.2)\n",
            "Requirement already satisfied: importlib-metadata>=4.12.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (8.6.1)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (5.10.4)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (3.1.1)\n",
            "Collecting deprecation>=2.1.0 (from pycaret[full])\n",
            "  Downloading deprecation-2.1.0-py2.py3-none-any.whl.metadata (4.6 kB)\n",
            "Collecting xxhash (from pycaret[full])\n",
            "  Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting matplotlib<3.8.0 (from pycaret[full])\n",
            "  Downloading matplotlib-3.7.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.7 kB)\n",
            "Collecting scikit-plot>=0.3.7 (from pycaret[full])\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl.metadata (7.1 kB)\n",
            "Requirement already satisfied: yellowbrick>=1.4 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (1.5)\n",
            "Requirement already satisfied: plotly>=5.14.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (5.24.1)\n",
            "Collecting kaleido>=0.2.1 (from pycaret[full])\n",
            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl.metadata (15 kB)\n",
            "Collecting schemdraw==0.15 (from pycaret[full])\n",
            "  Downloading schemdraw-0.15-py3-none-any.whl.metadata (2.2 kB)\n",
            "Collecting plotly-resampler>=0.8.3.1 (from pycaret[full])\n",
            "  Downloading plotly_resampler-0.10.0-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: statsmodels>=0.12.1 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (0.14.4)\n",
            "Collecting sktime==0.26.0 (from pycaret[full])\n",
            "  Downloading sktime-0.26.0-py3-none-any.whl.metadata (29 kB)\n",
            "Collecting tbats>=1.1.3 (from pycaret[full])\n",
            "  Downloading tbats-1.1.3-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting pmdarima>=2.0.4 (from pycaret[full])\n",
            "  Downloading pmdarima-2.0.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl.metadata (7.8 kB)\n",
            "Collecting wurlitzer (from pycaret[full])\n",
            "  Downloading wurlitzer-3.1.1-py3-none-any.whl.metadata (2.5 kB)\n",
            "Collecting shap~=0.44.0 (from pycaret[full])\n",
            "  Downloading shap-0.44.1-cp311-cp311-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (24 kB)\n",
            "Collecting interpret>=0.2.7 (from pycaret[full])\n",
            "  Downloading interpret-0.6.9-py3-none-any.whl.metadata (1.0 kB)\n",
            "Requirement already satisfied: umap-learn>=0.5.2 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (0.5.7)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (6.0.2)\n",
            "Collecting ydata-profiling>=4.3.1 (from pycaret[full])\n",
            "  Downloading ydata_profiling-4.15.0-py2.py3-none-any.whl.metadata (22 kB)\n",
            "Collecting explainerdashboard>=0.3.8 (from pycaret[full])\n",
            "  Downloading explainerdashboard-0.4.8-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting fairlearn==0.7.0 (from pycaret[full])\n",
            "  Downloading fairlearn-0.7.0-py3-none-any.whl.metadata (7.3 kB)\n",
            "Collecting kmodes>=0.11.1 (from pycaret[full])\n",
            "  Downloading kmodes-0.12.2-py2.py3-none-any.whl.metadata (8.1 kB)\n",
            "Requirement already satisfied: mlxtend>=0.19.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (0.23.4)\n",
            "Collecting statsforecast<1.6.0,>=0.5.5 (from pycaret[full])\n",
            "  Downloading statsforecast-1.5.0-py3-none-any.whl.metadata (23 kB)\n",
            "Requirement already satisfied: hyperopt>=0.2.7 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (0.2.7)\n",
            "Collecting optuna>=3.0.0 (from pycaret[full])\n",
            "  Downloading optuna-4.2.1-py3-none-any.whl.metadata (17 kB)\n",
            "Collecting optuna-integration (from pycaret[full])\n",
            "  Downloading optuna_integration-4.2.1-py3-none-any.whl.metadata (12 kB)\n",
            "Collecting scikit-optimize>=0.9.0 (from pycaret[full])\n",
            "  Downloading scikit_optimize-0.10.2-py2.py3-none-any.whl.metadata (9.7 kB)\n",
            "Collecting mlflow>=2.0.0 (from pycaret[full])\n",
            "  Downloading mlflow-2.21.0-py3-none-any.whl.metadata (30 kB)\n",
            "Collecting gradio>=3.50.2 (from pycaret[full])\n",
            "  Downloading gradio-5.22.0-py3-none-any.whl.metadata (16 kB)\n",
            "Collecting boto3>=1.24.56 (from pycaret[full])\n",
            "  Downloading boto3-1.37.16-py3-none-any.whl.metadata (6.7 kB)\n",
            "Collecting fastapi (from pycaret[full])\n",
            "  Downloading fastapi-0.115.11-py3-none-any.whl.metadata (27 kB)\n",
            "Collecting uvicorn>=0.17.6 (from pycaret[full])\n",
            "  Downloading uvicorn-0.34.0-py3-none-any.whl.metadata (6.5 kB)\n",
            "Collecting m2cgen>=0.9.0 (from pycaret[full])\n",
            "  Downloading m2cgen-0.10.0-py3-none-any.whl.metadata (9.9 kB)\n",
            "Collecting evidently~=0.4.16 (from pycaret[full])\n",
            "  Downloading evidently-0.4.40-py3-none-any.whl.metadata (11 kB)\n",
            "Requirement already satisfied: dask>=2024.4.1 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (2024.12.1)\n",
            "Requirement already satisfied: distributed>=2024.4.1 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (2024.12.1)\n",
            "Collecting fugue~=0.8.0 (from pycaret[full])\n",
            "  Downloading fugue-0.8.7-py3-none-any.whl.metadata (17 kB)\n",
            "Requirement already satisfied: flask in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (3.1.0)\n",
            "Collecting Werkzeug<3.0,>=2.2 (from pycaret[full])\n",
            "  Downloading werkzeug-2.3.8-py3-none-any.whl.metadata (4.1 kB)\n",
            "Collecting pytest<8.0.0 (from pycaret[full])\n",
            "  Downloading pytest-7.4.4-py3-none-any.whl.metadata (7.9 kB)\n",
            "Collecting moto<5.0.0 (from pycaret[full])\n",
            "  Downloading moto-4.2.14-py2.py3-none-any.whl.metadata (12 kB)\n",
            "Collecting dash[testing] (from pycaret[full])\n",
            "  Downloading dash-3.0.0-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting scikit-learn-intelex>=2023.0.1 (from pycaret[full])\n",
            "  Downloading scikit_learn_intelex-2025.2.0-py311-none-manylinux_2_28_x86_64.whl.metadata (12 kB)\n",
            "Collecting catboost>=0.23.2 (from pycaret[full])\n",
            "  Downloading catboost-1.2.7-cp311-cp311-manylinux2014_x86_64.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: xgboost>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from pycaret[full]) (2.1.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from sktime==0.26.0->pycaret[full]) (24.2)\n",
            "Collecting scikit-base<0.8.0 (from sktime==0.26.0->pycaret[full])\n",
            "  Downloading scikit_base-0.7.8-py3-none-any.whl.metadata (8.8 kB)\n",
            "Collecting scikit-learn>1.4.0 (from pycaret[full])\n",
            "  Downloading scikit_learn-1.4.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
            "Collecting botocore<1.38.0,>=1.37.16 (from boto3>=1.24.56->pycaret[full])\n",
            "  Downloading botocore-1.37.16-py3-none-any.whl.metadata (5.7 kB)\n",
            "Collecting jmespath<2.0.0,>=0.7.1 (from boto3>=1.24.56->pycaret[full])\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl.metadata (7.6 kB)\n",
            "Collecting s3transfer<0.12.0,>=0.11.0 (from boto3>=1.24.56->pycaret[full])\n",
            "  Downloading s3transfer-0.11.4-py3-none-any.whl.metadata (1.7 kB)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from catboost>=0.23.2->pycaret[full]) (0.20.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from catboost>=0.23.2->pycaret[full]) (1.17.0)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.11/dist-packages (from category-encoders>=2.4.0->pycaret[full]) (1.0.1)\n",
            "INFO: pip is looking at multiple versions of category-encoders to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting category-encoders>=2.4.0 (from pycaret[full])\n",
            "  Downloading category_encoders-2.8.0-py3-none-any.whl.metadata (7.9 kB)\n",
            "  Downloading category_encoders-2.7.0-py3-none-any.whl.metadata (7.9 kB)\n",
            "Requirement already satisfied: click>=8.1 in /usr/local/lib/python3.11/dist-packages (from dask>=2024.4.1->pycaret[full]) (8.1.8)\n",
            "Requirement already satisfied: fsspec>=2021.09.0 in /usr/local/lib/python3.11/dist-packages (from dask>=2024.4.1->pycaret[full]) (2024.10.0)\n",
            "Requirement already satisfied: partd>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from dask>=2024.4.1->pycaret[full]) (1.4.2)\n",
            "Requirement already satisfied: toolz>=0.10.0 in /usr/local/lib/python3.11/dist-packages (from dask>=2024.4.1->pycaret[full]) (0.12.1)\n",
            "Requirement already satisfied: locket>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from distributed>=2024.4.1->pycaret[full]) (1.0.0)\n",
            "Requirement already satisfied: msgpack>=1.0.2 in /usr/local/lib/python3.11/dist-packages (from distributed>=2024.4.1->pycaret[full]) (1.1.0)\n",
            "Requirement already satisfied: sortedcontainers>=2.0.5 in /usr/local/lib/python3.11/dist-packages (from distributed>=2024.4.1->pycaret[full]) (2.4.0)\n",
            "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from distributed>=2024.4.1->pycaret[full]) (3.0.0)\n",
            "Requirement already satisfied: tornado>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from distributed>=2024.4.1->pycaret[full]) (6.4.2)\n",
            "Requirement already satisfied: urllib3>=1.26.5 in /usr/local/lib/python3.11/dist-packages (from distributed>=2024.4.1->pycaret[full]) (2.3.0)\n",
            "Requirement already satisfied: zict>=3.0.0 in /usr/local/lib/python3.11/dist-packages (from distributed>=2024.4.1->pycaret[full]) (3.0.0)\n",
            "Requirement already satisfied: nltk>=3.6.7 in /usr/local/lib/python3.11/dist-packages (from evidently~=0.4.16->pycaret[full]) (3.9.1)\n",
            "Requirement already satisfied: pydantic>=1.10.13 in /usr/local/lib/python3.11/dist-packages (from evidently~=0.4.16->pycaret[full]) (2.10.6)\n",
            "Collecting litestar>=2.8.3 (from evidently~=0.4.16->pycaret[full])\n",
            "  Downloading litestar-2.15.1-py3-none-any.whl.metadata (109 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109.9/109.9 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting typing-inspect>=0.9.0 (from evidently~=0.4.16->pycaret[full])\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl.metadata (1.5 kB)\n",
            "Collecting watchdog>=3.0.0 (from evidently~=0.4.16->pycaret[full])\n",
            "  Downloading watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl.metadata (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.3/44.3 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typer>=0.3 in /usr/local/lib/python3.11/dist-packages (from evidently~=0.4.16->pycaret[full]) (0.15.2)\n",
            "Requirement already satisfied: rich>=13 in /usr/local/lib/python3.11/dist-packages (from evidently~=0.4.16->pycaret[full]) (13.9.4)\n",
            "Collecting iterative-telemetry>=0.0.5 (from evidently~=0.4.16->pycaret[full])\n",
            "  Downloading iterative_telemetry-0.0.10-py3-none-any.whl.metadata (4.1 kB)\n",
            "Collecting dynaconf>=3.2.4 (from evidently~=0.4.16->pycaret[full])\n",
            "  Downloading dynaconf-3.2.10-py2.py3-none-any.whl.metadata (9.1 kB)\n",
            "Requirement already satisfied: certifi>=2024.7.4 in /usr/local/lib/python3.11/dist-packages (from evidently~=0.4.16->pycaret[full]) (2025.1.31)\n",
            "Collecting ujson>=5.4.0 (from evidently~=0.4.16->pycaret[full])\n",
            "  Downloading ujson-5.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.3 kB)\n",
            "Collecting uuid6>=2024.7.10 (from evidently~=0.4.16->pycaret[full])\n",
            "  Downloading uuid6-2024.7.10-py3-none-any.whl.metadata (8.6 kB)\n",
            "Requirement already satisfied: cryptography>=43.0.1 in /usr/local/lib/python3.11/dist-packages (from evidently~=0.4.16->pycaret[full]) (43.0.3)\n",
            "Collecting dash-auth (from explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading dash_auth-2.3.0-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting dash-bootstrap-components>=1 (from explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading dash_bootstrap_components-2.0.0-py3-none-any.whl.metadata (18 kB)\n",
            "Collecting dtreeviz>=2.1 (from explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading dtreeviz-2.2.2-py3-none-any.whl.metadata (2.4 kB)\n",
            "Collecting flask_simplelogin (from explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading flask_simplelogin-0.2.0-py3-none-any.whl.metadata (3.3 kB)\n",
            "Collecting Flask-WTF>=1.1 (from explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading flask_wtf-1.2.2-py3-none-any.whl.metadata (3.4 kB)\n",
            "Collecting jupyter_dash>=0.4.1 (from explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading jupyter_dash-0.4.2-py3-none-any.whl.metadata (3.6 kB)\n",
            "Collecting oyaml (from explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading oyaml-1.0-py2.py3-none-any.whl.metadata (1.2 kB)\n",
            "Collecting waitress (from explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading waitress-3.0.2-py3-none-any.whl.metadata (5.8 kB)\n",
            "Collecting triad>=0.9.3 (from fugue~=0.8.0->pycaret[full])\n",
            "  Downloading triad-0.9.8-py3-none-any.whl.metadata (6.3 kB)\n",
            "Collecting adagio>=0.2.4 (from fugue~=0.8.0->pycaret[full])\n",
            "  Downloading adagio-0.2.6-py3-none-any.whl.metadata (1.8 kB)\n",
            "Collecting qpd>=0.4.4 (from fugue~=0.8.0->pycaret[full])\n",
            "  Downloading qpd-0.4.4-py3-none-any.whl.metadata (6.4 kB)\n",
            "Collecting fugue-sql-antlr>=0.1.6 (from fugue~=0.8.0->pycaret[full])\n",
            "  Downloading fugue-sql-antlr-0.2.2.tar.gz (154 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.7/154.7 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: sqlglot in /usr/local/lib/python3.11/dist-packages (from fugue~=0.8.0->pycaret[full]) (25.6.1)\n",
            "Collecting aiofiles<24.0,>=22.0 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading aiofiles-23.2.1-py3-none-any.whl.metadata (9.7 kB)\n",
            "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.11/dist-packages (from gradio>=3.50.2->pycaret[full]) (3.7.1)\n",
            "Collecting ffmpy (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading ffmpy-0.5.0-py3-none-any.whl.metadata (3.0 kB)\n",
            "Collecting gradio-client==1.8.0 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading gradio_client-1.8.0-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting groovy~=0.1 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading groovy-0.1.2-py3-none-any.whl.metadata (6.1 kB)\n",
            "Requirement already satisfied: httpx>=0.24.1 in /usr/local/lib/python3.11/dist-packages (from gradio>=3.50.2->pycaret[full]) (0.28.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.28.1 in /usr/local/lib/python3.11/dist-packages (from gradio>=3.50.2->pycaret[full]) (0.28.1)\n",
            "Requirement already satisfied: orjson~=3.0 in /usr/local/lib/python3.11/dist-packages (from gradio>=3.50.2->pycaret[full]) (3.10.15)\n",
            "Requirement already satisfied: pillow<12.0,>=8.0 in /usr/local/lib/python3.11/dist-packages (from gradio>=3.50.2->pycaret[full]) (11.1.0)\n",
            "Collecting pydub (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
            "Collecting python-multipart>=0.0.18 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading python_multipart-0.0.20-py3-none-any.whl.metadata (1.8 kB)\n",
            "Collecting ruff>=0.9.3 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading ruff-0.11.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (25 kB)\n",
            "Collecting safehttpx<0.2.0,>=0.1.6 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading safehttpx-0.1.6-py3-none-any.whl.metadata (4.2 kB)\n",
            "Collecting semantic-version~=2.0 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl.metadata (9.7 kB)\n",
            "Collecting starlette<1.0,>=0.40.0 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading starlette-0.46.1-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting tomlkit<0.14.0,>=0.12.0 (from gradio>=3.50.2->pycaret[full])\n",
            "  Downloading tomlkit-0.13.2-py3-none-any.whl.metadata (2.7 kB)\n",
            "Requirement already satisfied: typing-extensions~=4.0 in /usr/local/lib/python3.11/dist-packages (from gradio>=3.50.2->pycaret[full]) (4.12.2)\n",
            "Requirement already satisfied: websockets<16.0,>=10.0 in /usr/local/lib/python3.11/dist-packages (from gradio-client==1.8.0->gradio>=3.50.2->pycaret[full]) (14.2)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.11/dist-packages (from hyperopt>=0.2.7->pycaret[full]) (3.4.2)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.11/dist-packages (from hyperopt>=0.2.7->pycaret[full]) (1.0.0)\n",
            "Requirement already satisfied: py4j in /usr/local/lib/python3.11/dist-packages (from hyperopt>=0.2.7->pycaret[full]) (0.10.9.7)\n",
            "Requirement already satisfied: sklearn-compat<1,>=0.1 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn>=0.12.0->pycaret[full]) (0.1.3)\n",
            "Requirement already satisfied: threadpoolctl<4,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn>=0.12.0->pycaret[full]) (3.6.0)\n",
            "Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.11/dist-packages (from importlib-metadata>=4.12.0->pycaret[full]) (3.21.0)\n",
            "Collecting interpret-core==0.6.9 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading interpret_core-0.6.9-py3-none-any.whl.metadata (2.8 kB)\n",
            "Requirement already satisfied: ipykernel>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full]) (6.17.1)\n",
            "Collecting SALib>=1.3.3 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading salib-1.5.1-py3-none-any.whl.metadata (11 kB)\n",
            "Collecting dill>=0.2.5 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading dill-0.3.9-py3-none-any.whl.metadata (10 kB)\n",
            "Collecting aplr>=10.6.1 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading aplr-10.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB)\n",
            "Collecting dash-core-components>=1.0.0 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading dash_core_components-2.0.0-py3-none-any.whl.metadata (2.9 kB)\n",
            "Collecting dash-html-components>=1.0.0 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading dash_html_components-2.0.0-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting dash-table>=4.1.0 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading dash_table-5.0.0-py3-none-any.whl.metadata (2.4 kB)\n",
            "Collecting dash-cytoscape>=0.1.1 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading dash_cytoscape-1.0.2.tar.gz (4.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m48.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting gevent>=1.3.6 (from interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading gevent-24.11.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (75.1.0)\n",
            "Collecting jedi>=0.16 (from ipython>=5.5.0->pycaret[full])\n",
            "  Downloading jedi-0.19.2-py2.py3-none-any.whl.metadata (22 kB)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (5.7.1)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (3.0.50)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (2.18.0)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (0.1.7)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret[full]) (4.9.0)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.11/dist-packages (from ipywidgets>=7.6.5->pycaret[full]) (0.2.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.11/dist-packages (from ipywidgets>=7.6.5->pycaret[full]) (3.6.10)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from ipywidgets>=7.6.5->pycaret[full]) (3.0.13)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret[full]) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret[full]) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret[full]) (4.56.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret[full]) (1.4.8)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret[full]) (3.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret[full]) (2.8.2)\n",
            "Collecting mlflow-skinny==2.21.0 (from mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading mlflow_skinny-2.21.0-py3-none-any.whl.metadata (31 kB)\n",
            "Collecting alembic!=1.10.0,<2 (from mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading alembic-1.15.1-py3-none-any.whl.metadata (7.2 kB)\n",
            "Collecting docker<8,>=4.0.0 (from mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading docker-7.1.0-py3-none-any.whl.metadata (3.8 kB)\n",
            "Collecting graphene<4 (from mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading graphene-3.4.3-py2.py3-none-any.whl.metadata (6.9 kB)\n",
            "Collecting gunicorn<24 (from mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading gunicorn-23.0.0-py3-none-any.whl.metadata (4.4 kB)\n",
            "Requirement already satisfied: markdown<4,>=3.3 in /usr/local/lib/python3.11/dist-packages (from mlflow>=2.0.0->pycaret[full]) (3.7)\n",
            "Requirement already satisfied: pyarrow<20,>=4.0.0 in /usr/local/lib/python3.11/dist-packages (from mlflow>=2.0.0->pycaret[full]) (18.1.0)\n",
            "Requirement already satisfied: sqlalchemy<3,>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from mlflow>=2.0.0->pycaret[full]) (2.0.39)\n",
            "Requirement already satisfied: cachetools<6,>=5.0.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (5.5.2)\n",
            "Collecting databricks-sdk<1,>=0.20.0 (from mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading databricks_sdk-0.46.0-py3-none-any.whl.metadata (38 kB)\n",
            "Requirement already satisfied: gitpython<4,>=3.1.9 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (3.1.44)\n",
            "Requirement already satisfied: opentelemetry-api<3,>=1.9.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (1.31.0)\n",
            "Requirement already satisfied: opentelemetry-sdk<3,>=1.9.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (1.31.0)\n",
            "Requirement already satisfied: protobuf<6,>=3.12.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (4.25.6)\n",
            "Requirement already satisfied: sqlparse<1,>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (0.5.3)\n",
            "INFO: pip is looking at multiple versions of flask to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting flask (from pycaret[full])\n",
            "  Downloading flask-3.0.3-py3-none-any.whl.metadata (3.2 kB)\n",
            "  Downloading flask-3.0.2-py3-none-any.whl.metadata (3.6 kB)\n",
            "  Downloading flask-3.0.1-py3-none-any.whl.metadata (3.6 kB)\n",
            "  Downloading flask-3.0.0-py3-none-any.whl.metadata (3.6 kB)\n",
            "  Downloading flask-2.3.3-py3-none-any.whl.metadata (3.6 kB)\n",
            "Requirement already satisfied: itsdangerous>=2.1.2 in /usr/local/lib/python3.11/dist-packages (from flask->pycaret[full]) (2.2.0)\n",
            "Requirement already satisfied: blinker>=1.6.2 in /usr/local/lib/python3.11/dist-packages (from flask->pycaret[full]) (1.9.0)\n",
            "Collecting xmltodict (from moto<5.0.0->pycaret[full])\n",
            "  Downloading xmltodict-0.14.2-py2.py3-none-any.whl.metadata (8.0 kB)\n",
            "Collecting responses>=0.13.0 (from moto<5.0.0->pycaret[full])\n",
            "  Downloading responses-0.25.7-py3-none-any.whl.metadata (47 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.2/47.2 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fastjsonschema>=2.15 in /usr/local/lib/python3.11/dist-packages (from nbformat>=4.2.0->pycaret[full]) (2.21.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.11/dist-packages (from nbformat>=4.2.0->pycaret[full]) (4.23.0)\n",
            "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /usr/local/lib/python3.11/dist-packages (from nbformat>=4.2.0->pycaret[full]) (5.7.2)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.11/dist-packages (from numba>=0.55.0->pycaret[full]) (0.43.0)\n",
            "Collecting colorlog (from optuna>=3.0.0->pycaret[full])\n",
            "  Downloading colorlog-6.9.0-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas<2.2.0->pycaret[full]) (2025.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.11/dist-packages (from pandas<2.2.0->pycaret[full]) (2025.1)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly>=5.14.0->pycaret[full]) (9.0.0)\n",
            "Collecting tsdownsample>=0.1.3 (from plotly-resampler>=0.8.3.1->pycaret[full])\n",
            "  Downloading tsdownsample-0.1.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (8.0 kB)\n",
            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.11/dist-packages (from pmdarima>=2.0.4->pycaret[full]) (3.0.12)\n",
            "Requirement already satisfied: iniconfig in /usr/local/lib/python3.11/dist-packages (from pytest<8.0.0->pycaret[full]) (2.0.0)\n",
            "Requirement already satisfied: pluggy<2.0,>=0.12 in /usr/local/lib/python3.11/dist-packages (from pytest<8.0.0->pycaret[full]) (1.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.27.1->pycaret[full]) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.27.1->pycaret[full]) (3.10)\n",
            "Collecting daal==2025.2.0 (from scikit-learn-intelex>=2023.0.1->pycaret[full])\n",
            "  Downloading daal-2025.2.0-py2.py3-none-manylinux_2_28_x86_64.whl.metadata (1.1 kB)\n",
            "Requirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from daal==2025.2.0->scikit-learn-intelex>=2023.0.1->pycaret[full]) (2022.0.0)\n",
            "Requirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->daal==2025.2.0->scikit-learn-intelex>=2023.0.1->pycaret[full]) (1.2.0)\n",
            "Collecting pyaml>=16.9 (from scikit-optimize>=0.9.0->pycaret[full])\n",
            "  Downloading pyaml-25.1.0-py3-none-any.whl.metadata (12 kB)\n",
            "Collecting slicer==0.0.7 (from shap~=0.44.0->pycaret[full])\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl.metadata (3.7 kB)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.11/dist-packages (from umap-learn>=0.5.2->pycaret[full]) (0.5.13)\n",
            "Requirement already satisfied: h11>=0.8 in /usr/local/lib/python3.11/dist-packages (from uvicorn>=0.17.6->pycaret[full]) (0.14.0)\n",
            "Requirement already satisfied: nvidia-nccl-cu12 in /usr/local/lib/python3.11/dist-packages (from xgboost>=1.1.0->pycaret[full]) (2.21.5)\n",
            "Collecting visions<0.8.2,>=0.7.5 (from visions[type_image_path]<0.8.2,>=0.7.5->ydata-profiling>=4.3.1->pycaret[full])\n",
            "  Downloading visions-0.8.1-py3-none-any.whl.metadata (11 kB)\n",
            "Collecting htmlmin==0.1.12 (from ydata-profiling>=4.3.1->pycaret[full])\n",
            "  Downloading htmlmin-0.1.12.tar.gz (19 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting phik<0.13,>=0.11.1 (from ydata-profiling>=4.3.1->pycaret[full])\n",
            "  Downloading phik-0.12.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.6 kB)\n",
            "Requirement already satisfied: seaborn<0.14,>=0.10.1 in /usr/local/lib/python3.11/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (0.13.2)\n",
            "Collecting multimethod<2,>=1.4 (from ydata-profiling>=4.3.1->pycaret[full])\n",
            "  Downloading multimethod-1.12-py3-none-any.whl.metadata (9.6 kB)\n",
            "Requirement already satisfied: typeguard<5,>=3 in /usr/local/lib/python3.11/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (4.4.2)\n",
            "Collecting imagehash==4.3.1 (from ydata-profiling>=4.3.1->pycaret[full])\n",
            "  Downloading ImageHash-4.3.1-py2.py3-none-any.whl.metadata (8.0 kB)\n",
            "Requirement already satisfied: wordcloud>=1.9.3 in /usr/local/lib/python3.11/dist-packages (from ydata-profiling>=4.3.1->pycaret[full]) (1.9.4)\n",
            "Collecting dacite>=1.8 (from ydata-profiling>=4.3.1->pycaret[full])\n",
            "  Downloading dacite-1.9.2-py3-none-any.whl.metadata (17 kB)\n",
            "Collecting PyWavelets (from imagehash==4.3.1->ydata-profiling>=4.3.1->pycaret[full])\n",
            "  Downloading pywavelets-1.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (9.0 kB)\n",
            "Collecting retrying (from dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading retrying-1.3.4-py3-none-any.whl.metadata (6.9 kB)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.11/dist-packages (from dash[testing]; extra == \"full\"->pycaret[full]) (1.6.0)\n",
            "Collecting stringcase>=1.2.0 (from dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading stringcase-1.2.0.tar.gz (3.0 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: beautifulsoup4>=4.8.2 in /usr/local/lib/python3.11/dist-packages (from dash[testing]; extra == \"full\"->pycaret[full]) (4.13.3)\n",
            "Requirement already satisfied: lxml>=4.6.2 in /usr/local/lib/python3.11/dist-packages (from dash[testing]; extra == \"full\"->pycaret[full]) (5.3.1)\n",
            "Collecting percy>=2.0.2 (from dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading percy-2.0.2-py2.py3-none-any.whl.metadata (821 bytes)\n",
            "Collecting selenium<=4.2.0,>=3.141.0 (from dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading selenium-4.2.0-py3-none-any.whl.metadata (6.5 kB)\n",
            "Collecting multiprocess>=0.70.12 (from dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading multiprocess-0.70.17-py311-none-any.whl.metadata (7.2 kB)\n",
            "Collecting dash-testing-stub>=0.0.2 (from dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading dash_testing_stub-0.0.2-py3-none-any.whl.metadata (587 bytes)\n",
            "Collecting Mako (from alembic!=1.10.0,<2->mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading Mako-1.3.9-py3-none-any.whl.metadata (2.9 kB)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio<5.0,>=3.0->gradio>=3.50.2->pycaret[full]) (1.3.1)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4>=4.8.2->dash[testing]; extra == \"full\"->pycaret[full]) (2.6)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.11/dist-packages (from cryptography>=43.0.1->evidently~=0.4.16->pycaret[full]) (1.17.1)\n",
            "Requirement already satisfied: dask-expr<1.2,>=1.1 in /usr/local/lib/python3.11/dist-packages (from dask[dataframe,distributed]>=2023.5.0; extra == \"dask\"->fugue[dask]; extra == \"full\"->pycaret[full]) (1.1.21)\n",
            "Requirement already satisfied: colour in /usr/local/lib/python3.11/dist-packages (from dtreeviz>=2.1->explainerdashboard>=0.3.8->pycaret[full]) (0.1.5)\n",
            "Collecting wtforms (from Flask-WTF>=1.1->explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading wtforms-3.2.1-py3-none-any.whl.metadata (5.3 kB)\n",
            "Collecting antlr4-python3-runtime<4.12 (from fugue-sql-antlr>=0.1.6->fugue~=0.8.0->pycaret[full])\n",
            "  Downloading antlr4_python3_runtime-4.11.1-py3-none-any.whl.metadata (291 bytes)\n",
            "Collecting graphql-core<3.3,>=3.1 (from graphene<4->mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading graphql_core-3.2.6-py3-none-any.whl.metadata (11 kB)\n",
            "Collecting graphql-relay<3.3,>=3.1 (from graphene<4->mlflow>=2.0.0->pycaret[full])\n",
            "  Downloading graphql_relay-3.2.0-py3-none-any.whl.metadata (12 kB)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.11/dist-packages (from httpx>=0.24.1->gradio>=3.50.2->pycaret[full]) (1.0.7)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.28.1->gradio>=3.50.2->pycaret[full]) (3.17.0)\n",
            "Requirement already satisfied: debugpy>=1.0 in /usr/local/lib/python3.11/dist-packages (from ipykernel>=4.10.0->interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full]) (1.8.0)\n",
            "Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.11/dist-packages (from ipykernel>=4.10.0->interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full]) (6.1.12)\n",
            "Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.11/dist-packages (from ipykernel>=4.10.0->interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full]) (24.0.1)\n",
            "Collecting appdirs (from iterative-telemetry>=0.0.5->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading appdirs-1.4.4-py2.py3-none-any.whl.metadata (9.0 kB)\n",
            "Requirement already satisfied: distro in /usr/local/lib/python3.11/dist-packages (from iterative-telemetry>=0.0.5->evidently~=0.4.16->pycaret[full]) (1.9.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /usr/local/lib/python3.11/dist-packages (from jedi>=0.16->ipython>=5.5.0->pycaret[full]) (0.8.4)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret[full]) (25.3.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret[full]) (2024.10.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret[full]) (0.36.2)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret[full]) (0.23.1)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.11/dist-packages (from jupyter-core!=5.0.*,>=4.12->nbformat>=4.2.0->pycaret[full]) (4.3.6)\n",
            "Collecting ansi2html (from jupyter_dash>=0.4.1->explainerdashboard>=0.3.8->pycaret[full])\n",
            "  Downloading ansi2html-1.9.2-py3-none-any.whl.metadata (3.7 kB)\n",
            "Collecting litestar-htmx>=0.4.0 (from litestar>=2.8.3->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading litestar_htmx-0.4.1-py3-none-any.whl.metadata (2.0 kB)\n",
            "Collecting msgspec>=0.18.2 (from litestar>=2.8.3->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading msgspec-0.19.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.9 kB)\n",
            "Requirement already satisfied: multidict>=6.0.2 in /usr/local/lib/python3.11/dist-packages (from litestar>=2.8.3->evidently~=0.4.16->pycaret[full]) (6.1.0)\n",
            "Collecting multipart>=1.2.0 (from litestar>=2.8.3->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading multipart-1.2.1-py3-none-any.whl.metadata (4.4 kB)\n",
            "Collecting polyfactory>=2.6.3 (from litestar>=2.8.3->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading polyfactory-2.19.0-py3-none-any.whl.metadata (27 kB)\n",
            "Collecting rich-click (from litestar>=2.8.3->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading rich_click-1.8.8-py3-none-any.whl.metadata (7.9 kB)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.11/dist-packages (from nltk>=3.6.7->evidently~=0.4.16->pycaret[full]) (2024.11.6)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.11/dist-packages (from pexpect>4.3->ipython>=5.5.0->pycaret[full]) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.11/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.5.0->pycaret[full]) (0.2.13)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic>=1.10.13->evidently~=0.4.16->pycaret[full]) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic>=1.10.13->evidently~=0.4.16->pycaret[full]) (2.27.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich>=13->evidently~=0.4.16->pycaret[full]) (3.0.0)\n",
            "Collecting urllib3>=1.26.5 (from distributed>=2024.4.1->pycaret[full])\n",
            "  Downloading urllib3-1.26.20-py2.py3-none-any.whl.metadata (50 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.1/50.1 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting trio~=0.17 (from selenium<=4.2.0,>=3.141.0->dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading trio-0.29.0-py3-none-any.whl.metadata (8.5 kB)\n",
            "Collecting trio-websocket~=0.9 (from selenium<=4.2.0,>=3.141.0->dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading trio_websocket-0.12.2-py3-none-any.whl.metadata (5.1 kB)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.11/dist-packages (from sqlalchemy<3,>=1.4.0->mlflow>=2.0.0->pycaret[full]) (3.1.1)\n",
            "Collecting fs (from triad>=0.9.3->fugue~=0.8.0->pycaret[full])\n",
            "  Downloading fs-2.4.16-py2.py3-none-any.whl.metadata (6.3 kB)\n",
            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.11/dist-packages (from typer>=0.3->evidently~=0.4.16->pycaret[full]) (1.5.4)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect>=0.9.0->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl.metadata (1.1 kB)\n",
            "Collecting httptools>=0.6.3 (from uvicorn[standard]>=0.22.0->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading httptools-0.6.4-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.6 kB)\n",
            "Collecting python-dotenv>=0.13 (from uvicorn[standard]>=0.22.0->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n",
            "Collecting uvloop!=0.15.0,!=0.15.1,>=0.14.0 (from uvicorn[standard]>=0.22.0->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading uvloop-0.21.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n",
            "Collecting watchfiles>=0.13 (from uvicorn[standard]>=0.22.0->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading watchfiles-1.0.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n",
            "Collecting puremagic (from visions<0.8.2,>=0.7.5->visions[type_image_path]<0.8.2,>=0.7.5->ydata-profiling>=4.3.1->pycaret[full])\n",
            "  Downloading puremagic-1.28-py3-none-any.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.11/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (6.5.5)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.11/dist-packages (from cffi>=1.12->cryptography>=43.0.1->evidently~=0.4.16->pycaret[full]) (2.22)\n",
            "Requirement already satisfied: google-auth~=2.0 in /usr/local/lib/python3.11/dist-packages (from databricks-sdk<1,>=0.20.0->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (2.38.0)\n",
            "Collecting zope.event (from gevent>=1.3.6->interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading zope.event-5.0-py3-none-any.whl.metadata (4.4 kB)\n",
            "Collecting zope.interface (from gevent>=1.3.6->interpret-core[aplr,dash,debug,linear,notebook,plotly,sensitivity,shap]==0.6.9->interpret>=0.2.7->pycaret[full])\n",
            "  Downloading zope.interface-7.2-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.11/dist-packages (from gitpython<4,>=3.1.9->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (4.0.12)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich>=13->evidently~=0.4.16->pycaret[full]) (0.1.2)\n",
            "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (23.1.0)\n",
            "Requirement already satisfied: nbconvert>=5 in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (7.16.6)\n",
            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (1.8.3)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (0.18.1)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (0.21.1)\n",
            "Requirement already satisfied: nbclassic>=0.4.7 in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (1.2.0)\n",
            "Requirement already satisfied: deprecated>=1.2.6 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-api<3,>=1.9.0->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (1.2.18)\n",
            "Requirement already satisfied: opentelemetry-semantic-conventions==0.52b0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-sdk<3,>=1.9.0->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (0.52b0)\n",
            "Collecting faker (from polyfactory>=2.6.3->litestar>=2.8.3->evidently~=0.4.16->pycaret[full])\n",
            "  Downloading faker-37.0.2-py3-none-any.whl.metadata (15 kB)\n",
            "Collecting outcome (from trio~=0.17->selenium<=4.2.0,>=3.141.0->dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading outcome-1.3.0.post0-py2.py3-none-any.whl.metadata (2.6 kB)\n",
            "Collecting wsproto>=0.14 (from trio-websocket~=0.9->selenium<=4.2.0,>=3.141.0->dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading wsproto-1.2.0-py3-none-any.whl.metadata (5.6 kB)\n",
            "Requirement already satisfied: pyOpenSSL>=0.14 in /usr/local/lib/python3.11/dist-packages (from urllib3[secure,socks]~=1.26->selenium<=4.2.0,>=3.141.0->dash[testing]; extra == \"full\"->pycaret[full]) (24.2.1)\n",
            "Collecting urllib3-secure-extra (from urllib3[secure,socks]~=1.26->selenium<=4.2.0,>=3.141.0->dash[testing]; extra == \"full\"->pycaret[full])\n",
            "  Downloading urllib3_secure_extra-0.1.0-py2.py3-none-any.whl.metadata (828 bytes)\n",
            "Requirement already satisfied: PySocks!=1.5.7,<2.0,>=1.5.6 in /usr/local/lib/python3.11/dist-packages (from urllib3[secure,socks]~=1.26->selenium<=4.2.0,>=3.141.0->dash[testing]; extra == \"full\"->pycaret[full]) (1.7.1)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.11/dist-packages (from deprecated>=1.2.6->opentelemetry-api<3,>=1.9.0->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (1.17.2)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.11/dist-packages (from gitdb<5,>=4.0.1->gitpython<4,>=3.1.9->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (5.0.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth~=2.0->databricks-sdk<1,>=0.20.0->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (0.4.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth~=2.0->databricks-sdk<1,>=0.20.0->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (4.9)\n",
            "Requirement already satisfied: notebook-shim>=0.2.3 in /usr/local/lib/python3.11/dist-packages (from nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (0.2.4)\n",
            "Requirement already satisfied: bleach!=5.0.0 in /usr/local/lib/python3.11/dist-packages (from bleach[css]!=5.0.0->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (6.2.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (0.7.1)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (0.3.0)\n",
            "Requirement already satisfied: mistune<4,>=2.0.3 in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (3.1.2)\n",
            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (0.10.2)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (1.5.1)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.11/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (21.2.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.11/dist-packages (from bleach!=5.0.0->bleach[css]!=5.0.0->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (0.5.1)\n",
            "Requirement already satisfied: tinycss2<1.5,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from bleach[css]!=5.0.0->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (1.4.0)\n",
            "Requirement already satisfied: jupyter-server<3,>=1.8 in /usr/local/lib/python3.11/dist-packages (from notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (1.24.0)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth~=2.0->databricks-sdk<1,>=0.20.0->mlflow-skinny==2.21.0->mlflow>=2.0.0->pycaret[full]) (0.6.1)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.11/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret[full]) (1.8.0)\n",
            "Downloading fairlearn-0.7.0-py3-none-any.whl (177 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.5/177.5 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading schemdraw-0.15-py3-none-any.whl (106 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.8/106.8 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading sktime-0.26.0-py3-none-any.whl (21.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.8/21.8 MB\u001b[0m \u001b[31m65.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading boto3-1.37.16-py3-none-any.whl (139 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.6/139.6 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading catboost-1.2.7-cp311-cp311-manylinux2014_x86_64.whl (98.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 MB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading category_encoders-2.7.0-py3-none-any.whl (85 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.4/85.4 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading deprecation-2.1.0-py2.py3-none-any.whl (11 kB)\n",
            "Downloading evidently-0.4.40-py3-none-any.whl (3.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.5/3.5 MB\u001b[0m \u001b[31m78.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading explainerdashboard-0.4.8-py3-none-any.whl (287 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m287.9/287.9 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading fugue-0.8.7-py3-none-any.whl (279 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m279.8/279.8 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading gradio-5.22.0-py3-none-any.whl (46.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.2/46.2 MB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading gradio_client-1.8.0-py3-none-any.whl (322 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m322.2/322.2 kB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading fastapi-0.115.11-py3-none-any.whl (94 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.9/94.9 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading interpret-0.6.9-py3-none-any.whl (1.4 kB)\n",
            "Downloading interpret_core-0.6.9-py3-none-any.whl (15.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.0/15.0 MB\u001b[0m \u001b[31m90.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading joblib-1.3.2-py3-none-any.whl (302 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.2/302.2 kB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading kmodes-0.12.2-py2.py3-none-any.whl (20 kB)\n",
            "Downloading m2cgen-0.10.0-py3-none-any.whl (92 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/92.2 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading matplotlib-3.7.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m97.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading mlflow-2.21.0-py3-none-any.whl (28.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m28.2/28.2 MB\u001b[0m \u001b[31m56.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading mlflow_skinny-2.21.0-py3-none-any.whl (6.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.1/6.1 MB\u001b[0m \u001b[31m86.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading flask-2.3.3-py3-none-any.whl (96 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.1/96.1 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading moto-4.2.14-py2.py3-none-any.whl (3.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m77.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.3/18.3 MB\u001b[0m \u001b[31m74.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading optuna-4.2.1-py3-none-any.whl (383 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m383.6/383.6 kB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pandas-2.1.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m84.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading plotly_resampler-0.10.0-py3-none-any.whl (80 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.7/80.7 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pmdarima-2.0.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (2.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m62.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pytest-7.4.4-py3-none-any.whl (325 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m325.3/325.3 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading scikit_learn-1.4.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.1/12.1 MB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading scikit_learn_intelex-2025.2.0-py311-none-manylinux_2_28_x86_64.whl (4.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m79.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading daal-2025.2.0-py2.py3-none-manylinux_2_28_x86_64.whl (91.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.4/91.4 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading scikit_optimize-0.10.2-py2.py3-none-any.whl (107 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.8/107.8 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Downloading scipy-1.11.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m36.4/36.4 MB\u001b[0m \u001b[31m33.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading shap-0.44.1-cp311-cp311-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (535 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m535.8/535.8 kB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Downloading statsforecast-1.5.0-py3-none-any.whl (99 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.0/100.0 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tbats-1.1.3-py3-none-any.whl (44 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.0/44.0 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading uvicorn-0.34.0-py3-none-any.whl (62 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.3/62.3 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading werkzeug-2.3.8-py3-none-any.whl (242 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m242.3/242.3 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ydata_profiling-4.15.0-py2.py3-none-any.whl (395 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m395.7/395.7 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ImageHash-4.3.1-py2.py3-none-any.whl (296 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.5/296.5 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading optuna_integration-4.2.1-py3-none-any.whl (97 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.6/97.6 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pycaret-3.3.2-py3-none-any.whl (486 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.1/486.1 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading wurlitzer-3.1.1-py3-none-any.whl (8.6 kB)\n",
            "Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading adagio-0.2.6-py3-none-any.whl (19 kB)\n",
            "Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n",
            "Downloading alembic-1.15.1-py3-none-any.whl (231 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m231.8/231.8 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading botocore-1.37.16-py3-none-any.whl (13.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.4/13.4 MB\u001b[0m \u001b[31m85.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dacite-1.9.2-py3-none-any.whl (16 kB)\n",
            "Downloading dash-3.0.0-py3-none-any.whl (8.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.0/8.0 MB\u001b[0m \u001b[31m89.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dash_bootstrap_components-2.0.0-py3-none-any.whl (202 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202.4/202.4 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dash_testing_stub-0.0.2-py3-none-any.whl (2.6 kB)\n",
            "Downloading docker-7.1.0-py3-none-any.whl (147 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.8/147.8 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dtreeviz-2.2.2-py3-none-any.whl (91 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.8/91.8 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dynaconf-3.2.10-py2.py3-none-any.whl (236 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.2/236.2 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading flask_wtf-1.2.2-py3-none-any.whl (12 kB)\n",
            "Downloading graphene-3.4.3-py2.py3-none-any.whl (114 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.9/114.9 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading groovy-0.1.2-py3-none-any.whl (14 kB)\n",
            "Downloading gunicorn-23.0.0-py3-none-any.whl (85 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.0/85.0 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading iterative_telemetry-0.0.10-py3-none-any.whl (10 kB)\n",
            "Downloading jedi-0.19.2-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m55.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
            "Downloading jupyter_dash-0.4.2-py3-none-any.whl (23 kB)\n",
            "Downloading litestar-2.15.1-py3-none-any.whl (571 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m571.0/571.0 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multimethod-1.12-py3-none-any.whl (10 kB)\n",
            "Downloading multiprocess-0.70.17-py311-none-any.whl (144 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.3/144.3 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading percy-2.0.2-py2.py3-none-any.whl (12 kB)\n",
            "Downloading phik-0.12.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (687 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m687.8/687.8 kB\u001b[0m \u001b[31m33.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pyaml-25.1.0-py3-none-any.whl (26 kB)\n",
            "Downloading python_multipart-0.0.20-py3-none-any.whl (24 kB)\n",
            "Downloading qpd-0.4.4-py3-none-any.whl (169 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m169.2/169.2 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading responses-0.25.7-py3-none-any.whl (34 kB)\n",
            "Downloading ruff-0.11.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.3/11.3 MB\u001b[0m \u001b[31m92.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading s3transfer-0.11.4-py3-none-any.whl (84 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.4/84.4 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading safehttpx-0.1.6-py3-none-any.whl (8.7 kB)\n",
            "Downloading scikit_base-0.7.8-py3-none-any.whl (130 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.1/130.1 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading selenium-4.2.0-py3-none-any.whl (983 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m983.2/983.2 kB\u001b[0m \u001b[31m34.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
            "Downloading starlette-0.46.1-py3-none-any.whl (71 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tomlkit-0.13.2-py3-none-any.whl (37 kB)\n",
            "Downloading triad-0.9.8-py3-none-any.whl (62 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.3/62.3 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tsdownsample-0.1.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m49.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Downloading ujson-5.10.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (53 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.6/53.6 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading urllib3-1.26.20-py2.py3-none-any.whl (144 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.2/144.2 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading uuid6-2024.7.10-py3-none-any.whl (6.4 kB)\n",
            "Downloading visions-0.8.1-py3-none-any.whl (105 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.4/105.4 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading waitress-3.0.2-py3-none-any.whl (56 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.2/56.2 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl (79 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.1/79.1 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading colorlog-6.9.0-py3-none-any.whl (11 kB)\n",
            "Downloading dash_auth-2.3.0-py3-none-any.whl (14 kB)\n",
            "Downloading ffmpy-0.5.0-py3-none-any.whl (6.0 kB)\n",
            "Downloading flask_simplelogin-0.2.0-py3-none-any.whl (7.3 kB)\n",
            "Downloading oyaml-1.0-py2.py3-none-any.whl (3.0 kB)\n",
            "Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Downloading retrying-1.3.4-py3-none-any.whl (11 kB)\n",
            "Downloading xmltodict-0.14.2-py2.py3-none-any.whl (10.0 kB)\n",
            "Downloading antlr4_python3_runtime-4.11.1-py3-none-any.whl (144 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.2/144.2 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading aplr-10.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.7 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.7/6.7 MB\u001b[0m \u001b[31m81.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dash_core_components-2.0.0-py3-none-any.whl (3.8 kB)\n",
            "Downloading dash_html_components-2.0.0-py3-none-any.whl (4.1 kB)\n",
            "Downloading dash_table-5.0.0-py3-none-any.whl (3.9 kB)\n",
            "Downloading databricks_sdk-0.46.0-py3-none-any.whl (677 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m677.5/677.5 kB\u001b[0m \u001b[31m30.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dill-0.3.9-py3-none-any.whl (119 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.4/119.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading gevent-24.11.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m82.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading graphql_core-3.2.6-py3-none-any.whl (203 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m203.4/203.4 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading graphql_relay-3.2.0-py3-none-any.whl (16 kB)\n",
            "Downloading httptools-0.6.4-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (459 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m459.8/459.8 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading litestar_htmx-0.4.1-py3-none-any.whl (10.0 kB)\n",
            "Downloading msgspec-0.19.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (210 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m210.7/210.7 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multipart-1.2.1-py3-none-any.whl (13 kB)\n",
            "Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Downloading polyfactory-2.19.0-py3-none-any.whl (59 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.8/59.8 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
            "Downloading salib-1.5.1-py3-none-any.whl (778 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m778.9/778.9 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading trio-0.29.0-py3-none-any.whl (492 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m492.9/492.9 kB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading trio_websocket-0.12.2-py3-none-any.whl (21 kB)\n",
            "Downloading uvloop-0.21.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m69.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading watchfiles-1.0.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (452 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m452.6/452.6 kB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading wtforms-3.2.1-py3-none-any.whl (152 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.5/152.5 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ansi2html-1.9.2-py3-none-any.whl (17 kB)\n",
            "Downloading appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)\n",
            "Downloading fs-2.4.16-py2.py3-none-any.whl (135 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.3/135.3 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading Mako-1.3.9-py3-none-any.whl (78 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading puremagic-1.28-py3-none-any.whl (43 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.2/43.2 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading pywavelets-1.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m79.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading rich_click-1.8.8-py3-none-any.whl (35 kB)\n",
            "Downloading outcome-1.3.0.post0-py2.py3-none-any.whl (10 kB)\n",
            "Downloading wsproto-1.2.0-py3-none-any.whl (24 kB)\n",
            "Downloading faker-37.0.2-py3-none-any.whl (1.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m54.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading urllib3_secure_extra-0.1.0-py2.py3-none-any.whl (1.4 kB)\n",
            "Downloading zope.event-5.0-py3-none-any.whl (6.8 kB)\n",
            "Downloading zope.interface-7.2-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (259 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m259.8/259.8 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hBuilding wheels for collected packages: pyod, htmlmin, fugue-sql-antlr, stringcase, dash-cytoscape\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-2.0.3-py3-none-any.whl size=200466 sha256=b3121cd34ee93d78b1d08130f7d2f6c9d9e778f378f3eea86ae4440b0fae9629\n",
            "  Stored in directory: /root/.cache/pip/wheels/2d/60/5b/f74eccd2c9c892a2c298202ca510f10995f9940647fcc2d97f\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27081 sha256=0f020479eedc583aefe5952652ce9296700add2d59ac6922def4dd52491e8fc6\n",
            "  Stored in directory: /root/.cache/pip/wheels/8d/55/1a/19cd535375ed1ede0c996405ebffe34b196d78e2d9545723a2\n",
            "  Building wheel for fugue-sql-antlr (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fugue-sql-antlr: filename=fugue_sql_antlr-0.2.2-py3-none-any.whl size=158200 sha256=22cf50733c68f39be2c70331c24309ba40f763f28af8b2f2255973e3bdc0ff80\n",
            "  Stored in directory: /root/.cache/pip/wheels/a0/18/b5/191b8df81519a2fb4b4a61aa0105a10d7121cd945bed15bb50\n",
            "  Building wheel for stringcase (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for stringcase: filename=stringcase-1.2.0-py3-none-any.whl size=3568 sha256=9e36aadf2a453fc74a71a03e6f10b1bd46cfb56ebea1ba1ee98404685309d418\n",
            "  Stored in directory: /root/.cache/pip/wheels/b4/33/6d/d0820be98063da218c3206fbad2381cd2db3fbb1a0f0d254b5\n",
            "  Building wheel for dash-cytoscape (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-cytoscape: filename=dash_cytoscape-1.0.2-py3-none-any.whl size=4010716 sha256=129cd2fcf7d07823be5fbdedfbf70271a0a78c6d356a33fdee2dab2495dfd205\n",
            "  Stored in directory: /root/.cache/pip/wheels/99/b1/ab/6c999ab288b4849d372e23c0a8f6ece7edb7ffeb8c97959ab0\n",
            "Successfully built pyod htmlmin fugue-sql-antlr stringcase dash-cytoscape\n",
            "Installing collected packages: urllib3-secure-extra, stringcase, pydub, puremagic, kaleido, htmlmin, dash-testing-stub, dash-table, dash-html-components, dash-core-components, appdirs, antlr4-python3-runtime, zope.interface, zope.event, xxhash, xmltodict, wurlitzer, wtforms, wsproto, Werkzeug, watchdog, waitress, uvloop, uvicorn, uuid6, urllib3, ujson, tomlkit, slicer, semantic-version, scikit-base, schemdraw, ruff, retrying, python-multipart, python-dotenv, pytest, pyaml, oyaml, outcome, numpy, mypy-extensions, multipart, multimethod, msgspec, Mako, litestar-htmx, joblib, jmespath, jedi, httptools, gunicorn, groovy, graphql-core, fs, ffmpy, faker, dynaconf, dill, deprecation, dacite, colorlog, ansi2html, aiofiles, watchfiles, typing-inspect, tsdownsample, trio, starlette, scipy, PyWavelets, polyfactory, pandas, multiprocess, m2cgen, graphql-relay, gevent, flask, daal, botocore, aplr, alembic, visions, trio-websocket, triad, scikit-learn, safehttpx, s3transfer, rich-click, responses, percy, optuna, matplotlib, iterative-telemetry, imagehash, graphene, Flask-WTF, fastapi, docker, databricks-sdk, dash, sktime, shap, scikit-plot, scikit-optimize, scikit-learn-intelex, SALib, pyod, pmdarima, plotly-resampler, phik, optuna-integration, litestar, kmodes, jupyter_dash, interpret-core, gradio-client, fugue-sql-antlr, flask_simplelogin, fairlearn, dtreeviz, dash-cytoscape, dash-bootstrap-components, dash-auth, category-encoders, catboost, boto3, adagio, ydata-profiling, tbats, selenium, qpd, moto, mlflow-skinny, gradio, explainerdashboard, evidently, mlflow, interpret, fugue, statsforecast, pycaret\n",
            "  Attempting uninstall: Werkzeug\n",
            "    Found existing installation: Werkzeug 3.1.3\n",
            "    Uninstalling Werkzeug-3.1.3:\n",
            "      Successfully uninstalled Werkzeug-3.1.3\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.3.0\n",
            "    Uninstalling urllib3-2.3.0:\n",
            "      Successfully uninstalled urllib3-2.3.0\n",
            "  Attempting uninstall: slicer\n",
            "    Found existing installation: slicer 0.0.8\n",
            "    Uninstalling slicer-0.0.8:\n",
            "      Successfully uninstalled slicer-0.0.8\n",
            "  Attempting uninstall: pytest\n",
            "    Found existing installation: pytest 8.3.5\n",
            "    Uninstalling pytest-8.3.5:\n",
            "      Successfully uninstalled pytest-8.3.5\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 2.0.2\n",
            "    Uninstalling numpy-2.0.2:\n",
            "      Successfully uninstalled numpy-2.0.2\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.4.2\n",
            "    Uninstalling joblib-1.4.2:\n",
            "      Successfully uninstalled joblib-1.4.2\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.14.1\n",
            "    Uninstalling scipy-1.14.1:\n",
            "      Successfully uninstalled scipy-1.14.1\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 2.2.2\n",
            "    Uninstalling pandas-2.2.2:\n",
            "      Successfully uninstalled pandas-2.2.2\n",
            "  Attempting uninstall: flask\n",
            "    Found existing installation: Flask 3.1.0\n",
            "    Uninstalling Flask-3.1.0:\n",
            "      Successfully uninstalled Flask-3.1.0\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.6.1\n",
            "    Uninstalling scikit-learn-1.6.1:\n",
            "      Successfully uninstalled scikit-learn-1.6.1\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.10.0\n",
            "    Uninstalling matplotlib-3.10.0:\n",
            "      Successfully uninstalled matplotlib-3.10.0\n",
            "  Attempting uninstall: shap\n",
            "    Found existing installation: shap 0.46.0\n",
            "    Uninstalling shap-0.46.0:\n",
            "      Successfully uninstalled shap-0.46.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.1.4 which is incompatible.\n",
            "mizani 0.13.1 requires pandas>=2.2.0, but you have pandas 2.1.4 which is incompatible.\n",
            "plotnine 0.14.5 requires matplotlib>=3.8.0, but you have matplotlib 3.7.5 which is incompatible.\n",
            "plotnine 0.14.5 requires pandas>=2.2.0, but you have pandas 2.1.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Flask-WTF-1.2.2 Mako-1.3.9 PyWavelets-1.8.0 SALib-1.5.1 Werkzeug-2.3.8 adagio-0.2.6 aiofiles-23.2.1 alembic-1.15.1 ansi2html-1.9.2 antlr4-python3-runtime-4.11.1 aplr-10.8.0 appdirs-1.4.4 boto3-1.37.16 botocore-1.37.16 catboost-1.2.7 category-encoders-2.7.0 colorlog-6.9.0 daal-2025.2.0 dacite-1.9.2 dash-3.0.0 dash-auth-2.3.0 dash-bootstrap-components-2.0.0 dash-core-components-2.0.0 dash-cytoscape-1.0.2 dash-html-components-2.0.0 dash-table-5.0.0 dash-testing-stub-0.0.2 databricks-sdk-0.46.0 deprecation-2.1.0 dill-0.3.9 docker-7.1.0 dtreeviz-2.2.2 dynaconf-3.2.10 evidently-0.4.40 explainerdashboard-0.4.8 fairlearn-0.7.0 faker-37.0.2 fastapi-0.115.11 ffmpy-0.5.0 flask-2.3.3 flask_simplelogin-0.2.0 fs-2.4.16 fugue-0.8.7 fugue-sql-antlr-0.2.2 gevent-24.11.1 gradio-5.22.0 gradio-client-1.8.0 graphene-3.4.3 graphql-core-3.2.6 graphql-relay-3.2.0 groovy-0.1.2 gunicorn-23.0.0 htmlmin-0.1.12 httptools-0.6.4 imagehash-4.3.1 interpret-0.6.9 interpret-core-0.6.9 iterative-telemetry-0.0.10 jedi-0.19.2 jmespath-1.0.1 joblib-1.3.2 jupyter_dash-0.4.2 kaleido-0.2.1 kmodes-0.12.2 litestar-2.15.1 litestar-htmx-0.4.1 m2cgen-0.10.0 matplotlib-3.7.5 mlflow-2.21.0 mlflow-skinny-2.21.0 moto-4.2.14 msgspec-0.19.0 multimethod-1.12 multipart-1.2.1 multiprocess-0.70.17 mypy-extensions-1.0.0 numpy-1.26.4 optuna-4.2.1 optuna-integration-4.2.1 outcome-1.3.0.post0 oyaml-1.0 pandas-2.1.4 percy-2.0.2 phik-0.12.4 plotly-resampler-0.10.0 pmdarima-2.0.4 polyfactory-2.19.0 puremagic-1.28 pyaml-25.1.0 pycaret-3.3.2 pydub-0.25.1 pyod-2.0.3 pytest-7.4.4 python-dotenv-1.0.1 python-multipart-0.0.20 qpd-0.4.4 responses-0.25.7 retrying-1.3.4 rich-click-1.8.8 ruff-0.11.0 s3transfer-0.11.4 safehttpx-0.1.6 schemdraw-0.15 scikit-base-0.7.8 scikit-learn-1.4.2 scikit-learn-intelex-2025.2.0 scikit-optimize-0.10.2 scikit-plot-0.3.7 scipy-1.11.4 selenium-4.2.0 semantic-version-2.10.0 shap-0.44.1 sktime-0.26.0 slicer-0.0.7 starlette-0.46.1 statsforecast-1.5.0 stringcase-1.2.0 tbats-1.1.3 tomlkit-0.13.2 triad-0.9.8 trio-0.29.0 trio-websocket-0.12.2 tsdownsample-0.1.4.1 typing-inspect-0.9.0 ujson-5.10.0 urllib3-1.26.20 urllib3-secure-extra-0.1.0 uuid6-2024.7.10 uvicorn-0.34.0 uvloop-0.21.0 visions-0.8.1 waitress-3.0.2 watchdog-6.0.0 watchfiles-1.0.4 wsproto-1.2.0 wtforms-3.2.1 wurlitzer-3.1.1 xmltodict-0.14.2 xxhash-3.5.0 ydata-profiling-4.15.0 zope.event-5.0 zope.interface-7.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "joblib",
                  "matplotlib",
                  "mpl_toolkits",
                  "sklearn"
                ]
              },
              "id": "5e6713a028b24b259b2d07f91cc30073"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwClIIrqnX-L",
        "outputId": "36869519-b866-458d-d654-ffebbd87e77f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install pyyaml==5.4.1\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pyyaml==5.4.1 in /usr/local/lib/python3.7/dist-packages (5.4.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VKGDPlBlpaI"
      },
      "source": [
        "By installing the full version of pycaret, all the optional dependencies as listed here are also installed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hh4GSJw-1Y5a"
      },
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Import the necessary packages**</p>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install markupsafe==2.0.1\n"
      ],
      "metadata": {
        "id": "RK3KHprB0smS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5eccdc0e-c476-4206-dcf7-f5ef45c855ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting markupsafe==2.0.1\n",
            "  Downloading MarkupSafe-2.0.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (31 kB)\n",
            "Installing collected packages: markupsafe\n",
            "  Attempting uninstall: markupsafe\n",
            "    Found existing installation: MarkupSafe 2.1.1\n",
            "    Uninstalling MarkupSafe-2.1.1:\n",
            "      Successfully uninstalled MarkupSafe-2.1.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pandas-profiling 3.2.0 requires markupsafe~=2.1.1, but you have markupsafe 2.0.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed markupsafe-2.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Runtime> Restart Runtime"
      ],
      "metadata": {
        "id": "6LPVVcETrlFq"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQ2lwtepnpJw"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pycaret\n",
        "import jinja2\n",
        "#from pycaret.regression import*\n",
        "from pycaret.classification import*\n",
        "#pycaret.classification import *\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Dataset**</p>"
      ],
      "metadata": {
        "id": "7qKvhCCSXctf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.datasets import get_data\n",
        "all_datasets = get_data()"
      ],
      "metadata": {
        "id": "56uS89neX3Qm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1950ebbf-9e66-4efe-ba79-31395b4b72b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                             Dataset    Data Types  \\\n",
              "0                            anomaly  Multivariate   \n",
              "1                             france  Multivariate   \n",
              "2                            germany  Multivariate   \n",
              "3                               bank  Multivariate   \n",
              "4                              blood  Multivariate   \n",
              "5                             cancer  Multivariate   \n",
              "6                             credit  Multivariate   \n",
              "7                           diabetes  Multivariate   \n",
              "8                    electrical_grid  Multivariate   \n",
              "9                           employee  Multivariate   \n",
              "10                             heart  Multivariate   \n",
              "11                     heart_disease  Multivariate   \n",
              "12                         hepatitis  Multivariate   \n",
              "13                            income  Multivariate   \n",
              "14                             juice  Multivariate   \n",
              "15                               nba  Multivariate   \n",
              "16                              wine  Multivariate   \n",
              "17                         telescope  Multivariate   \n",
              "18                           titanic  Multivariate   \n",
              "19  us_presidential_election_results  Multivariate   \n",
              "20                             glass  Multivariate   \n",
              "21                              iris  Multivariate   \n",
              "22                             poker  Multivariate   \n",
              "23                         questions  Multivariate   \n",
              "24                         satellite  Multivariate   \n",
              "25                               CTG  Multivariate   \n",
              "26                          asia_gdp  Multivariate   \n",
              "27                         elections  Multivariate   \n",
              "28                          facebook  Multivariate   \n",
              "29                               ipl  Multivariate   \n",
              "30                         jewellery  Multivariate   \n",
              "31                              mice  Multivariate   \n",
              "32                         migration  Multivariate   \n",
              "33                           perfume  Multivariate   \n",
              "34                           pokemon  Multivariate   \n",
              "35                        population  Multivariate   \n",
              "36                     public_health  Multivariate   \n",
              "37                             seeds  Multivariate   \n",
              "38                         wholesale  Multivariate   \n",
              "39                            tweets          Text   \n",
              "40                            amazon          Text   \n",
              "41                              kiva          Text   \n",
              "42                               spx          Text   \n",
              "43                         wikipedia          Text   \n",
              "44                        automobile  Multivariate   \n",
              "45                              bike  Multivariate   \n",
              "46                            boston  Multivariate   \n",
              "47                          concrete  Multivariate   \n",
              "48                           diamond  Multivariate   \n",
              "49                            energy  Multivariate   \n",
              "50                            forest  Multivariate   \n",
              "51                              gold  Multivariate   \n",
              "52                             house  Multivariate   \n",
              "53                         insurance  Multivariate   \n",
              "54                        parkinsons  Multivariate   \n",
              "55                           traffic  Multivariate   \n",
              "\n",
              "                   Default Task Target Variable 1 Target Variable 2  \\\n",
              "0             Anomaly Detection               NaN               NaN   \n",
              "1       Association Rule Mining         InvoiceNo       Description   \n",
              "2       Association Rule Mining         InvoiceNo       Description   \n",
              "3       Classification (Binary)           deposit               NaN   \n",
              "4       Classification (Binary)             Class               NaN   \n",
              "5       Classification (Binary)             Class               NaN   \n",
              "6       Classification (Binary)           default               NaN   \n",
              "7       Classification (Binary)    Class variable               NaN   \n",
              "8       Classification (Binary)             stabf               NaN   \n",
              "9       Classification (Binary)              left               NaN   \n",
              "10      Classification (Binary)             DEATH               NaN   \n",
              "11      Classification (Binary)           Disease               NaN   \n",
              "12      Classification (Binary)             Class               NaN   \n",
              "13      Classification (Binary)       income >50K               NaN   \n",
              "14      Classification (Binary)          Purchase               NaN   \n",
              "15      Classification (Binary)       TARGET_5Yrs               NaN   \n",
              "16      Classification (Binary)              type               NaN   \n",
              "17      Classification (Binary)             Class               NaN   \n",
              "18      Classification (Binary)          Survived               NaN   \n",
              "19      Classification (Binary)      party_winner               NaN   \n",
              "20  Classification (Multiclass)              Type               NaN   \n",
              "21  Classification (Multiclass)           species               NaN   \n",
              "22  Classification (Multiclass)             CLASS               NaN   \n",
              "23  Classification (Multiclass)     Next_Question               NaN   \n",
              "24  Classification (Multiclass)             Class               NaN   \n",
              "25  Classification (Multiclass)               NSP               NaN   \n",
              "26                   Clustering               NaN               NaN   \n",
              "27                   Clustering               NaN               NaN   \n",
              "28                   Clustering               NaN               NaN   \n",
              "29                   Clustering               NaN               NaN   \n",
              "30                   Clustering               NaN               NaN   \n",
              "31                   Clustering               NaN               NaN   \n",
              "32                   Clustering               NaN               NaN   \n",
              "33                   Clustering               NaN               NaN   \n",
              "34                   Clustering               NaN               NaN   \n",
              "35                   Clustering               NaN               NaN   \n",
              "36                   Clustering               NaN               NaN   \n",
              "37                   Clustering               NaN               NaN   \n",
              "38                   Clustering               NaN               NaN   \n",
              "39                          NLP             tweet               NaN   \n",
              "40         NLP / Classification        reviewText               NaN   \n",
              "41         NLP / Classification                en               NaN   \n",
              "42             NLP / Regression              text               NaN   \n",
              "43         NLP / Classification              Text               NaN   \n",
              "44                   Regression             price               NaN   \n",
              "45                   Regression               cnt               NaN   \n",
              "46                   Regression              medv               NaN   \n",
              "47                   Regression          strength               NaN   \n",
              "48                   Regression             Price               NaN   \n",
              "49                   Regression      Heating Load      Cooling Load   \n",
              "50                   Regression              area               NaN   \n",
              "51                   Regression         Gold_T+22               NaN   \n",
              "52                   Regression         SalePrice               NaN   \n",
              "53                   Regression           charges               NaN   \n",
              "54                   Regression               PPE               NaN   \n",
              "55                   Regression    traffic_volume               NaN   \n",
              "\n",
              "    # Instances  # Attributes Missing Values  \n",
              "0          1000            10              N  \n",
              "1          8557             8              N  \n",
              "2          9495             8              N  \n",
              "3         45211            17              N  \n",
              "4           748             5              N  \n",
              "5           683            10              N  \n",
              "6         24000            24              N  \n",
              "7           768             9              N  \n",
              "8         10000            14              N  \n",
              "9         14999            10              N  \n",
              "10          200            16              N  \n",
              "11          270            14              N  \n",
              "12          154            32              Y  \n",
              "13        32561            14              Y  \n",
              "14         1070            15              N  \n",
              "15         1340            21              N  \n",
              "16         6498            13              N  \n",
              "17        19020            11              N  \n",
              "18          891            11              Y  \n",
              "19          497             7              N  \n",
              "20          214            10              N  \n",
              "21          150             5              N  \n",
              "22       100000            11              N  \n",
              "23          499             4              N  \n",
              "24         6435            37              N  \n",
              "25         2129            40              Y  \n",
              "26           40            11              N  \n",
              "27         3195            54              Y  \n",
              "28         7050            12              N  \n",
              "29          153            25              N  \n",
              "30          505             4              N  \n",
              "31         1080            82              Y  \n",
              "32          233            12              N  \n",
              "33           20            29              N  \n",
              "34          800            13              Y  \n",
              "35          255            56              Y  \n",
              "36          224            21              N  \n",
              "37          210             7              N  \n",
              "38          440             8              N  \n",
              "39         8594             2              N  \n",
              "40        20000             2              N  \n",
              "41         6818             7              N  \n",
              "42          874             4              N  \n",
              "43          500             3              N  \n",
              "44          202            26              Y  \n",
              "45        17379            15              N  \n",
              "46          506            14              N  \n",
              "47         1030             9              N  \n",
              "48         6000             8              N  \n",
              "49          768            10              N  \n",
              "50          517            13              N  \n",
              "51         2558           121              N  \n",
              "52         1461            81              Y  \n",
              "53         1338             7              N  \n",
              "54         5875            22              N  \n",
              "55        48204             8              N  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1bac05c9-5583-458c-9074-05dc71f20173\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "      <th>Data Types</th>\n",
              "      <th>Default Task</th>\n",
              "      <th>Target Variable 1</th>\n",
              "      <th>Target Variable 2</th>\n",
              "      <th># Instances</th>\n",
              "      <th># Attributes</th>\n",
              "      <th>Missing Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>anomaly</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Anomaly Detection</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000</td>\n",
              "      <td>10</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>france</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Association Rule Mining</td>\n",
              "      <td>InvoiceNo</td>\n",
              "      <td>Description</td>\n",
              "      <td>8557</td>\n",
              "      <td>8</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>germany</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Association Rule Mining</td>\n",
              "      <td>InvoiceNo</td>\n",
              "      <td>Description</td>\n",
              "      <td>9495</td>\n",
              "      <td>8</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bank</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45211</td>\n",
              "      <td>17</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>blood</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>Class</td>\n",
              "      <td>NaN</td>\n",
              "      <td>748</td>\n",
              "      <td>5</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>cancer</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>Class</td>\n",
              "      <td>NaN</td>\n",
              "      <td>683</td>\n",
              "      <td>10</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>credit</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>default</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24000</td>\n",
              "      <td>24</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>diabetes</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>Class variable</td>\n",
              "      <td>NaN</td>\n",
              "      <td>768</td>\n",
              "      <td>9</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>electrical_grid</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>stabf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10000</td>\n",
              "      <td>14</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>employee</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>left</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14999</td>\n",
              "      <td>10</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>heart</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>DEATH</td>\n",
              "      <td>NaN</td>\n",
              "      <td>200</td>\n",
              "      <td>16</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>heart_disease</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>Disease</td>\n",
              "      <td>NaN</td>\n",
              "      <td>270</td>\n",
              "      <td>14</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>hepatitis</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>Class</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154</td>\n",
              "      <td>32</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>income</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>income &gt;50K</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32561</td>\n",
              "      <td>14</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>juice</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>Purchase</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1070</td>\n",
              "      <td>15</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>nba</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>TARGET_5Yrs</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1340</td>\n",
              "      <td>21</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>wine</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>type</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6498</td>\n",
              "      <td>13</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>telescope</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>Class</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19020</td>\n",
              "      <td>11</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>titanic</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>Survived</td>\n",
              "      <td>NaN</td>\n",
              "      <td>891</td>\n",
              "      <td>11</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>us_presidential_election_results</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Binary)</td>\n",
              "      <td>party_winner</td>\n",
              "      <td>NaN</td>\n",
              "      <td>497</td>\n",
              "      <td>7</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>glass</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Multiclass)</td>\n",
              "      <td>Type</td>\n",
              "      <td>NaN</td>\n",
              "      <td>214</td>\n",
              "      <td>10</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>iris</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Multiclass)</td>\n",
              "      <td>species</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150</td>\n",
              "      <td>5</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>poker</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Multiclass)</td>\n",
              "      <td>CLASS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100000</td>\n",
              "      <td>11</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>questions</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Multiclass)</td>\n",
              "      <td>Next_Question</td>\n",
              "      <td>NaN</td>\n",
              "      <td>499</td>\n",
              "      <td>4</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>satellite</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Multiclass)</td>\n",
              "      <td>Class</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6435</td>\n",
              "      <td>37</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>CTG</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Classification (Multiclass)</td>\n",
              "      <td>NSP</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2129</td>\n",
              "      <td>40</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>asia_gdp</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40</td>\n",
              "      <td>11</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>elections</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3195</td>\n",
              "      <td>54</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>facebook</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7050</td>\n",
              "      <td>12</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>ipl</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>153</td>\n",
              "      <td>25</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>jewellery</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>505</td>\n",
              "      <td>4</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>mice</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1080</td>\n",
              "      <td>82</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>migration</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>233</td>\n",
              "      <td>12</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>perfume</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20</td>\n",
              "      <td>29</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>pokemon</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>800</td>\n",
              "      <td>13</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>population</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>255</td>\n",
              "      <td>56</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>public_health</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>224</td>\n",
              "      <td>21</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>seeds</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>210</td>\n",
              "      <td>7</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>wholesale</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Clustering</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>440</td>\n",
              "      <td>8</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>tweets</td>\n",
              "      <td>Text</td>\n",
              "      <td>NLP</td>\n",
              "      <td>tweet</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8594</td>\n",
              "      <td>2</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>amazon</td>\n",
              "      <td>Text</td>\n",
              "      <td>NLP / Classification</td>\n",
              "      <td>reviewText</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20000</td>\n",
              "      <td>2</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>kiva</td>\n",
              "      <td>Text</td>\n",
              "      <td>NLP / Classification</td>\n",
              "      <td>en</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6818</td>\n",
              "      <td>7</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>spx</td>\n",
              "      <td>Text</td>\n",
              "      <td>NLP / Regression</td>\n",
              "      <td>text</td>\n",
              "      <td>NaN</td>\n",
              "      <td>874</td>\n",
              "      <td>4</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>wikipedia</td>\n",
              "      <td>Text</td>\n",
              "      <td>NLP / Classification</td>\n",
              "      <td>Text</td>\n",
              "      <td>NaN</td>\n",
              "      <td>500</td>\n",
              "      <td>3</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>automobile</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>price</td>\n",
              "      <td>NaN</td>\n",
              "      <td>202</td>\n",
              "      <td>26</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>bike</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>cnt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17379</td>\n",
              "      <td>15</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>boston</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>medv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>506</td>\n",
              "      <td>14</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>concrete</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>strength</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1030</td>\n",
              "      <td>9</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>diamond</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>Price</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6000</td>\n",
              "      <td>8</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>energy</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>Heating Load</td>\n",
              "      <td>Cooling Load</td>\n",
              "      <td>768</td>\n",
              "      <td>10</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>forest</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>area</td>\n",
              "      <td>NaN</td>\n",
              "      <td>517</td>\n",
              "      <td>13</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>gold</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>Gold_T+22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2558</td>\n",
              "      <td>121</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>house</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>SalePrice</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1461</td>\n",
              "      <td>81</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>insurance</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>charges</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1338</td>\n",
              "      <td>7</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>parkinsons</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>PPE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5875</td>\n",
              "      <td>22</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>traffic</td>\n",
              "      <td>Multivariate</td>\n",
              "      <td>Regression</td>\n",
              "      <td>traffic_volume</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48204</td>\n",
              "      <td>8</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1bac05c9-5583-458c-9074-05dc71f20173')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1bac05c9-5583-458c-9074-05dc71f20173 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1bac05c9-5583-458c-9074-05dc71f20173');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5d4998fe-b230-47b7-a5f9-c19b533f2cba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5d4998fe-b230-47b7-a5f9-c19b533f2cba')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5d4998fe-b230-47b7-a5f9-c19b533f2cba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"all_datasets = get_data()\",\n  \"rows\": 56,\n  \"fields\": [\n    {\n      \"column\": \"Dataset\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 56,\n        \"samples\": [\n          \"anomaly\",\n          \"cancer\",\n          \"perfume\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Data Types\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Text\",\n          \"Multivariate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Default Task\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"NLP / Regression\",\n          \"Association Rule Mining\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target Variable 1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"CLASS\",\n          \"Survived\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target Variable 2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cooling Load\",\n          \"Description\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"# Instances\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16351,\n        \"min\": 20,\n        \"max\": 100000,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          1080,\n          683\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"# Attributes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 2,\n        \"max\": 121,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          81,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Missing Values\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Y\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.datasets import get_data\n",
        "dataset = get_data('iris')"
      ],
      "metadata": {
        "id": "ip2XVa5LX-rp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "46d8e975-85e2-4eb9-b727-381002d1532d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d173ba3-22b6-406f-ae0d-e77f35d3df97\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d173ba3-22b6-406f-ae0d-e77f35d3df97')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0d173ba3-22b6-406f-ae0d-e77f35d3df97 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0d173ba3-22b6-406f-ae0d-e77f35d3df97');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e927380c-44fc-46f8-be3d-9c6eab496209\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e927380c-44fc-46f8-be3d-9c6eab496209')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e927380c-44fc-46f8-be3d-9c6eab496209 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset = get_data('iris')\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2073644135332772,\n        \"min\": 4.6,\n        \"max\": 5.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2588435821108957,\n        \"min\": 3.0,\n        \"max\": 3.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          3.6,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.3,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset.sample(frac=0.95, random_state=786).reset_index(drop=True)\n",
        "data_unseen = dataset.drop(data.index).reset_index(drop=True)\n",
        "\n",
        "print('Data for Modeling: ' + str(data.shape))\n",
        "print('Unseen Data For Predictions: ' + str(data_unseen.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBnbw0uNMBbL",
        "outputId": "a687b559-f357-41ac-9c30-0250b77cdc6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data for Modeling: (142, 5)\n",
            "Unseen Data For Predictions: (8, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**1- Classification**</p>"
      ],
      "metadata": {
        "id": "mdmt3jUofJOb"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmV8y9BlfDpF"
      },
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Setting up Environment for Classification**</p>\n",
        "\n",
        "Common to all modules in PyCaret, the setup is the first and the only mandatory step in any machine learning experiment using PyCaret. This function takes care of all the data preparation required prior to training models. Besides performing some basic default processing tasks, PyCaret also offers a wide array of pre-processing features. To learn more about all the preprocessing functionalities in PyCaret, you can see this link. We use ‘pycaret.classification’ to set up with original dataset, target, and session_id. After we do it, we compare any models.\n",
        "\n",
        "setup function in PyCaret, profiles the dataset and infers the data types for all input features. It is the first and the only mandatory step to start any machine learning experiment in PyCaret."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kiHZL2E-fpkr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "outputId": "365317b0-1113-4acf-d5e7-f575ef5167be"
      },
      "source": [
        "from pycaret.classification import *\n",
        "exp_mclf101 = setup(data = dataset, target = 'species', session_id=123, experiment_name = 'diamond')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0ea269c510>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_b9ed1_row9_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_b9ed1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b9ed1_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_b9ed1_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_b9ed1_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_b9ed1_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_b9ed1_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_b9ed1_row1_col1\" class=\"data row1 col1\" >species</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_b9ed1_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_b9ed1_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_b9ed1_row3_col0\" class=\"data row3 col0\" >Target mapping</td>\n",
              "      <td id=\"T_b9ed1_row3_col1\" class=\"data row3 col1\" >Iris-setosa: 0, Iris-versicolor: 1, Iris-virginica: 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_b9ed1_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n",
              "      <td id=\"T_b9ed1_row4_col1\" class=\"data row4 col1\" >(150, 5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_b9ed1_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_b9ed1_row5_col1\" class=\"data row5 col1\" >(150, 5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_b9ed1_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_b9ed1_row6_col1\" class=\"data row6 col1\" >(105, 5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_b9ed1_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_b9ed1_row7_col1\" class=\"data row7 col1\" >(45, 5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_b9ed1_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
              "      <td id=\"T_b9ed1_row8_col1\" class=\"data row8 col1\" >4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_b9ed1_row9_col0\" class=\"data row9 col0\" >Preprocess</td>\n",
              "      <td id=\"T_b9ed1_row9_col1\" class=\"data row9 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_b9ed1_row10_col0\" class=\"data row10 col0\" >Imputation type</td>\n",
              "      <td id=\"T_b9ed1_row10_col1\" class=\"data row10 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_b9ed1_row11_col0\" class=\"data row11 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_b9ed1_row11_col1\" class=\"data row11 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_b9ed1_row12_col0\" class=\"data row12 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_b9ed1_row12_col1\" class=\"data row12 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_b9ed1_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_b9ed1_row13_col1\" class=\"data row13 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_b9ed1_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
              "      <td id=\"T_b9ed1_row14_col1\" class=\"data row14 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_b9ed1_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_b9ed1_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_b9ed1_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
              "      <td id=\"T_b9ed1_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_b9ed1_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_b9ed1_row17_col1\" class=\"data row17 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_b9ed1_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_b9ed1_row18_col1\" class=\"data row18 col1\" >diamond</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b9ed1_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_b9ed1_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
              "      <td id=\"T_b9ed1_row19_col1\" class=\"data row19 col1\" >d897</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mpPVpLlEwC6w"
      },
      "source": [
        "When you initialize the setup function in PyCaret, it profiles the dataset and infers the data types for all input features. If all data types are correctly inferred, you can press enter to continue.[4]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ovUHsgDmwJBO"
      },
      "source": [
        "Notice that:[4]\n",
        "\n",
        "- I have passed log_experiment = True and experiment_name = 'diamond' , this will tell PyCaret to automatically log all the metrics, hyperparameters, and model artifacts behind the scene as you progress through the modeling phase. This is possible due to integration with MLflow.\n",
        "\n",
        "- Also, I have used transform_target = True inside the setup. PyCaret will transform the Price variable behind the scene using box-cox transformation. It affects the distribution of data in a similar way as log transformation (technically different). If you would like to learn more about box-cox transformations, you can refer to this link."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**1- Comparing all models**</p>"
      ],
      "metadata": {
        "id": "hZhwazLhr2Px"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vHop60ms9n7"
      },
      "source": [
        "This function trains all the available models in the model library using default hyperparameters and evaluates performance metrics using cross-validation. The number of folds can be defined using the foldparameter (default = 10 folds). The table is sorted (highest to lowest) by the metric of choice which can be defined using the sortparameter(in this case we have sorted it on RMSE)\n",
        "n_select parameter in the setup function controls the return of trained models. In this case, I am setting it to 15, meaning return the top 15 models as a list. pull function in the second line stores the output of compare_models as pd.DataFrame .\n",
        "\n",
        "Now that data is ready for modeling, let’s start the training process by using compare_models function. It will train all the algorithms available in the model library and evaluates multiple performance metrics using k-fold cross-validation.italicized text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZGSbzfZyG0s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684,
          "referenced_widgets": [
            "fe12c58a8d8b4334a69555bd5f8f9b22",
            "e3bde63d11f946acad4867b85b24dbfe",
            "d0d39b88563c404e88bc0fdde4395610",
            "351c3aa064ec4c9395a6bf9d8b565dab",
            "9b1657c5c0f743a8bcb7353d055721ba",
            "bbe09e543ebb4cb2a50f22b199c5796d",
            "bb3b1caa5e06445590cbfa1479cb0155",
            "4c5c5ba1e5734a13bec4dd4c57dc9681",
            "8859fb0160a44cc28655789d7a478f8f",
            "7dd20595731c41a89338bbb775a41636",
            "80a2059d0fcf4565ab4b05656c8999db"
          ]
        },
        "outputId": "a591b2bf-a445-493e-9ace-5287ec4e1fb7"
      },
      "source": [
        "compare_models()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0ea24057d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_9933b th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_9933b_row0_col0, #T_9933b_row0_col2, #T_9933b_row1_col0, #T_9933b_row1_col2, #T_9933b_row2_col0, #T_9933b_row2_col2, #T_9933b_row3_col0, #T_9933b_row3_col2, #T_9933b_row4_col0, #T_9933b_row4_col1, #T_9933b_row4_col3, #T_9933b_row4_col4, #T_9933b_row4_col5, #T_9933b_row4_col6, #T_9933b_row4_col7, #T_9933b_row5_col0, #T_9933b_row5_col1, #T_9933b_row5_col2, #T_9933b_row5_col3, #T_9933b_row5_col4, #T_9933b_row5_col5, #T_9933b_row5_col6, #T_9933b_row5_col7, #T_9933b_row6_col0, #T_9933b_row6_col1, #T_9933b_row6_col3, #T_9933b_row6_col4, #T_9933b_row6_col5, #T_9933b_row6_col6, #T_9933b_row6_col7, #T_9933b_row7_col0, #T_9933b_row7_col1, #T_9933b_row7_col2, #T_9933b_row7_col3, #T_9933b_row7_col4, #T_9933b_row7_col5, #T_9933b_row7_col6, #T_9933b_row7_col7, #T_9933b_row8_col0, #T_9933b_row8_col1, #T_9933b_row8_col2, #T_9933b_row8_col3, #T_9933b_row8_col4, #T_9933b_row8_col5, #T_9933b_row8_col6, #T_9933b_row8_col7, #T_9933b_row9_col0, #T_9933b_row9_col1, #T_9933b_row9_col2, #T_9933b_row9_col3, #T_9933b_row9_col4, #T_9933b_row9_col5, #T_9933b_row9_col6, #T_9933b_row9_col7, #T_9933b_row10_col0, #T_9933b_row10_col1, #T_9933b_row10_col2, #T_9933b_row10_col3, #T_9933b_row10_col4, #T_9933b_row10_col5, #T_9933b_row10_col6, #T_9933b_row10_col7, #T_9933b_row11_col0, #T_9933b_row11_col1, #T_9933b_row11_col2, #T_9933b_row11_col3, #T_9933b_row11_col4, #T_9933b_row11_col5, #T_9933b_row11_col6, #T_9933b_row11_col7, #T_9933b_row12_col0, #T_9933b_row12_col1, #T_9933b_row12_col2, #T_9933b_row12_col3, #T_9933b_row12_col4, #T_9933b_row12_col5, #T_9933b_row12_col6, #T_9933b_row12_col7, #T_9933b_row13_col0, #T_9933b_row13_col1, #T_9933b_row13_col2, #T_9933b_row13_col3, #T_9933b_row13_col4, #T_9933b_row13_col5, #T_9933b_row13_col6, #T_9933b_row13_col7, #T_9933b_row14_col0, #T_9933b_row14_col1, #T_9933b_row14_col2, #T_9933b_row14_col3, #T_9933b_row14_col4, #T_9933b_row14_col5, #T_9933b_row14_col6, #T_9933b_row14_col7, #T_9933b_row15_col0, #T_9933b_row15_col1, #T_9933b_row15_col2, #T_9933b_row15_col3, #T_9933b_row15_col4, #T_9933b_row15_col5, #T_9933b_row15_col6, #T_9933b_row15_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_9933b_row0_col1, #T_9933b_row0_col3, #T_9933b_row0_col4, #T_9933b_row0_col5, #T_9933b_row0_col6, #T_9933b_row0_col7, #T_9933b_row1_col1, #T_9933b_row1_col3, #T_9933b_row1_col4, #T_9933b_row1_col5, #T_9933b_row1_col6, #T_9933b_row1_col7, #T_9933b_row2_col1, #T_9933b_row2_col3, #T_9933b_row2_col4, #T_9933b_row2_col5, #T_9933b_row2_col6, #T_9933b_row2_col7, #T_9933b_row3_col1, #T_9933b_row3_col3, #T_9933b_row3_col4, #T_9933b_row3_col5, #T_9933b_row3_col6, #T_9933b_row3_col7, #T_9933b_row4_col2, #T_9933b_row6_col2 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_9933b_row0_col8, #T_9933b_row1_col8, #T_9933b_row2_col8, #T_9933b_row3_col8, #T_9933b_row4_col8, #T_9933b_row5_col8, #T_9933b_row6_col8, #T_9933b_row7_col8, #T_9933b_row8_col8, #T_9933b_row9_col8, #T_9933b_row10_col8, #T_9933b_row11_col8, #T_9933b_row12_col8, #T_9933b_row13_col8, #T_9933b_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_9933b_row15_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_9933b\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_9933b_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_9933b_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_9933b_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_9933b_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_9933b_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_9933b_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_9933b_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_9933b_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_9933b_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row0\" class=\"row_heading level0 row0\" >lr</th>\n",
              "      <td id=\"T_9933b_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_9933b_row0_col1\" class=\"data row0 col1\" >0.9718</td>\n",
              "      <td id=\"T_9933b_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row0_col3\" class=\"data row0 col3\" >0.9718</td>\n",
              "      <td id=\"T_9933b_row0_col4\" class=\"data row0 col4\" >0.9780</td>\n",
              "      <td id=\"T_9933b_row0_col5\" class=\"data row0 col5\" >0.9712</td>\n",
              "      <td id=\"T_9933b_row0_col6\" class=\"data row0 col6\" >0.9573</td>\n",
              "      <td id=\"T_9933b_row0_col7\" class=\"data row0 col7\" >0.9609</td>\n",
              "      <td id=\"T_9933b_row0_col8\" class=\"data row0 col8\" >0.8860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
              "      <td id=\"T_9933b_row1_col0\" class=\"data row1 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_9933b_row1_col1\" class=\"data row1 col1\" >0.9718</td>\n",
              "      <td id=\"T_9933b_row1_col2\" class=\"data row1 col2\" >0.9830</td>\n",
              "      <td id=\"T_9933b_row1_col3\" class=\"data row1 col3\" >0.9718</td>\n",
              "      <td id=\"T_9933b_row1_col4\" class=\"data row1 col4\" >0.9780</td>\n",
              "      <td id=\"T_9933b_row1_col5\" class=\"data row1 col5\" >0.9712</td>\n",
              "      <td id=\"T_9933b_row1_col6\" class=\"data row1 col6\" >0.9573</td>\n",
              "      <td id=\"T_9933b_row1_col7\" class=\"data row1 col7\" >0.9609</td>\n",
              "      <td id=\"T_9933b_row1_col8\" class=\"data row1 col8\" >0.0460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row2\" class=\"row_heading level0 row2\" >qda</th>\n",
              "      <td id=\"T_9933b_row2_col0\" class=\"data row2 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_9933b_row2_col1\" class=\"data row2 col1\" >0.9718</td>\n",
              "      <td id=\"T_9933b_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row2_col3\" class=\"data row2 col3\" >0.9718</td>\n",
              "      <td id=\"T_9933b_row2_col4\" class=\"data row2 col4\" >0.9780</td>\n",
              "      <td id=\"T_9933b_row2_col5\" class=\"data row2 col5\" >0.9712</td>\n",
              "      <td id=\"T_9933b_row2_col6\" class=\"data row2 col6\" >0.9573</td>\n",
              "      <td id=\"T_9933b_row2_col7\" class=\"data row2 col7\" >0.9609</td>\n",
              "      <td id=\"T_9933b_row2_col8\" class=\"data row2 col8\" >0.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row3\" class=\"row_heading level0 row3\" >lda</th>\n",
              "      <td id=\"T_9933b_row3_col0\" class=\"data row3 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_9933b_row3_col1\" class=\"data row3 col1\" >0.9718</td>\n",
              "      <td id=\"T_9933b_row3_col2\" class=\"data row3 col2\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row3_col3\" class=\"data row3 col3\" >0.9718</td>\n",
              "      <td id=\"T_9933b_row3_col4\" class=\"data row3 col4\" >0.9780</td>\n",
              "      <td id=\"T_9933b_row3_col5\" class=\"data row3 col5\" >0.9712</td>\n",
              "      <td id=\"T_9933b_row3_col6\" class=\"data row3 col6\" >0.9573</td>\n",
              "      <td id=\"T_9933b_row3_col7\" class=\"data row3 col7\" >0.9609</td>\n",
              "      <td id=\"T_9933b_row3_col8\" class=\"data row3 col8\" >0.0300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
              "      <td id=\"T_9933b_row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_9933b_row4_col1\" class=\"data row4 col1\" >0.9536</td>\n",
              "      <td id=\"T_9933b_row4_col2\" class=\"data row4 col2\" >0.9935</td>\n",
              "      <td id=\"T_9933b_row4_col3\" class=\"data row4 col3\" >0.9536</td>\n",
              "      <td id=\"T_9933b_row4_col4\" class=\"data row4 col4\" >0.9634</td>\n",
              "      <td id=\"T_9933b_row4_col5\" class=\"data row4 col5\" >0.9528</td>\n",
              "      <td id=\"T_9933b_row4_col6\" class=\"data row4 col6\" >0.9298</td>\n",
              "      <td id=\"T_9933b_row4_col7\" class=\"data row4 col7\" >0.9356</td>\n",
              "      <td id=\"T_9933b_row4_col8\" class=\"data row4 col8\" >0.2690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row5\" class=\"row_heading level0 row5\" >nb</th>\n",
              "      <td id=\"T_9933b_row5_col0\" class=\"data row5 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_9933b_row5_col1\" class=\"data row5 col1\" >0.9445</td>\n",
              "      <td id=\"T_9933b_row5_col2\" class=\"data row5 col2\" >0.9868</td>\n",
              "      <td id=\"T_9933b_row5_col3\" class=\"data row5 col3\" >0.9445</td>\n",
              "      <td id=\"T_9933b_row5_col4\" class=\"data row5 col4\" >0.9525</td>\n",
              "      <td id=\"T_9933b_row5_col5\" class=\"data row5 col5\" >0.9438</td>\n",
              "      <td id=\"T_9933b_row5_col6\" class=\"data row5 col6\" >0.9161</td>\n",
              "      <td id=\"T_9933b_row5_col7\" class=\"data row5 col7\" >0.9207</td>\n",
              "      <td id=\"T_9933b_row5_col8\" class=\"data row5 col8\" >0.0550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row6\" class=\"row_heading level0 row6\" >et</th>\n",
              "      <td id=\"T_9933b_row6_col0\" class=\"data row6 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_9933b_row6_col1\" class=\"data row6 col1\" >0.9445</td>\n",
              "      <td id=\"T_9933b_row6_col2\" class=\"data row6 col2\" >0.9935</td>\n",
              "      <td id=\"T_9933b_row6_col3\" class=\"data row6 col3\" >0.9445</td>\n",
              "      <td id=\"T_9933b_row6_col4\" class=\"data row6 col4\" >0.9586</td>\n",
              "      <td id=\"T_9933b_row6_col5\" class=\"data row6 col5\" >0.9426</td>\n",
              "      <td id=\"T_9933b_row6_col6\" class=\"data row6 col6\" >0.9161</td>\n",
              "      <td id=\"T_9933b_row6_col7\" class=\"data row6 col7\" >0.9246</td>\n",
              "      <td id=\"T_9933b_row6_col8\" class=\"data row6 col8\" >0.2420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row7\" class=\"row_heading level0 row7\" >catboost</th>\n",
              "      <td id=\"T_9933b_row7_col0\" class=\"data row7 col0\" >CatBoost Classifier</td>\n",
              "      <td id=\"T_9933b_row7_col1\" class=\"data row7 col1\" >0.9445</td>\n",
              "      <td id=\"T_9933b_row7_col2\" class=\"data row7 col2\" >0.9922</td>\n",
              "      <td id=\"T_9933b_row7_col3\" class=\"data row7 col3\" >0.9445</td>\n",
              "      <td id=\"T_9933b_row7_col4\" class=\"data row7 col4\" >0.9586</td>\n",
              "      <td id=\"T_9933b_row7_col5\" class=\"data row7 col5\" >0.9426</td>\n",
              "      <td id=\"T_9933b_row7_col6\" class=\"data row7 col6\" >0.9161</td>\n",
              "      <td id=\"T_9933b_row7_col7\" class=\"data row7 col7\" >0.9246</td>\n",
              "      <td id=\"T_9933b_row7_col8\" class=\"data row7 col8\" >0.9620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row8\" class=\"row_heading level0 row8\" >gbc</th>\n",
              "      <td id=\"T_9933b_row8_col0\" class=\"data row8 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_9933b_row8_col1\" class=\"data row8 col1\" >0.9355</td>\n",
              "      <td id=\"T_9933b_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row8_col3\" class=\"data row8 col3\" >0.9355</td>\n",
              "      <td id=\"T_9933b_row8_col4\" class=\"data row8 col4\" >0.9416</td>\n",
              "      <td id=\"T_9933b_row8_col5\" class=\"data row8 col5\" >0.9325</td>\n",
              "      <td id=\"T_9933b_row8_col6\" class=\"data row8 col6\" >0.9023</td>\n",
              "      <td id=\"T_9933b_row8_col7\" class=\"data row8 col7\" >0.9083</td>\n",
              "      <td id=\"T_9933b_row8_col8\" class=\"data row8 col8\" >0.2600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row9\" class=\"row_heading level0 row9\" >dt</th>\n",
              "      <td id=\"T_9933b_row9_col0\" class=\"data row9 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_9933b_row9_col1\" class=\"data row9 col1\" >0.9264</td>\n",
              "      <td id=\"T_9933b_row9_col2\" class=\"data row9 col2\" >0.9429</td>\n",
              "      <td id=\"T_9933b_row9_col3\" class=\"data row9 col3\" >0.9264</td>\n",
              "      <td id=\"T_9933b_row9_col4\" class=\"data row9 col4\" >0.9502</td>\n",
              "      <td id=\"T_9933b_row9_col5\" class=\"data row9 col5\" >0.9201</td>\n",
              "      <td id=\"T_9933b_row9_col6\" class=\"data row9 col6\" >0.8886</td>\n",
              "      <td id=\"T_9933b_row9_col7\" class=\"data row9 col7\" >0.9040</td>\n",
              "      <td id=\"T_9933b_row9_col8\" class=\"data row9 col8\" >0.0540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row10\" class=\"row_heading level0 row10\" >rf</th>\n",
              "      <td id=\"T_9933b_row10_col0\" class=\"data row10 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_9933b_row10_col1\" class=\"data row10 col1\" >0.9264</td>\n",
              "      <td id=\"T_9933b_row10_col2\" class=\"data row10 col2\" >0.9909</td>\n",
              "      <td id=\"T_9933b_row10_col3\" class=\"data row10 col3\" >0.9264</td>\n",
              "      <td id=\"T_9933b_row10_col4\" class=\"data row10 col4\" >0.9343</td>\n",
              "      <td id=\"T_9933b_row10_col5\" class=\"data row10 col5\" >0.9232</td>\n",
              "      <td id=\"T_9933b_row10_col6\" class=\"data row10 col6\" >0.8886</td>\n",
              "      <td id=\"T_9933b_row10_col7\" class=\"data row10 col7\" >0.8956</td>\n",
              "      <td id=\"T_9933b_row10_col8\" class=\"data row10 col8\" >0.1790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row11\" class=\"row_heading level0 row11\" >xgboost</th>\n",
              "      <td id=\"T_9933b_row11_col0\" class=\"data row11 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_9933b_row11_col1\" class=\"data row11 col1\" >0.9255</td>\n",
              "      <td id=\"T_9933b_row11_col2\" class=\"data row11 col2\" >0.9710</td>\n",
              "      <td id=\"T_9933b_row11_col3\" class=\"data row11 col3\" >0.9255</td>\n",
              "      <td id=\"T_9933b_row11_col4\" class=\"data row11 col4\" >0.9360</td>\n",
              "      <td id=\"T_9933b_row11_col5\" class=\"data row11 col5\" >0.9239</td>\n",
              "      <td id=\"T_9933b_row11_col6\" class=\"data row11 col6\" >0.8870</td>\n",
              "      <td id=\"T_9933b_row11_col7\" class=\"data row11 col7\" >0.8937</td>\n",
              "      <td id=\"T_9933b_row11_col8\" class=\"data row11 col8\" >0.0910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row12\" class=\"row_heading level0 row12\" >ada</th>\n",
              "      <td id=\"T_9933b_row12_col0\" class=\"data row12 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_9933b_row12_col1\" class=\"data row12 col1\" >0.9155</td>\n",
              "      <td id=\"T_9933b_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row12_col3\" class=\"data row12 col3\" >0.9155</td>\n",
              "      <td id=\"T_9933b_row12_col4\" class=\"data row12 col4\" >0.9401</td>\n",
              "      <td id=\"T_9933b_row12_col5\" class=\"data row12 col5\" >0.9097</td>\n",
              "      <td id=\"T_9933b_row12_col6\" class=\"data row12 col6\" >0.8720</td>\n",
              "      <td id=\"T_9933b_row12_col7\" class=\"data row12 col7\" >0.8873</td>\n",
              "      <td id=\"T_9933b_row12_col8\" class=\"data row12 col8\" >0.1040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row13\" class=\"row_heading level0 row13\" >ridge</th>\n",
              "      <td id=\"T_9933b_row13_col0\" class=\"data row13 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_9933b_row13_col1\" class=\"data row13 col1\" >0.8227</td>\n",
              "      <td id=\"T_9933b_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row13_col3\" class=\"data row13 col3\" >0.8227</td>\n",
              "      <td id=\"T_9933b_row13_col4\" class=\"data row13 col4\" >0.8437</td>\n",
              "      <td id=\"T_9933b_row13_col5\" class=\"data row13 col5\" >0.8186</td>\n",
              "      <td id=\"T_9933b_row13_col6\" class=\"data row13 col6\" >0.7320</td>\n",
              "      <td id=\"T_9933b_row13_col7\" class=\"data row13 col7\" >0.7454</td>\n",
              "      <td id=\"T_9933b_row13_col8\" class=\"data row13 col8\" >0.0320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
              "      <td id=\"T_9933b_row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_9933b_row14_col1\" class=\"data row14 col1\" >0.7618</td>\n",
              "      <td id=\"T_9933b_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row14_col3\" class=\"data row14 col3\" >0.7618</td>\n",
              "      <td id=\"T_9933b_row14_col4\" class=\"data row14 col4\" >0.6655</td>\n",
              "      <td id=\"T_9933b_row14_col5\" class=\"data row14 col5\" >0.6888</td>\n",
              "      <td id=\"T_9933b_row14_col6\" class=\"data row14 col6\" >0.6333</td>\n",
              "      <td id=\"T_9933b_row14_col7\" class=\"data row14 col7\" >0.7048</td>\n",
              "      <td id=\"T_9933b_row14_col8\" class=\"data row14 col8\" >0.0480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_9933b_level0_row15\" class=\"row_heading level0 row15\" >dummy</th>\n",
              "      <td id=\"T_9933b_row15_col0\" class=\"data row15 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_9933b_row15_col1\" class=\"data row15 col1\" >0.2864</td>\n",
              "      <td id=\"T_9933b_row15_col2\" class=\"data row15 col2\" >0.5000</td>\n",
              "      <td id=\"T_9933b_row15_col3\" class=\"data row15 col3\" >0.2864</td>\n",
              "      <td id=\"T_9933b_row15_col4\" class=\"data row15 col4\" >0.0822</td>\n",
              "      <td id=\"T_9933b_row15_col5\" class=\"data row15 col5\" >0.1277</td>\n",
              "      <td id=\"T_9933b_row15_col6\" class=\"data row15 col6\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row15_col7\" class=\"data row15 col7\" >0.0000</td>\n",
              "      <td id=\"T_9933b_row15_col8\" class=\"data row15 col8\" >0.0290</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/69 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe12c58a8d8b4334a69555bd5f8f9b22"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=123, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class=&#x27;auto&#x27;, n_jobs=None, penalty=&#x27;l2&#x27;,\n",
              "                   random_state=123, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class=&#x27;auto&#x27;, n_jobs=None, penalty=&#x27;l2&#x27;,\n",
              "                   random_state=123, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compare_models(sort = 'Recall')"
      ],
      "metadata": {
        "id": "CspHA1YY2TEk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "736d961677af48459236cfc4a27c47e5",
            "780a00ac92ac4efcbe5fb5c873c8eb97",
            "ffaaac9c620d43139617b0dece1763d0",
            "4336bf10c14d4c94a2bd5380bc4c9acc",
            "419f396041754b18b4bf15fea8dc5d86",
            "e2f33375c26f4081adc660ddc87e0f44",
            "aca67b6208af4e14920d1794218c49c2",
            "e6d92ceb3db24b3d9e08544e3b7a0110",
            "b60cc5e2e49e449dbf67319bfddb2738",
            "f48ac55177684624baeae207e1fc83c5",
            "4b4a94b356d14d0384a74caf5db0f6de"
          ]
        },
        "outputId": "90e35947-66f6-4c7f-adee-81f2602f6b88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7da5d746f1d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_2a3e0 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_2a3e0_row0_col0, #T_2a3e0_row0_col2, #T_2a3e0_row1_col0, #T_2a3e0_row1_col2, #T_2a3e0_row2_col0, #T_2a3e0_row2_col2, #T_2a3e0_row3_col0, #T_2a3e0_row3_col2, #T_2a3e0_row4_col0, #T_2a3e0_row4_col1, #T_2a3e0_row4_col3, #T_2a3e0_row4_col4, #T_2a3e0_row4_col5, #T_2a3e0_row4_col6, #T_2a3e0_row4_col7, #T_2a3e0_row5_col0, #T_2a3e0_row5_col1, #T_2a3e0_row5_col2, #T_2a3e0_row5_col3, #T_2a3e0_row5_col4, #T_2a3e0_row5_col5, #T_2a3e0_row5_col6, #T_2a3e0_row5_col7, #T_2a3e0_row6_col0, #T_2a3e0_row6_col1, #T_2a3e0_row6_col3, #T_2a3e0_row6_col4, #T_2a3e0_row6_col5, #T_2a3e0_row6_col6, #T_2a3e0_row6_col7, #T_2a3e0_row7_col0, #T_2a3e0_row7_col1, #T_2a3e0_row7_col2, #T_2a3e0_row7_col3, #T_2a3e0_row7_col4, #T_2a3e0_row7_col5, #T_2a3e0_row7_col6, #T_2a3e0_row7_col7, #T_2a3e0_row8_col0, #T_2a3e0_row8_col1, #T_2a3e0_row8_col2, #T_2a3e0_row8_col3, #T_2a3e0_row8_col4, #T_2a3e0_row8_col5, #T_2a3e0_row8_col6, #T_2a3e0_row8_col7, #T_2a3e0_row9_col0, #T_2a3e0_row9_col1, #T_2a3e0_row9_col2, #T_2a3e0_row9_col3, #T_2a3e0_row9_col4, #T_2a3e0_row9_col5, #T_2a3e0_row9_col6, #T_2a3e0_row9_col7, #T_2a3e0_row10_col0, #T_2a3e0_row10_col1, #T_2a3e0_row10_col2, #T_2a3e0_row10_col3, #T_2a3e0_row10_col4, #T_2a3e0_row10_col5, #T_2a3e0_row10_col6, #T_2a3e0_row10_col7, #T_2a3e0_row11_col0, #T_2a3e0_row11_col1, #T_2a3e0_row11_col2, #T_2a3e0_row11_col3, #T_2a3e0_row11_col4, #T_2a3e0_row11_col5, #T_2a3e0_row11_col6, #T_2a3e0_row11_col7, #T_2a3e0_row12_col0, #T_2a3e0_row12_col1, #T_2a3e0_row12_col2, #T_2a3e0_row12_col3, #T_2a3e0_row12_col4, #T_2a3e0_row12_col5, #T_2a3e0_row12_col6, #T_2a3e0_row12_col7, #T_2a3e0_row13_col0, #T_2a3e0_row13_col1, #T_2a3e0_row13_col2, #T_2a3e0_row13_col3, #T_2a3e0_row13_col4, #T_2a3e0_row13_col5, #T_2a3e0_row13_col6, #T_2a3e0_row13_col7, #T_2a3e0_row14_col0, #T_2a3e0_row14_col1, #T_2a3e0_row14_col2, #T_2a3e0_row14_col3, #T_2a3e0_row14_col4, #T_2a3e0_row14_col5, #T_2a3e0_row14_col6, #T_2a3e0_row14_col7, #T_2a3e0_row15_col0, #T_2a3e0_row15_col1, #T_2a3e0_row15_col2, #T_2a3e0_row15_col3, #T_2a3e0_row15_col4, #T_2a3e0_row15_col5, #T_2a3e0_row15_col6, #T_2a3e0_row15_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_2a3e0_row0_col1, #T_2a3e0_row0_col3, #T_2a3e0_row0_col4, #T_2a3e0_row0_col5, #T_2a3e0_row0_col6, #T_2a3e0_row0_col7, #T_2a3e0_row1_col1, #T_2a3e0_row1_col3, #T_2a3e0_row1_col4, #T_2a3e0_row1_col5, #T_2a3e0_row1_col6, #T_2a3e0_row1_col7, #T_2a3e0_row2_col1, #T_2a3e0_row2_col3, #T_2a3e0_row2_col4, #T_2a3e0_row2_col5, #T_2a3e0_row2_col6, #T_2a3e0_row2_col7, #T_2a3e0_row3_col1, #T_2a3e0_row3_col3, #T_2a3e0_row3_col4, #T_2a3e0_row3_col5, #T_2a3e0_row3_col6, #T_2a3e0_row3_col7, #T_2a3e0_row4_col2, #T_2a3e0_row6_col2 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_2a3e0_row0_col8, #T_2a3e0_row1_col8, #T_2a3e0_row2_col8, #T_2a3e0_row3_col8, #T_2a3e0_row4_col8, #T_2a3e0_row5_col8, #T_2a3e0_row6_col8, #T_2a3e0_row7_col8, #T_2a3e0_row8_col8, #T_2a3e0_row9_col8, #T_2a3e0_row10_col8, #T_2a3e0_row11_col8, #T_2a3e0_row12_col8, #T_2a3e0_row14_col8, #T_2a3e0_row15_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_2a3e0_row13_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_2a3e0\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_2a3e0_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_2a3e0_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_2a3e0_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_2a3e0_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_2a3e0_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_2a3e0_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_2a3e0_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_2a3e0_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_2a3e0_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row0\" class=\"row_heading level0 row0\" >lr</th>\n",
              "      <td id=\"T_2a3e0_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_2a3e0_row0_col1\" class=\"data row0 col1\" >0.9718</td>\n",
              "      <td id=\"T_2a3e0_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row0_col3\" class=\"data row0 col3\" >0.9718</td>\n",
              "      <td id=\"T_2a3e0_row0_col4\" class=\"data row0 col4\" >0.9780</td>\n",
              "      <td id=\"T_2a3e0_row0_col5\" class=\"data row0 col5\" >0.9712</td>\n",
              "      <td id=\"T_2a3e0_row0_col6\" class=\"data row0 col6\" >0.9573</td>\n",
              "      <td id=\"T_2a3e0_row0_col7\" class=\"data row0 col7\" >0.9609</td>\n",
              "      <td id=\"T_2a3e0_row0_col8\" class=\"data row0 col8\" >0.8340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
              "      <td id=\"T_2a3e0_row1_col0\" class=\"data row1 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_2a3e0_row1_col1\" class=\"data row1 col1\" >0.9718</td>\n",
              "      <td id=\"T_2a3e0_row1_col2\" class=\"data row1 col2\" >0.9830</td>\n",
              "      <td id=\"T_2a3e0_row1_col3\" class=\"data row1 col3\" >0.9718</td>\n",
              "      <td id=\"T_2a3e0_row1_col4\" class=\"data row1 col4\" >0.9780</td>\n",
              "      <td id=\"T_2a3e0_row1_col5\" class=\"data row1 col5\" >0.9712</td>\n",
              "      <td id=\"T_2a3e0_row1_col6\" class=\"data row1 col6\" >0.9573</td>\n",
              "      <td id=\"T_2a3e0_row1_col7\" class=\"data row1 col7\" >0.9609</td>\n",
              "      <td id=\"T_2a3e0_row1_col8\" class=\"data row1 col8\" >0.0440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row2\" class=\"row_heading level0 row2\" >qda</th>\n",
              "      <td id=\"T_2a3e0_row2_col0\" class=\"data row2 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_2a3e0_row2_col1\" class=\"data row2 col1\" >0.9718</td>\n",
              "      <td id=\"T_2a3e0_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row2_col3\" class=\"data row2 col3\" >0.9718</td>\n",
              "      <td id=\"T_2a3e0_row2_col4\" class=\"data row2 col4\" >0.9780</td>\n",
              "      <td id=\"T_2a3e0_row2_col5\" class=\"data row2 col5\" >0.9712</td>\n",
              "      <td id=\"T_2a3e0_row2_col6\" class=\"data row2 col6\" >0.9573</td>\n",
              "      <td id=\"T_2a3e0_row2_col7\" class=\"data row2 col7\" >0.9609</td>\n",
              "      <td id=\"T_2a3e0_row2_col8\" class=\"data row2 col8\" >0.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row3\" class=\"row_heading level0 row3\" >lda</th>\n",
              "      <td id=\"T_2a3e0_row3_col0\" class=\"data row3 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_2a3e0_row3_col1\" class=\"data row3 col1\" >0.9718</td>\n",
              "      <td id=\"T_2a3e0_row3_col2\" class=\"data row3 col2\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row3_col3\" class=\"data row3 col3\" >0.9718</td>\n",
              "      <td id=\"T_2a3e0_row3_col4\" class=\"data row3 col4\" >0.9780</td>\n",
              "      <td id=\"T_2a3e0_row3_col5\" class=\"data row3 col5\" >0.9712</td>\n",
              "      <td id=\"T_2a3e0_row3_col6\" class=\"data row3 col6\" >0.9573</td>\n",
              "      <td id=\"T_2a3e0_row3_col7\" class=\"data row3 col7\" >0.9609</td>\n",
              "      <td id=\"T_2a3e0_row3_col8\" class=\"data row3 col8\" >0.0290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
              "      <td id=\"T_2a3e0_row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_2a3e0_row4_col1\" class=\"data row4 col1\" >0.9536</td>\n",
              "      <td id=\"T_2a3e0_row4_col2\" class=\"data row4 col2\" >0.9935</td>\n",
              "      <td id=\"T_2a3e0_row4_col3\" class=\"data row4 col3\" >0.9536</td>\n",
              "      <td id=\"T_2a3e0_row4_col4\" class=\"data row4 col4\" >0.9634</td>\n",
              "      <td id=\"T_2a3e0_row4_col5\" class=\"data row4 col5\" >0.9528</td>\n",
              "      <td id=\"T_2a3e0_row4_col6\" class=\"data row4 col6\" >0.9298</td>\n",
              "      <td id=\"T_2a3e0_row4_col7\" class=\"data row4 col7\" >0.9356</td>\n",
              "      <td id=\"T_2a3e0_row4_col8\" class=\"data row4 col8\" >0.7210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row5\" class=\"row_heading level0 row5\" >nb</th>\n",
              "      <td id=\"T_2a3e0_row5_col0\" class=\"data row5 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_2a3e0_row5_col1\" class=\"data row5 col1\" >0.9445</td>\n",
              "      <td id=\"T_2a3e0_row5_col2\" class=\"data row5 col2\" >0.9868</td>\n",
              "      <td id=\"T_2a3e0_row5_col3\" class=\"data row5 col3\" >0.9445</td>\n",
              "      <td id=\"T_2a3e0_row5_col4\" class=\"data row5 col4\" >0.9525</td>\n",
              "      <td id=\"T_2a3e0_row5_col5\" class=\"data row5 col5\" >0.9438</td>\n",
              "      <td id=\"T_2a3e0_row5_col6\" class=\"data row5 col6\" >0.9161</td>\n",
              "      <td id=\"T_2a3e0_row5_col7\" class=\"data row5 col7\" >0.9207</td>\n",
              "      <td id=\"T_2a3e0_row5_col8\" class=\"data row5 col8\" >0.0330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row6\" class=\"row_heading level0 row6\" >et</th>\n",
              "      <td id=\"T_2a3e0_row6_col0\" class=\"data row6 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_2a3e0_row6_col1\" class=\"data row6 col1\" >0.9445</td>\n",
              "      <td id=\"T_2a3e0_row6_col2\" class=\"data row6 col2\" >0.9935</td>\n",
              "      <td id=\"T_2a3e0_row6_col3\" class=\"data row6 col3\" >0.9445</td>\n",
              "      <td id=\"T_2a3e0_row6_col4\" class=\"data row6 col4\" >0.9586</td>\n",
              "      <td id=\"T_2a3e0_row6_col5\" class=\"data row6 col5\" >0.9426</td>\n",
              "      <td id=\"T_2a3e0_row6_col6\" class=\"data row6 col6\" >0.9161</td>\n",
              "      <td id=\"T_2a3e0_row6_col7\" class=\"data row6 col7\" >0.9246</td>\n",
              "      <td id=\"T_2a3e0_row6_col8\" class=\"data row6 col8\" >0.1440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row7\" class=\"row_heading level0 row7\" >catboost</th>\n",
              "      <td id=\"T_2a3e0_row7_col0\" class=\"data row7 col0\" >CatBoost Classifier</td>\n",
              "      <td id=\"T_2a3e0_row7_col1\" class=\"data row7 col1\" >0.9445</td>\n",
              "      <td id=\"T_2a3e0_row7_col2\" class=\"data row7 col2\" >0.9922</td>\n",
              "      <td id=\"T_2a3e0_row7_col3\" class=\"data row7 col3\" >0.9445</td>\n",
              "      <td id=\"T_2a3e0_row7_col4\" class=\"data row7 col4\" >0.9586</td>\n",
              "      <td id=\"T_2a3e0_row7_col5\" class=\"data row7 col5\" >0.9426</td>\n",
              "      <td id=\"T_2a3e0_row7_col6\" class=\"data row7 col6\" >0.9161</td>\n",
              "      <td id=\"T_2a3e0_row7_col7\" class=\"data row7 col7\" >0.9246</td>\n",
              "      <td id=\"T_2a3e0_row7_col8\" class=\"data row7 col8\" >0.7160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row8\" class=\"row_heading level0 row8\" >gbc</th>\n",
              "      <td id=\"T_2a3e0_row8_col0\" class=\"data row8 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_2a3e0_row8_col1\" class=\"data row8 col1\" >0.9355</td>\n",
              "      <td id=\"T_2a3e0_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row8_col3\" class=\"data row8 col3\" >0.9355</td>\n",
              "      <td id=\"T_2a3e0_row8_col4\" class=\"data row8 col4\" >0.9416</td>\n",
              "      <td id=\"T_2a3e0_row8_col5\" class=\"data row8 col5\" >0.9325</td>\n",
              "      <td id=\"T_2a3e0_row8_col6\" class=\"data row8 col6\" >0.9023</td>\n",
              "      <td id=\"T_2a3e0_row8_col7\" class=\"data row8 col7\" >0.9083</td>\n",
              "      <td id=\"T_2a3e0_row8_col8\" class=\"data row8 col8\" >0.2610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row9\" class=\"row_heading level0 row9\" >dt</th>\n",
              "      <td id=\"T_2a3e0_row9_col0\" class=\"data row9 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_2a3e0_row9_col1\" class=\"data row9 col1\" >0.9264</td>\n",
              "      <td id=\"T_2a3e0_row9_col2\" class=\"data row9 col2\" >0.9429</td>\n",
              "      <td id=\"T_2a3e0_row9_col3\" class=\"data row9 col3\" >0.9264</td>\n",
              "      <td id=\"T_2a3e0_row9_col4\" class=\"data row9 col4\" >0.9502</td>\n",
              "      <td id=\"T_2a3e0_row9_col5\" class=\"data row9 col5\" >0.9201</td>\n",
              "      <td id=\"T_2a3e0_row9_col6\" class=\"data row9 col6\" >0.8886</td>\n",
              "      <td id=\"T_2a3e0_row9_col7\" class=\"data row9 col7\" >0.9040</td>\n",
              "      <td id=\"T_2a3e0_row9_col8\" class=\"data row9 col8\" >0.0370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row10\" class=\"row_heading level0 row10\" >rf</th>\n",
              "      <td id=\"T_2a3e0_row10_col0\" class=\"data row10 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_2a3e0_row10_col1\" class=\"data row10 col1\" >0.9264</td>\n",
              "      <td id=\"T_2a3e0_row10_col2\" class=\"data row10 col2\" >0.9909</td>\n",
              "      <td id=\"T_2a3e0_row10_col3\" class=\"data row10 col3\" >0.9264</td>\n",
              "      <td id=\"T_2a3e0_row10_col4\" class=\"data row10 col4\" >0.9343</td>\n",
              "      <td id=\"T_2a3e0_row10_col5\" class=\"data row10 col5\" >0.9232</td>\n",
              "      <td id=\"T_2a3e0_row10_col6\" class=\"data row10 col6\" >0.8886</td>\n",
              "      <td id=\"T_2a3e0_row10_col7\" class=\"data row10 col7\" >0.8956</td>\n",
              "      <td id=\"T_2a3e0_row10_col8\" class=\"data row10 col8\" >0.3170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row11\" class=\"row_heading level0 row11\" >xgboost</th>\n",
              "      <td id=\"T_2a3e0_row11_col0\" class=\"data row11 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_2a3e0_row11_col1\" class=\"data row11 col1\" >0.9255</td>\n",
              "      <td id=\"T_2a3e0_row11_col2\" class=\"data row11 col2\" >0.9710</td>\n",
              "      <td id=\"T_2a3e0_row11_col3\" class=\"data row11 col3\" >0.9255</td>\n",
              "      <td id=\"T_2a3e0_row11_col4\" class=\"data row11 col4\" >0.9360</td>\n",
              "      <td id=\"T_2a3e0_row11_col5\" class=\"data row11 col5\" >0.9239</td>\n",
              "      <td id=\"T_2a3e0_row11_col6\" class=\"data row11 col6\" >0.8870</td>\n",
              "      <td id=\"T_2a3e0_row11_col7\" class=\"data row11 col7\" >0.8937</td>\n",
              "      <td id=\"T_2a3e0_row11_col8\" class=\"data row11 col8\" >0.0580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row12\" class=\"row_heading level0 row12\" >ada</th>\n",
              "      <td id=\"T_2a3e0_row12_col0\" class=\"data row12 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_2a3e0_row12_col1\" class=\"data row12 col1\" >0.9155</td>\n",
              "      <td id=\"T_2a3e0_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row12_col3\" class=\"data row12 col3\" >0.9155</td>\n",
              "      <td id=\"T_2a3e0_row12_col4\" class=\"data row12 col4\" >0.9401</td>\n",
              "      <td id=\"T_2a3e0_row12_col5\" class=\"data row12 col5\" >0.9097</td>\n",
              "      <td id=\"T_2a3e0_row12_col6\" class=\"data row12 col6\" >0.8720</td>\n",
              "      <td id=\"T_2a3e0_row12_col7\" class=\"data row12 col7\" >0.8873</td>\n",
              "      <td id=\"T_2a3e0_row12_col8\" class=\"data row12 col8\" >0.1020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row13\" class=\"row_heading level0 row13\" >ridge</th>\n",
              "      <td id=\"T_2a3e0_row13_col0\" class=\"data row13 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_2a3e0_row13_col1\" class=\"data row13 col1\" >0.8227</td>\n",
              "      <td id=\"T_2a3e0_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row13_col3\" class=\"data row13 col3\" >0.8227</td>\n",
              "      <td id=\"T_2a3e0_row13_col4\" class=\"data row13 col4\" >0.8437</td>\n",
              "      <td id=\"T_2a3e0_row13_col5\" class=\"data row13 col5\" >0.8186</td>\n",
              "      <td id=\"T_2a3e0_row13_col6\" class=\"data row13 col6\" >0.7320</td>\n",
              "      <td id=\"T_2a3e0_row13_col7\" class=\"data row13 col7\" >0.7454</td>\n",
              "      <td id=\"T_2a3e0_row13_col8\" class=\"data row13 col8\" >0.0270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
              "      <td id=\"T_2a3e0_row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_2a3e0_row14_col1\" class=\"data row14 col1\" >0.7618</td>\n",
              "      <td id=\"T_2a3e0_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row14_col3\" class=\"data row14 col3\" >0.7618</td>\n",
              "      <td id=\"T_2a3e0_row14_col4\" class=\"data row14 col4\" >0.6655</td>\n",
              "      <td id=\"T_2a3e0_row14_col5\" class=\"data row14 col5\" >0.6888</td>\n",
              "      <td id=\"T_2a3e0_row14_col6\" class=\"data row14 col6\" >0.6333</td>\n",
              "      <td id=\"T_2a3e0_row14_col7\" class=\"data row14 col7\" >0.7048</td>\n",
              "      <td id=\"T_2a3e0_row14_col8\" class=\"data row14 col8\" >0.0350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2a3e0_level0_row15\" class=\"row_heading level0 row15\" >dummy</th>\n",
              "      <td id=\"T_2a3e0_row15_col0\" class=\"data row15 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_2a3e0_row15_col1\" class=\"data row15 col1\" >0.2864</td>\n",
              "      <td id=\"T_2a3e0_row15_col2\" class=\"data row15 col2\" >0.5000</td>\n",
              "      <td id=\"T_2a3e0_row15_col3\" class=\"data row15 col3\" >0.2864</td>\n",
              "      <td id=\"T_2a3e0_row15_col4\" class=\"data row15 col4\" >0.0822</td>\n",
              "      <td id=\"T_2a3e0_row15_col5\" class=\"data row15 col5\" >0.1277</td>\n",
              "      <td id=\"T_2a3e0_row15_col6\" class=\"data row15 col6\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row15_col7\" class=\"data row15 col7\" >0.0000</td>\n",
              "      <td id=\"T_2a3e0_row15_col8\" class=\"data row15 col8\" >0.0330</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/69 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "736d961677af48459236cfc4a27c47e5"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=123, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class=&#x27;auto&#x27;, n_jobs=None, penalty=&#x27;l2&#x27;,\n",
              "                   random_state=123, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class=&#x27;auto&#x27;, n_jobs=None, penalty=&#x27;l2&#x27;,\n",
              "                   random_state=123, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compare_models(fold = 5)"
      ],
      "metadata": {
        "id": "7e9zxxUe211z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "d552ed22fa7d4bff9281e1db7186b5e7",
            "a9dcf9df647f4cd68a04650d7fa6e39d",
            "e40178ae092b403980a3de17f0534594",
            "1f1cf46fe27541e9a5952ba95afb8b23",
            "67c8b9714fb84ae281bfb1a0b979f350",
            "1a552384c9bb4dc7bdd22298b5c9bd75",
            "807c5a3bd4fb42bca02bf90531218120",
            "199914b070b64ce2b4cf3391e3707db1",
            "fe4cbc2bd50b4cf9ae96d48abd3b7c1a",
            "18eb6201e0aa4adf9832606b5ec27d68",
            "4ec1aed20fce490389a06e8d23f3d109"
          ]
        },
        "outputId": "1864651f-e8de-43c7-9329-16d1a99f01a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7da5d737f750>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_947db th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_947db_row0_col0, #T_947db_row0_col2, #T_947db_row1_col0, #T_947db_row1_col2, #T_947db_row2_col0, #T_947db_row2_col2, #T_947db_row3_col0, #T_947db_row3_col1, #T_947db_row3_col2, #T_947db_row3_col3, #T_947db_row3_col4, #T_947db_row3_col5, #T_947db_row3_col6, #T_947db_row3_col7, #T_947db_row4_col0, #T_947db_row4_col1, #T_947db_row4_col3, #T_947db_row4_col4, #T_947db_row4_col5, #T_947db_row4_col6, #T_947db_row4_col7, #T_947db_row5_col0, #T_947db_row5_col1, #T_947db_row5_col2, #T_947db_row5_col3, #T_947db_row5_col4, #T_947db_row5_col5, #T_947db_row5_col6, #T_947db_row5_col7, #T_947db_row6_col0, #T_947db_row6_col1, #T_947db_row6_col2, #T_947db_row6_col3, #T_947db_row6_col4, #T_947db_row6_col5, #T_947db_row6_col6, #T_947db_row6_col7, #T_947db_row7_col0, #T_947db_row7_col1, #T_947db_row7_col3, #T_947db_row7_col4, #T_947db_row7_col5, #T_947db_row7_col6, #T_947db_row7_col7, #T_947db_row8_col0, #T_947db_row8_col1, #T_947db_row8_col3, #T_947db_row8_col4, #T_947db_row8_col5, #T_947db_row8_col6, #T_947db_row8_col7, #T_947db_row9_col0, #T_947db_row9_col1, #T_947db_row9_col2, #T_947db_row9_col3, #T_947db_row9_col4, #T_947db_row9_col5, #T_947db_row9_col6, #T_947db_row9_col7, #T_947db_row10_col0, #T_947db_row10_col1, #T_947db_row10_col2, #T_947db_row10_col3, #T_947db_row10_col4, #T_947db_row10_col5, #T_947db_row10_col6, #T_947db_row10_col7, #T_947db_row11_col0, #T_947db_row11_col1, #T_947db_row11_col2, #T_947db_row11_col3, #T_947db_row11_col4, #T_947db_row11_col5, #T_947db_row11_col6, #T_947db_row11_col7, #T_947db_row12_col0, #T_947db_row12_col1, #T_947db_row12_col2, #T_947db_row12_col3, #T_947db_row12_col4, #T_947db_row12_col5, #T_947db_row12_col6, #T_947db_row12_col7, #T_947db_row13_col0, #T_947db_row13_col1, #T_947db_row13_col2, #T_947db_row13_col3, #T_947db_row13_col4, #T_947db_row13_col5, #T_947db_row13_col6, #T_947db_row13_col7, #T_947db_row14_col0, #T_947db_row14_col1, #T_947db_row14_col2, #T_947db_row14_col3, #T_947db_row14_col4, #T_947db_row14_col5, #T_947db_row14_col6, #T_947db_row14_col7, #T_947db_row15_col0, #T_947db_row15_col1, #T_947db_row15_col2, #T_947db_row15_col3, #T_947db_row15_col4, #T_947db_row15_col5, #T_947db_row15_col6, #T_947db_row15_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_947db_row0_col1, #T_947db_row0_col3, #T_947db_row0_col4, #T_947db_row0_col5, #T_947db_row0_col6, #T_947db_row0_col7, #T_947db_row1_col1, #T_947db_row1_col3, #T_947db_row1_col4, #T_947db_row1_col5, #T_947db_row1_col6, #T_947db_row1_col7, #T_947db_row2_col1, #T_947db_row2_col3, #T_947db_row2_col4, #T_947db_row2_col5, #T_947db_row2_col6, #T_947db_row2_col7, #T_947db_row4_col2, #T_947db_row7_col2, #T_947db_row8_col2 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_947db_row0_col8, #T_947db_row2_col8, #T_947db_row3_col8, #T_947db_row4_col8, #T_947db_row5_col8, #T_947db_row6_col8, #T_947db_row7_col8, #T_947db_row8_col8, #T_947db_row9_col8, #T_947db_row10_col8, #T_947db_row11_col8, #T_947db_row12_col8, #T_947db_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_947db_row1_col8, #T_947db_row13_col8, #T_947db_row15_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_947db\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_947db_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_947db_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_947db_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_947db_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_947db_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_947db_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_947db_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_947db_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_947db_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row0\" class=\"row_heading level0 row0\" >lr</th>\n",
              "      <td id=\"T_947db_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_947db_row0_col1\" class=\"data row0 col1\" >0.9714</td>\n",
              "      <td id=\"T_947db_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
              "      <td id=\"T_947db_row0_col3\" class=\"data row0 col3\" >0.9714</td>\n",
              "      <td id=\"T_947db_row0_col4\" class=\"data row0 col4\" >0.9750</td>\n",
              "      <td id=\"T_947db_row0_col5\" class=\"data row0 col5\" >0.9713</td>\n",
              "      <td id=\"T_947db_row0_col6\" class=\"data row0 col6\" >0.9571</td>\n",
              "      <td id=\"T_947db_row0_col7\" class=\"data row0 col7\" >0.9590</td>\n",
              "      <td id=\"T_947db_row0_col8\" class=\"data row0 col8\" >0.0420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row1\" class=\"row_heading level0 row1\" >qda</th>\n",
              "      <td id=\"T_947db_row1_col0\" class=\"data row1 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_947db_row1_col1\" class=\"data row1 col1\" >0.9714</td>\n",
              "      <td id=\"T_947db_row1_col2\" class=\"data row1 col2\" >0.0000</td>\n",
              "      <td id=\"T_947db_row1_col3\" class=\"data row1 col3\" >0.9714</td>\n",
              "      <td id=\"T_947db_row1_col4\" class=\"data row1 col4\" >0.9750</td>\n",
              "      <td id=\"T_947db_row1_col5\" class=\"data row1 col5\" >0.9713</td>\n",
              "      <td id=\"T_947db_row1_col6\" class=\"data row1 col6\" >0.9571</td>\n",
              "      <td id=\"T_947db_row1_col7\" class=\"data row1 col7\" >0.9590</td>\n",
              "      <td id=\"T_947db_row1_col8\" class=\"data row1 col8\" >0.0300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row2\" class=\"row_heading level0 row2\" >lda</th>\n",
              "      <td id=\"T_947db_row2_col0\" class=\"data row2 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_947db_row2_col1\" class=\"data row2 col1\" >0.9714</td>\n",
              "      <td id=\"T_947db_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
              "      <td id=\"T_947db_row2_col3\" class=\"data row2 col3\" >0.9714</td>\n",
              "      <td id=\"T_947db_row2_col4\" class=\"data row2 col4\" >0.9750</td>\n",
              "      <td id=\"T_947db_row2_col5\" class=\"data row2 col5\" >0.9713</td>\n",
              "      <td id=\"T_947db_row2_col6\" class=\"data row2 col6\" >0.9571</td>\n",
              "      <td id=\"T_947db_row2_col7\" class=\"data row2 col7\" >0.9590</td>\n",
              "      <td id=\"T_947db_row2_col8\" class=\"data row2 col8\" >0.0320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row3\" class=\"row_heading level0 row3\" >knn</th>\n",
              "      <td id=\"T_947db_row3_col0\" class=\"data row3 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_947db_row3_col1\" class=\"data row3 col1\" >0.9619</td>\n",
              "      <td id=\"T_947db_row3_col2\" class=\"data row3 col2\" >0.9850</td>\n",
              "      <td id=\"T_947db_row3_col3\" class=\"data row3 col3\" >0.9619</td>\n",
              "      <td id=\"T_947db_row3_col4\" class=\"data row3 col4\" >0.9667</td>\n",
              "      <td id=\"T_947db_row3_col5\" class=\"data row3 col5\" >0.9617</td>\n",
              "      <td id=\"T_947db_row3_col6\" class=\"data row3 col6\" >0.9429</td>\n",
              "      <td id=\"T_947db_row3_col7\" class=\"data row3 col7\" >0.9454</td>\n",
              "      <td id=\"T_947db_row3_col8\" class=\"data row3 col8\" >0.0520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row4\" class=\"row_heading level0 row4\" >et</th>\n",
              "      <td id=\"T_947db_row4_col0\" class=\"data row4 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_947db_row4_col1\" class=\"data row4 col1\" >0.9619</td>\n",
              "      <td id=\"T_947db_row4_col2\" class=\"data row4 col2\" >0.9959</td>\n",
              "      <td id=\"T_947db_row4_col3\" class=\"data row4 col3\" >0.9619</td>\n",
              "      <td id=\"T_947db_row4_col4\" class=\"data row4 col4\" >0.9667</td>\n",
              "      <td id=\"T_947db_row4_col5\" class=\"data row4 col5\" >0.9617</td>\n",
              "      <td id=\"T_947db_row4_col6\" class=\"data row4 col6\" >0.9429</td>\n",
              "      <td id=\"T_947db_row4_col7\" class=\"data row4 col7\" >0.9454</td>\n",
              "      <td id=\"T_947db_row4_col8\" class=\"data row4 col8\" >0.1720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row5\" class=\"row_heading level0 row5\" >lightgbm</th>\n",
              "      <td id=\"T_947db_row5_col0\" class=\"data row5 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_947db_row5_col1\" class=\"data row5 col1\" >0.9524</td>\n",
              "      <td id=\"T_947db_row5_col2\" class=\"data row5 col2\" >0.9912</td>\n",
              "      <td id=\"T_947db_row5_col3\" class=\"data row5 col3\" >0.9524</td>\n",
              "      <td id=\"T_947db_row5_col4\" class=\"data row5 col4\" >0.9560</td>\n",
              "      <td id=\"T_947db_row5_col5\" class=\"data row5 col5\" >0.9522</td>\n",
              "      <td id=\"T_947db_row5_col6\" class=\"data row5 col6\" >0.9286</td>\n",
              "      <td id=\"T_947db_row5_col7\" class=\"data row5 col7\" >0.9305</td>\n",
              "      <td id=\"T_947db_row5_col8\" class=\"data row5 col8\" >0.3200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row6\" class=\"row_heading level0 row6\" >catboost</th>\n",
              "      <td id=\"T_947db_row6_col0\" class=\"data row6 col0\" >CatBoost Classifier</td>\n",
              "      <td id=\"T_947db_row6_col1\" class=\"data row6 col1\" >0.9524</td>\n",
              "      <td id=\"T_947db_row6_col2\" class=\"data row6 col2\" >0.9946</td>\n",
              "      <td id=\"T_947db_row6_col3\" class=\"data row6 col3\" >0.9524</td>\n",
              "      <td id=\"T_947db_row6_col4\" class=\"data row6 col4\" >0.9560</td>\n",
              "      <td id=\"T_947db_row6_col5\" class=\"data row6 col5\" >0.9522</td>\n",
              "      <td id=\"T_947db_row6_col6\" class=\"data row6 col6\" >0.9286</td>\n",
              "      <td id=\"T_947db_row6_col7\" class=\"data row6 col7\" >0.9305</td>\n",
              "      <td id=\"T_947db_row6_col8\" class=\"data row6 col8\" >0.6640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row7\" class=\"row_heading level0 row7\" >nb</th>\n",
              "      <td id=\"T_947db_row7_col0\" class=\"data row7 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_947db_row7_col1\" class=\"data row7 col1\" >0.9429</td>\n",
              "      <td id=\"T_947db_row7_col2\" class=\"data row7 col2\" >0.9959</td>\n",
              "      <td id=\"T_947db_row7_col3\" class=\"data row7 col3\" >0.9429</td>\n",
              "      <td id=\"T_947db_row7_col4\" class=\"data row7 col4\" >0.9452</td>\n",
              "      <td id=\"T_947db_row7_col5\" class=\"data row7 col5\" >0.9428</td>\n",
              "      <td id=\"T_947db_row7_col6\" class=\"data row7 col6\" >0.9143</td>\n",
              "      <td id=\"T_947db_row7_col7\" class=\"data row7 col7\" >0.9156</td>\n",
              "      <td id=\"T_947db_row7_col8\" class=\"data row7 col8\" >0.0360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row8\" class=\"row_heading level0 row8\" >rf</th>\n",
              "      <td id=\"T_947db_row8_col0\" class=\"data row8 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_947db_row8_col1\" class=\"data row8 col1\" >0.9429</td>\n",
              "      <td id=\"T_947db_row8_col2\" class=\"data row8 col2\" >0.9959</td>\n",
              "      <td id=\"T_947db_row8_col3\" class=\"data row8 col3\" >0.9429</td>\n",
              "      <td id=\"T_947db_row8_col4\" class=\"data row8 col4\" >0.9495</td>\n",
              "      <td id=\"T_947db_row8_col5\" class=\"data row8 col5\" >0.9424</td>\n",
              "      <td id=\"T_947db_row8_col6\" class=\"data row8 col6\" >0.9143</td>\n",
              "      <td id=\"T_947db_row8_col7\" class=\"data row8 col7\" >0.9179</td>\n",
              "      <td id=\"T_947db_row8_col8\" class=\"data row8 col8\" >0.2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row9\" class=\"row_heading level0 row9\" >xgboost</th>\n",
              "      <td id=\"T_947db_row9_col0\" class=\"data row9 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_947db_row9_col1\" class=\"data row9 col1\" >0.9429</td>\n",
              "      <td id=\"T_947db_row9_col2\" class=\"data row9 col2\" >0.9810</td>\n",
              "      <td id=\"T_947db_row9_col3\" class=\"data row9 col3\" >0.9429</td>\n",
              "      <td id=\"T_947db_row9_col4\" class=\"data row9 col4\" >0.9476</td>\n",
              "      <td id=\"T_947db_row9_col5\" class=\"data row9 col5\" >0.9427</td>\n",
              "      <td id=\"T_947db_row9_col6\" class=\"data row9 col6\" >0.9143</td>\n",
              "      <td id=\"T_947db_row9_col7\" class=\"data row9 col7\" >0.9168</td>\n",
              "      <td id=\"T_947db_row9_col8\" class=\"data row9 col8\" >0.0660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row10\" class=\"row_heading level0 row10\" >dt</th>\n",
              "      <td id=\"T_947db_row10_col0\" class=\"data row10 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_947db_row10_col1\" class=\"data row10 col1\" >0.9333</td>\n",
              "      <td id=\"T_947db_row10_col2\" class=\"data row10 col2\" >0.9500</td>\n",
              "      <td id=\"T_947db_row10_col3\" class=\"data row10 col3\" >0.9333</td>\n",
              "      <td id=\"T_947db_row10_col4\" class=\"data row10 col4\" >0.9407</td>\n",
              "      <td id=\"T_947db_row10_col5\" class=\"data row10 col5\" >0.9327</td>\n",
              "      <td id=\"T_947db_row10_col6\" class=\"data row10 col6\" >0.9000</td>\n",
              "      <td id=\"T_947db_row10_col7\" class=\"data row10 col7\" >0.9042</td>\n",
              "      <td id=\"T_947db_row10_col8\" class=\"data row10 col8\" >0.1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row11\" class=\"row_heading level0 row11\" >ada</th>\n",
              "      <td id=\"T_947db_row11_col0\" class=\"data row11 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_947db_row11_col1\" class=\"data row11 col1\" >0.9238</td>\n",
              "      <td id=\"T_947db_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
              "      <td id=\"T_947db_row11_col3\" class=\"data row11 col3\" >0.9238</td>\n",
              "      <td id=\"T_947db_row11_col4\" class=\"data row11 col4\" >0.9347</td>\n",
              "      <td id=\"T_947db_row11_col5\" class=\"data row11 col5\" >0.9229</td>\n",
              "      <td id=\"T_947db_row11_col6\" class=\"data row11 col6\" >0.8857</td>\n",
              "      <td id=\"T_947db_row11_col7\" class=\"data row11 col7\" >0.8917</td>\n",
              "      <td id=\"T_947db_row11_col8\" class=\"data row11 col8\" >0.1840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row12\" class=\"row_heading level0 row12\" >gbc</th>\n",
              "      <td id=\"T_947db_row12_col0\" class=\"data row12 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_947db_row12_col1\" class=\"data row12 col1\" >0.9238</td>\n",
              "      <td id=\"T_947db_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_947db_row12_col3\" class=\"data row12 col3\" >0.9238</td>\n",
              "      <td id=\"T_947db_row12_col4\" class=\"data row12 col4\" >0.9300</td>\n",
              "      <td id=\"T_947db_row12_col5\" class=\"data row12 col5\" >0.9232</td>\n",
              "      <td id=\"T_947db_row12_col6\" class=\"data row12 col6\" >0.8857</td>\n",
              "      <td id=\"T_947db_row12_col7\" class=\"data row12 col7\" >0.8893</td>\n",
              "      <td id=\"T_947db_row12_col8\" class=\"data row12 col8\" >0.3720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row13\" class=\"row_heading level0 row13\" >ridge</th>\n",
              "      <td id=\"T_947db_row13_col0\" class=\"data row13 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_947db_row13_col1\" class=\"data row13 col1\" >0.8095</td>\n",
              "      <td id=\"T_947db_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_947db_row13_col3\" class=\"data row13 col3\" >0.8095</td>\n",
              "      <td id=\"T_947db_row13_col4\" class=\"data row13 col4\" >0.8205</td>\n",
              "      <td id=\"T_947db_row13_col5\" class=\"data row13 col5\" >0.8074</td>\n",
              "      <td id=\"T_947db_row13_col6\" class=\"data row13 col6\" >0.7143</td>\n",
              "      <td id=\"T_947db_row13_col7\" class=\"data row13 col7\" >0.7212</td>\n",
              "      <td id=\"T_947db_row13_col8\" class=\"data row13 col8\" >0.0300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
              "      <td id=\"T_947db_row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_947db_row14_col1\" class=\"data row14 col1\" >0.7048</td>\n",
              "      <td id=\"T_947db_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_947db_row14_col3\" class=\"data row14 col3\" >0.7048</td>\n",
              "      <td id=\"T_947db_row14_col4\" class=\"data row14 col4\" >0.6357</td>\n",
              "      <td id=\"T_947db_row14_col5\" class=\"data row14 col5\" >0.6182</td>\n",
              "      <td id=\"T_947db_row14_col6\" class=\"data row14 col6\" >0.5571</td>\n",
              "      <td id=\"T_947db_row14_col7\" class=\"data row14 col7\" >0.6445</td>\n",
              "      <td id=\"T_947db_row14_col8\" class=\"data row14 col8\" >0.0400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_947db_level0_row15\" class=\"row_heading level0 row15\" >dummy</th>\n",
              "      <td id=\"T_947db_row15_col0\" class=\"data row15 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_947db_row15_col1\" class=\"data row15 col1\" >0.3333</td>\n",
              "      <td id=\"T_947db_row15_col2\" class=\"data row15 col2\" >0.5000</td>\n",
              "      <td id=\"T_947db_row15_col3\" class=\"data row15 col3\" >0.3333</td>\n",
              "      <td id=\"T_947db_row15_col4\" class=\"data row15 col4\" >0.1111</td>\n",
              "      <td id=\"T_947db_row15_col5\" class=\"data row15 col5\" >0.1667</td>\n",
              "      <td id=\"T_947db_row15_col6\" class=\"data row15 col6\" >0.0000</td>\n",
              "      <td id=\"T_947db_row15_col7\" class=\"data row15 col7\" >0.0000</td>\n",
              "      <td id=\"T_947db_row15_col8\" class=\"data row15 col8\" >0.0300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/69 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d552ed22fa7d4bff9281e1db7186b5e7"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=123, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class=&#x27;auto&#x27;, n_jobs=None, penalty=&#x27;l2&#x27;,\n",
              "                   random_state=123, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class=&#x27;auto&#x27;, n_jobs=None, penalty=&#x27;l2&#x27;,\n",
              "                   random_state=123, solver=&#x27;lbfgs&#x27;, tol=0.0001, verbose=0,\n",
              "                   warm_start=False)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**2- Select Best Model**</p>"
      ],
      "metadata": {
        "id": "OAhMkMv1r-pB"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZfLqgVsuBXP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551,
          "referenced_widgets": [
            "b5060a53419c4af3b8e9e29f264382d1",
            "95214e15316349498e233119a96078c9",
            "7fceae272a634494b1abf17fa2ecc530",
            "f086b10a652d40559104c508c71b0548",
            "236ebdabe666429cb2d238266bffa5ab",
            "e25079bea25a452ca447696a91b230d2",
            "cfde94bcf49b4a22ae83795d5e206d3b",
            "3cb5942890ef456fb14f113f5395248d",
            "f0558c812cc14ea4b30dec2048ad516e",
            "1559bb6df224465f858dca4f1117609b",
            "7d9d7d0028714e85a78b0936ad9df162"
          ]
        },
        "outputId": "ed3d57dd-a5dc-4808-c11f-b0de45576932"
      },
      "source": [
        "best= compare_models(n_select = 2, sort= 'Accuracy')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0ea1e8e5d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_7e6e2 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_7e6e2_row0_col0, #T_7e6e2_row0_col2, #T_7e6e2_row1_col0, #T_7e6e2_row1_col2, #T_7e6e2_row2_col0, #T_7e6e2_row2_col2, #T_7e6e2_row3_col0, #T_7e6e2_row3_col2, #T_7e6e2_row4_col0, #T_7e6e2_row4_col1, #T_7e6e2_row4_col3, #T_7e6e2_row4_col4, #T_7e6e2_row4_col5, #T_7e6e2_row4_col6, #T_7e6e2_row4_col7, #T_7e6e2_row5_col0, #T_7e6e2_row5_col1, #T_7e6e2_row5_col2, #T_7e6e2_row5_col3, #T_7e6e2_row5_col4, #T_7e6e2_row5_col5, #T_7e6e2_row5_col6, #T_7e6e2_row5_col7, #T_7e6e2_row6_col0, #T_7e6e2_row6_col1, #T_7e6e2_row6_col3, #T_7e6e2_row6_col4, #T_7e6e2_row6_col5, #T_7e6e2_row6_col6, #T_7e6e2_row6_col7, #T_7e6e2_row7_col0, #T_7e6e2_row7_col1, #T_7e6e2_row7_col2, #T_7e6e2_row7_col3, #T_7e6e2_row7_col4, #T_7e6e2_row7_col5, #T_7e6e2_row7_col6, #T_7e6e2_row7_col7, #T_7e6e2_row8_col0, #T_7e6e2_row8_col1, #T_7e6e2_row8_col2, #T_7e6e2_row8_col3, #T_7e6e2_row8_col4, #T_7e6e2_row8_col5, #T_7e6e2_row8_col6, #T_7e6e2_row8_col7, #T_7e6e2_row9_col0, #T_7e6e2_row9_col1, #T_7e6e2_row9_col2, #T_7e6e2_row9_col3, #T_7e6e2_row9_col4, #T_7e6e2_row9_col5, #T_7e6e2_row9_col6, #T_7e6e2_row9_col7, #T_7e6e2_row10_col0, #T_7e6e2_row10_col1, #T_7e6e2_row10_col2, #T_7e6e2_row10_col3, #T_7e6e2_row10_col4, #T_7e6e2_row10_col5, #T_7e6e2_row10_col6, #T_7e6e2_row10_col7, #T_7e6e2_row11_col0, #T_7e6e2_row11_col1, #T_7e6e2_row11_col2, #T_7e6e2_row11_col3, #T_7e6e2_row11_col4, #T_7e6e2_row11_col5, #T_7e6e2_row11_col6, #T_7e6e2_row11_col7, #T_7e6e2_row12_col0, #T_7e6e2_row12_col1, #T_7e6e2_row12_col2, #T_7e6e2_row12_col3, #T_7e6e2_row12_col4, #T_7e6e2_row12_col5, #T_7e6e2_row12_col6, #T_7e6e2_row12_col7, #T_7e6e2_row13_col0, #T_7e6e2_row13_col1, #T_7e6e2_row13_col2, #T_7e6e2_row13_col3, #T_7e6e2_row13_col4, #T_7e6e2_row13_col5, #T_7e6e2_row13_col6, #T_7e6e2_row13_col7, #T_7e6e2_row14_col0, #T_7e6e2_row14_col1, #T_7e6e2_row14_col2, #T_7e6e2_row14_col3, #T_7e6e2_row14_col4, #T_7e6e2_row14_col5, #T_7e6e2_row14_col6, #T_7e6e2_row14_col7, #T_7e6e2_row15_col0, #T_7e6e2_row15_col1, #T_7e6e2_row15_col2, #T_7e6e2_row15_col3, #T_7e6e2_row15_col4, #T_7e6e2_row15_col5, #T_7e6e2_row15_col6, #T_7e6e2_row15_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_7e6e2_row0_col1, #T_7e6e2_row0_col3, #T_7e6e2_row0_col4, #T_7e6e2_row0_col5, #T_7e6e2_row0_col6, #T_7e6e2_row0_col7, #T_7e6e2_row1_col1, #T_7e6e2_row1_col3, #T_7e6e2_row1_col4, #T_7e6e2_row1_col5, #T_7e6e2_row1_col6, #T_7e6e2_row1_col7, #T_7e6e2_row2_col1, #T_7e6e2_row2_col3, #T_7e6e2_row2_col4, #T_7e6e2_row2_col5, #T_7e6e2_row2_col6, #T_7e6e2_row2_col7, #T_7e6e2_row3_col1, #T_7e6e2_row3_col3, #T_7e6e2_row3_col4, #T_7e6e2_row3_col5, #T_7e6e2_row3_col6, #T_7e6e2_row3_col7, #T_7e6e2_row4_col2, #T_7e6e2_row6_col2 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_7e6e2_row0_col8, #T_7e6e2_row1_col8, #T_7e6e2_row3_col8, #T_7e6e2_row4_col8, #T_7e6e2_row5_col8, #T_7e6e2_row6_col8, #T_7e6e2_row7_col8, #T_7e6e2_row8_col8, #T_7e6e2_row9_col8, #T_7e6e2_row10_col8, #T_7e6e2_row11_col8, #T_7e6e2_row12_col8, #T_7e6e2_row13_col8, #T_7e6e2_row14_col8, #T_7e6e2_row15_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_7e6e2_row2_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_7e6e2\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_7e6e2_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_7e6e2_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_7e6e2_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_7e6e2_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_7e6e2_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_7e6e2_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_7e6e2_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_7e6e2_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_7e6e2_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row0\" class=\"row_heading level0 row0\" >lr</th>\n",
              "      <td id=\"T_7e6e2_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_7e6e2_row0_col1\" class=\"data row0 col1\" >0.9718</td>\n",
              "      <td id=\"T_7e6e2_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row0_col3\" class=\"data row0 col3\" >0.9718</td>\n",
              "      <td id=\"T_7e6e2_row0_col4\" class=\"data row0 col4\" >0.9780</td>\n",
              "      <td id=\"T_7e6e2_row0_col5\" class=\"data row0 col5\" >0.9712</td>\n",
              "      <td id=\"T_7e6e2_row0_col6\" class=\"data row0 col6\" >0.9573</td>\n",
              "      <td id=\"T_7e6e2_row0_col7\" class=\"data row0 col7\" >0.9609</td>\n",
              "      <td id=\"T_7e6e2_row0_col8\" class=\"data row0 col8\" >0.0430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
              "      <td id=\"T_7e6e2_row1_col0\" class=\"data row1 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_7e6e2_row1_col1\" class=\"data row1 col1\" >0.9718</td>\n",
              "      <td id=\"T_7e6e2_row1_col2\" class=\"data row1 col2\" >0.9830</td>\n",
              "      <td id=\"T_7e6e2_row1_col3\" class=\"data row1 col3\" >0.9718</td>\n",
              "      <td id=\"T_7e6e2_row1_col4\" class=\"data row1 col4\" >0.9780</td>\n",
              "      <td id=\"T_7e6e2_row1_col5\" class=\"data row1 col5\" >0.9712</td>\n",
              "      <td id=\"T_7e6e2_row1_col6\" class=\"data row1 col6\" >0.9573</td>\n",
              "      <td id=\"T_7e6e2_row1_col7\" class=\"data row1 col7\" >0.9609</td>\n",
              "      <td id=\"T_7e6e2_row1_col8\" class=\"data row1 col8\" >0.0460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row2\" class=\"row_heading level0 row2\" >qda</th>\n",
              "      <td id=\"T_7e6e2_row2_col0\" class=\"data row2 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_7e6e2_row2_col1\" class=\"data row2 col1\" >0.9718</td>\n",
              "      <td id=\"T_7e6e2_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row2_col3\" class=\"data row2 col3\" >0.9718</td>\n",
              "      <td id=\"T_7e6e2_row2_col4\" class=\"data row2 col4\" >0.9780</td>\n",
              "      <td id=\"T_7e6e2_row2_col5\" class=\"data row2 col5\" >0.9712</td>\n",
              "      <td id=\"T_7e6e2_row2_col6\" class=\"data row2 col6\" >0.9573</td>\n",
              "      <td id=\"T_7e6e2_row2_col7\" class=\"data row2 col7\" >0.9609</td>\n",
              "      <td id=\"T_7e6e2_row2_col8\" class=\"data row2 col8\" >0.0270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row3\" class=\"row_heading level0 row3\" >lda</th>\n",
              "      <td id=\"T_7e6e2_row3_col0\" class=\"data row3 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_7e6e2_row3_col1\" class=\"data row3 col1\" >0.9718</td>\n",
              "      <td id=\"T_7e6e2_row3_col2\" class=\"data row3 col2\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row3_col3\" class=\"data row3 col3\" >0.9718</td>\n",
              "      <td id=\"T_7e6e2_row3_col4\" class=\"data row3 col4\" >0.9780</td>\n",
              "      <td id=\"T_7e6e2_row3_col5\" class=\"data row3 col5\" >0.9712</td>\n",
              "      <td id=\"T_7e6e2_row3_col6\" class=\"data row3 col6\" >0.9573</td>\n",
              "      <td id=\"T_7e6e2_row3_col7\" class=\"data row3 col7\" >0.9609</td>\n",
              "      <td id=\"T_7e6e2_row3_col8\" class=\"data row3 col8\" >0.0290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
              "      <td id=\"T_7e6e2_row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_7e6e2_row4_col1\" class=\"data row4 col1\" >0.9536</td>\n",
              "      <td id=\"T_7e6e2_row4_col2\" class=\"data row4 col2\" >0.9935</td>\n",
              "      <td id=\"T_7e6e2_row4_col3\" class=\"data row4 col3\" >0.9536</td>\n",
              "      <td id=\"T_7e6e2_row4_col4\" class=\"data row4 col4\" >0.9634</td>\n",
              "      <td id=\"T_7e6e2_row4_col5\" class=\"data row4 col5\" >0.9528</td>\n",
              "      <td id=\"T_7e6e2_row4_col6\" class=\"data row4 col6\" >0.9298</td>\n",
              "      <td id=\"T_7e6e2_row4_col7\" class=\"data row4 col7\" >0.9356</td>\n",
              "      <td id=\"T_7e6e2_row4_col8\" class=\"data row4 col8\" >0.1870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row5\" class=\"row_heading level0 row5\" >nb</th>\n",
              "      <td id=\"T_7e6e2_row5_col0\" class=\"data row5 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_7e6e2_row5_col1\" class=\"data row5 col1\" >0.9445</td>\n",
              "      <td id=\"T_7e6e2_row5_col2\" class=\"data row5 col2\" >0.9868</td>\n",
              "      <td id=\"T_7e6e2_row5_col3\" class=\"data row5 col3\" >0.9445</td>\n",
              "      <td id=\"T_7e6e2_row5_col4\" class=\"data row5 col4\" >0.9525</td>\n",
              "      <td id=\"T_7e6e2_row5_col5\" class=\"data row5 col5\" >0.9438</td>\n",
              "      <td id=\"T_7e6e2_row5_col6\" class=\"data row5 col6\" >0.9161</td>\n",
              "      <td id=\"T_7e6e2_row5_col7\" class=\"data row5 col7\" >0.9207</td>\n",
              "      <td id=\"T_7e6e2_row5_col8\" class=\"data row5 col8\" >0.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row6\" class=\"row_heading level0 row6\" >et</th>\n",
              "      <td id=\"T_7e6e2_row6_col0\" class=\"data row6 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_7e6e2_row6_col1\" class=\"data row6 col1\" >0.9445</td>\n",
              "      <td id=\"T_7e6e2_row6_col2\" class=\"data row6 col2\" >0.9935</td>\n",
              "      <td id=\"T_7e6e2_row6_col3\" class=\"data row6 col3\" >0.9445</td>\n",
              "      <td id=\"T_7e6e2_row6_col4\" class=\"data row6 col4\" >0.9586</td>\n",
              "      <td id=\"T_7e6e2_row6_col5\" class=\"data row6 col5\" >0.9426</td>\n",
              "      <td id=\"T_7e6e2_row6_col6\" class=\"data row6 col6\" >0.9161</td>\n",
              "      <td id=\"T_7e6e2_row6_col7\" class=\"data row6 col7\" >0.9246</td>\n",
              "      <td id=\"T_7e6e2_row6_col8\" class=\"data row6 col8\" >0.2120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row7\" class=\"row_heading level0 row7\" >catboost</th>\n",
              "      <td id=\"T_7e6e2_row7_col0\" class=\"data row7 col0\" >CatBoost Classifier</td>\n",
              "      <td id=\"T_7e6e2_row7_col1\" class=\"data row7 col1\" >0.9445</td>\n",
              "      <td id=\"T_7e6e2_row7_col2\" class=\"data row7 col2\" >0.9922</td>\n",
              "      <td id=\"T_7e6e2_row7_col3\" class=\"data row7 col3\" >0.9445</td>\n",
              "      <td id=\"T_7e6e2_row7_col4\" class=\"data row7 col4\" >0.9586</td>\n",
              "      <td id=\"T_7e6e2_row7_col5\" class=\"data row7 col5\" >0.9426</td>\n",
              "      <td id=\"T_7e6e2_row7_col6\" class=\"data row7 col6\" >0.9161</td>\n",
              "      <td id=\"T_7e6e2_row7_col7\" class=\"data row7 col7\" >0.9246</td>\n",
              "      <td id=\"T_7e6e2_row7_col8\" class=\"data row7 col8\" >0.9190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row8\" class=\"row_heading level0 row8\" >gbc</th>\n",
              "      <td id=\"T_7e6e2_row8_col0\" class=\"data row8 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_7e6e2_row8_col1\" class=\"data row8 col1\" >0.9355</td>\n",
              "      <td id=\"T_7e6e2_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row8_col3\" class=\"data row8 col3\" >0.9355</td>\n",
              "      <td id=\"T_7e6e2_row8_col4\" class=\"data row8 col4\" >0.9416</td>\n",
              "      <td id=\"T_7e6e2_row8_col5\" class=\"data row8 col5\" >0.9325</td>\n",
              "      <td id=\"T_7e6e2_row8_col6\" class=\"data row8 col6\" >0.9023</td>\n",
              "      <td id=\"T_7e6e2_row8_col7\" class=\"data row8 col7\" >0.9083</td>\n",
              "      <td id=\"T_7e6e2_row8_col8\" class=\"data row8 col8\" >0.2650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row9\" class=\"row_heading level0 row9\" >dt</th>\n",
              "      <td id=\"T_7e6e2_row9_col0\" class=\"data row9 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_7e6e2_row9_col1\" class=\"data row9 col1\" >0.9264</td>\n",
              "      <td id=\"T_7e6e2_row9_col2\" class=\"data row9 col2\" >0.9429</td>\n",
              "      <td id=\"T_7e6e2_row9_col3\" class=\"data row9 col3\" >0.9264</td>\n",
              "      <td id=\"T_7e6e2_row9_col4\" class=\"data row9 col4\" >0.9502</td>\n",
              "      <td id=\"T_7e6e2_row9_col5\" class=\"data row9 col5\" >0.9201</td>\n",
              "      <td id=\"T_7e6e2_row9_col6\" class=\"data row9 col6\" >0.8886</td>\n",
              "      <td id=\"T_7e6e2_row9_col7\" class=\"data row9 col7\" >0.9040</td>\n",
              "      <td id=\"T_7e6e2_row9_col8\" class=\"data row9 col8\" >0.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row10\" class=\"row_heading level0 row10\" >rf</th>\n",
              "      <td id=\"T_7e6e2_row10_col0\" class=\"data row10 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_7e6e2_row10_col1\" class=\"data row10 col1\" >0.9264</td>\n",
              "      <td id=\"T_7e6e2_row10_col2\" class=\"data row10 col2\" >0.9909</td>\n",
              "      <td id=\"T_7e6e2_row10_col3\" class=\"data row10 col3\" >0.9264</td>\n",
              "      <td id=\"T_7e6e2_row10_col4\" class=\"data row10 col4\" >0.9343</td>\n",
              "      <td id=\"T_7e6e2_row10_col5\" class=\"data row10 col5\" >0.9232</td>\n",
              "      <td id=\"T_7e6e2_row10_col6\" class=\"data row10 col6\" >0.8886</td>\n",
              "      <td id=\"T_7e6e2_row10_col7\" class=\"data row10 col7\" >0.8956</td>\n",
              "      <td id=\"T_7e6e2_row10_col8\" class=\"data row10 col8\" >0.2200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row11\" class=\"row_heading level0 row11\" >xgboost</th>\n",
              "      <td id=\"T_7e6e2_row11_col0\" class=\"data row11 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_7e6e2_row11_col1\" class=\"data row11 col1\" >0.9255</td>\n",
              "      <td id=\"T_7e6e2_row11_col2\" class=\"data row11 col2\" >0.9710</td>\n",
              "      <td id=\"T_7e6e2_row11_col3\" class=\"data row11 col3\" >0.9255</td>\n",
              "      <td id=\"T_7e6e2_row11_col4\" class=\"data row11 col4\" >0.9360</td>\n",
              "      <td id=\"T_7e6e2_row11_col5\" class=\"data row11 col5\" >0.9239</td>\n",
              "      <td id=\"T_7e6e2_row11_col6\" class=\"data row11 col6\" >0.8870</td>\n",
              "      <td id=\"T_7e6e2_row11_col7\" class=\"data row11 col7\" >0.8937</td>\n",
              "      <td id=\"T_7e6e2_row11_col8\" class=\"data row11 col8\" >0.1030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row12\" class=\"row_heading level0 row12\" >ada</th>\n",
              "      <td id=\"T_7e6e2_row12_col0\" class=\"data row12 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_7e6e2_row12_col1\" class=\"data row12 col1\" >0.9155</td>\n",
              "      <td id=\"T_7e6e2_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row12_col3\" class=\"data row12 col3\" >0.9155</td>\n",
              "      <td id=\"T_7e6e2_row12_col4\" class=\"data row12 col4\" >0.9401</td>\n",
              "      <td id=\"T_7e6e2_row12_col5\" class=\"data row12 col5\" >0.9097</td>\n",
              "      <td id=\"T_7e6e2_row12_col6\" class=\"data row12 col6\" >0.8720</td>\n",
              "      <td id=\"T_7e6e2_row12_col7\" class=\"data row12 col7\" >0.8873</td>\n",
              "      <td id=\"T_7e6e2_row12_col8\" class=\"data row12 col8\" >0.1060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row13\" class=\"row_heading level0 row13\" >ridge</th>\n",
              "      <td id=\"T_7e6e2_row13_col0\" class=\"data row13 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_7e6e2_row13_col1\" class=\"data row13 col1\" >0.8227</td>\n",
              "      <td id=\"T_7e6e2_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row13_col3\" class=\"data row13 col3\" >0.8227</td>\n",
              "      <td id=\"T_7e6e2_row13_col4\" class=\"data row13 col4\" >0.8437</td>\n",
              "      <td id=\"T_7e6e2_row13_col5\" class=\"data row13 col5\" >0.8186</td>\n",
              "      <td id=\"T_7e6e2_row13_col6\" class=\"data row13 col6\" >0.7320</td>\n",
              "      <td id=\"T_7e6e2_row13_col7\" class=\"data row13 col7\" >0.7454</td>\n",
              "      <td id=\"T_7e6e2_row13_col8\" class=\"data row13 col8\" >0.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row14\" class=\"row_heading level0 row14\" >svm</th>\n",
              "      <td id=\"T_7e6e2_row14_col0\" class=\"data row14 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_7e6e2_row14_col1\" class=\"data row14 col1\" >0.7618</td>\n",
              "      <td id=\"T_7e6e2_row14_col2\" class=\"data row14 col2\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row14_col3\" class=\"data row14 col3\" >0.7618</td>\n",
              "      <td id=\"T_7e6e2_row14_col4\" class=\"data row14 col4\" >0.6655</td>\n",
              "      <td id=\"T_7e6e2_row14_col5\" class=\"data row14 col5\" >0.6888</td>\n",
              "      <td id=\"T_7e6e2_row14_col6\" class=\"data row14 col6\" >0.6333</td>\n",
              "      <td id=\"T_7e6e2_row14_col7\" class=\"data row14 col7\" >0.7048</td>\n",
              "      <td id=\"T_7e6e2_row14_col8\" class=\"data row14 col8\" >0.0660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7e6e2_level0_row15\" class=\"row_heading level0 row15\" >dummy</th>\n",
              "      <td id=\"T_7e6e2_row15_col0\" class=\"data row15 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_7e6e2_row15_col1\" class=\"data row15 col1\" >0.2864</td>\n",
              "      <td id=\"T_7e6e2_row15_col2\" class=\"data row15 col2\" >0.5000</td>\n",
              "      <td id=\"T_7e6e2_row15_col3\" class=\"data row15 col3\" >0.2864</td>\n",
              "      <td id=\"T_7e6e2_row15_col4\" class=\"data row15 col4\" >0.0822</td>\n",
              "      <td id=\"T_7e6e2_row15_col5\" class=\"data row15 col5\" >0.1277</td>\n",
              "      <td id=\"T_7e6e2_row15_col6\" class=\"data row15 col6\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row15_col7\" class=\"data row15 col7\" >0.0000</td>\n",
              "      <td id=\"T_7e6e2_row15_col8\" class=\"data row15 col8\" >0.0690</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/70 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5060a53419c4af3b8e9e29f264382d1"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWV1Yz6Vx-oH"
      },
      "source": [
        "compare_model_result = pull()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CyvW0n6yt2U"
      },
      "source": [
        "since the RandomForestregressor was evaluated to have a comparatively better Accuracy, let us build the model using RandomForestregressor"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**3- Create a model**</p>\n"
      ],
      "metadata": {
        "id": "3EUl9Lcn3R7H"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTTkN03zAsnv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "5eddcf2ca6694774a0d590220dc6cf02",
            "1905609a80544e579941712f6420761e",
            "cc8d80d570a541e6ad0211721f5a5c3c",
            "560a9df2bfba4176819bfce61d475cae",
            "60f9dd8ba7e84cccb6dd9f010e9a0059",
            "c7ff12f5968a4feebdffd07dc23cd063",
            "f739af95a6e040ea9f24d9cdd9a43b37",
            "2e89b948f0d34ac48d0989b35f17ec20",
            "8c3d830d01ad4e6584ac9222fcc9cd45",
            "0a9afc85966a474e8badf65716db37cf",
            "e17ef2b06265473d8f7e6a3474ae5a48"
          ]
        },
        "outputId": "877885d9-705a-48aa-94c7-fb17277bc081"
      },
      "source": [
        "lr = create_model('lr')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0ea28dc8d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_84063_row10_col0, #T_84063_row10_col1, #T_84063_row10_col2, #T_84063_row10_col3, #T_84063_row10_col4, #T_84063_row10_col5, #T_84063_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_84063\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_84063_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_84063_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_84063_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_84063_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_84063_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_84063_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_84063_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_84063_row0_col0\" class=\"data row0 col0\" >0.9091</td>\n",
              "      <td id=\"T_84063_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row0_col2\" class=\"data row0 col2\" >0.9091</td>\n",
              "      <td id=\"T_84063_row0_col3\" class=\"data row0 col3\" >0.9273</td>\n",
              "      <td id=\"T_84063_row0_col4\" class=\"data row0 col4\" >0.9076</td>\n",
              "      <td id=\"T_84063_row0_col5\" class=\"data row0 col5\" >0.8625</td>\n",
              "      <td id=\"T_84063_row0_col6\" class=\"data row0 col6\" >0.8735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_84063_row1_col0\" class=\"data row1 col0\" >1.0000</td>\n",
              "      <td id=\"T_84063_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row1_col2\" class=\"data row1 col2\" >1.0000</td>\n",
              "      <td id=\"T_84063_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "      <td id=\"T_84063_row1_col4\" class=\"data row1 col4\" >1.0000</td>\n",
              "      <td id=\"T_84063_row1_col5\" class=\"data row1 col5\" >1.0000</td>\n",
              "      <td id=\"T_84063_row1_col6\" class=\"data row1 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_84063_row2_col0\" class=\"data row2 col0\" >0.9091</td>\n",
              "      <td id=\"T_84063_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row2_col2\" class=\"data row2 col2\" >0.9091</td>\n",
              "      <td id=\"T_84063_row2_col3\" class=\"data row2 col3\" >0.9273</td>\n",
              "      <td id=\"T_84063_row2_col4\" class=\"data row2 col4\" >0.9076</td>\n",
              "      <td id=\"T_84063_row2_col5\" class=\"data row2 col5\" >0.8625</td>\n",
              "      <td id=\"T_84063_row2_col6\" class=\"data row2 col6\" >0.8735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_84063_row3_col0\" class=\"data row3 col0\" >1.0000</td>\n",
              "      <td id=\"T_84063_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row3_col2\" class=\"data row3 col2\" >1.0000</td>\n",
              "      <td id=\"T_84063_row3_col3\" class=\"data row3 col3\" >1.0000</td>\n",
              "      <td id=\"T_84063_row3_col4\" class=\"data row3 col4\" >1.0000</td>\n",
              "      <td id=\"T_84063_row3_col5\" class=\"data row3 col5\" >1.0000</td>\n",
              "      <td id=\"T_84063_row3_col6\" class=\"data row3 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_84063_row4_col0\" class=\"data row4 col0\" >1.0000</td>\n",
              "      <td id=\"T_84063_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row4_col2\" class=\"data row4 col2\" >1.0000</td>\n",
              "      <td id=\"T_84063_row4_col3\" class=\"data row4 col3\" >1.0000</td>\n",
              "      <td id=\"T_84063_row4_col4\" class=\"data row4 col4\" >1.0000</td>\n",
              "      <td id=\"T_84063_row4_col5\" class=\"data row4 col5\" >1.0000</td>\n",
              "      <td id=\"T_84063_row4_col6\" class=\"data row4 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_84063_row5_col0\" class=\"data row5 col0\" >0.9000</td>\n",
              "      <td id=\"T_84063_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row5_col2\" class=\"data row5 col2\" >0.9000</td>\n",
              "      <td id=\"T_84063_row5_col3\" class=\"data row5 col3\" >0.9250</td>\n",
              "      <td id=\"T_84063_row5_col4\" class=\"data row5 col4\" >0.8971</td>\n",
              "      <td id=\"T_84063_row5_col5\" class=\"data row5 col5\" >0.8485</td>\n",
              "      <td id=\"T_84063_row5_col6\" class=\"data row5 col6\" >0.8616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_84063_row6_col0\" class=\"data row6 col0\" >1.0000</td>\n",
              "      <td id=\"T_84063_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row6_col2\" class=\"data row6 col2\" >1.0000</td>\n",
              "      <td id=\"T_84063_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
              "      <td id=\"T_84063_row6_col4\" class=\"data row6 col4\" >1.0000</td>\n",
              "      <td id=\"T_84063_row6_col5\" class=\"data row6 col5\" >1.0000</td>\n",
              "      <td id=\"T_84063_row6_col6\" class=\"data row6 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_84063_row7_col0\" class=\"data row7 col0\" >1.0000</td>\n",
              "      <td id=\"T_84063_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row7_col2\" class=\"data row7 col2\" >1.0000</td>\n",
              "      <td id=\"T_84063_row7_col3\" class=\"data row7 col3\" >1.0000</td>\n",
              "      <td id=\"T_84063_row7_col4\" class=\"data row7 col4\" >1.0000</td>\n",
              "      <td id=\"T_84063_row7_col5\" class=\"data row7 col5\" >1.0000</td>\n",
              "      <td id=\"T_84063_row7_col6\" class=\"data row7 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_84063_row8_col0\" class=\"data row8 col0\" >1.0000</td>\n",
              "      <td id=\"T_84063_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row8_col2\" class=\"data row8 col2\" >1.0000</td>\n",
              "      <td id=\"T_84063_row8_col3\" class=\"data row8 col3\" >1.0000</td>\n",
              "      <td id=\"T_84063_row8_col4\" class=\"data row8 col4\" >1.0000</td>\n",
              "      <td id=\"T_84063_row8_col5\" class=\"data row8 col5\" >1.0000</td>\n",
              "      <td id=\"T_84063_row8_col6\" class=\"data row8 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_84063_row9_col0\" class=\"data row9 col0\" >1.0000</td>\n",
              "      <td id=\"T_84063_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row9_col2\" class=\"data row9 col2\" >1.0000</td>\n",
              "      <td id=\"T_84063_row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
              "      <td id=\"T_84063_row9_col4\" class=\"data row9 col4\" >1.0000</td>\n",
              "      <td id=\"T_84063_row9_col5\" class=\"data row9 col5\" >1.0000</td>\n",
              "      <td id=\"T_84063_row9_col6\" class=\"data row9 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_84063_row10_col0\" class=\"data row10 col0\" >0.9718</td>\n",
              "      <td id=\"T_84063_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row10_col2\" class=\"data row10 col2\" >0.9718</td>\n",
              "      <td id=\"T_84063_row10_col3\" class=\"data row10 col3\" >0.9780</td>\n",
              "      <td id=\"T_84063_row10_col4\" class=\"data row10 col4\" >0.9712</td>\n",
              "      <td id=\"T_84063_row10_col5\" class=\"data row10 col5\" >0.9573</td>\n",
              "      <td id=\"T_84063_row10_col6\" class=\"data row10 col6\" >0.9609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_84063_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_84063_row11_col0\" class=\"data row11 col0\" >0.0431</td>\n",
              "      <td id=\"T_84063_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_84063_row11_col2\" class=\"data row11 col2\" >0.0431</td>\n",
              "      <td id=\"T_84063_row11_col3\" class=\"data row11 col3\" >0.0337</td>\n",
              "      <td id=\"T_84063_row11_col4\" class=\"data row11 col4\" >0.0440</td>\n",
              "      <td id=\"T_84063_row11_col5\" class=\"data row11 col5\" >0.0653</td>\n",
              "      <td id=\"T_84063_row11_col6\" class=\"data row11 col6\" >0.0599</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5eddcf2ca6694774a0d590220dc6cf02"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**4- Fune Tune models**</p>\n"
      ],
      "metadata": {
        "id": "1ETq9k5vsKKp"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WvIuHIdnl3LS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_lr = tune_model(lr)"
      ],
      "metadata": {
        "id": "BWqkQTC69Ps6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512,
          "referenced_widgets": [
            "bfa59bdea60e416b8afd543fb54376a0",
            "0cc2080657bc42f6802ade5eebc54715",
            "96f2f713b5ae4494b1a181743003b9de",
            "4e96dbbe73c54a6f86af1104c9c51195",
            "530ca4060154410891d7c26f5d6c919a",
            "e240c2a0817e4a10b8220a1fbbe46cdc",
            "581d880bbddd4c048db9c0b7d3dc3ecc",
            "f4e3267e86f04ffa9dacdf35c0ba0335",
            "0a7241334c314ecd9b66fd1620b7587e",
            "a197de60da7b4988894b6e282f63fbc1",
            "1b078964b6b94be4b12476a5eba44725"
          ]
        },
        "outputId": "5d50e859-27c2-4717-f912-b6944612e92e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0ea1634f90>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_bad79_row10_col0, #T_bad79_row10_col1, #T_bad79_row10_col2, #T_bad79_row10_col3, #T_bad79_row10_col4, #T_bad79_row10_col5, #T_bad79_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_bad79\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_bad79_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_bad79_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_bad79_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_bad79_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_bad79_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_bad79_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_bad79_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_bad79_row0_col0\" class=\"data row0 col0\" >0.9091</td>\n",
              "      <td id=\"T_bad79_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row0_col2\" class=\"data row0 col2\" >0.9091</td>\n",
              "      <td id=\"T_bad79_row0_col3\" class=\"data row0 col3\" >0.9273</td>\n",
              "      <td id=\"T_bad79_row0_col4\" class=\"data row0 col4\" >0.9076</td>\n",
              "      <td id=\"T_bad79_row0_col5\" class=\"data row0 col5\" >0.8625</td>\n",
              "      <td id=\"T_bad79_row0_col6\" class=\"data row0 col6\" >0.8735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_bad79_row1_col0\" class=\"data row1 col0\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row1_col2\" class=\"data row1 col2\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row1_col4\" class=\"data row1 col4\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row1_col5\" class=\"data row1 col5\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row1_col6\" class=\"data row1 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_bad79_row2_col0\" class=\"data row2 col0\" >0.9091</td>\n",
              "      <td id=\"T_bad79_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row2_col2\" class=\"data row2 col2\" >0.9091</td>\n",
              "      <td id=\"T_bad79_row2_col3\" class=\"data row2 col3\" >0.9273</td>\n",
              "      <td id=\"T_bad79_row2_col4\" class=\"data row2 col4\" >0.9076</td>\n",
              "      <td id=\"T_bad79_row2_col5\" class=\"data row2 col5\" >0.8625</td>\n",
              "      <td id=\"T_bad79_row2_col6\" class=\"data row2 col6\" >0.8735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_bad79_row3_col0\" class=\"data row3 col0\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row3_col2\" class=\"data row3 col2\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row3_col3\" class=\"data row3 col3\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row3_col4\" class=\"data row3 col4\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row3_col5\" class=\"data row3 col5\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row3_col6\" class=\"data row3 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_bad79_row4_col0\" class=\"data row4 col0\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row4_col2\" class=\"data row4 col2\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row4_col3\" class=\"data row4 col3\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row4_col4\" class=\"data row4 col4\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row4_col5\" class=\"data row4 col5\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row4_col6\" class=\"data row4 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_bad79_row5_col0\" class=\"data row5 col0\" >0.9000</td>\n",
              "      <td id=\"T_bad79_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row5_col2\" class=\"data row5 col2\" >0.9000</td>\n",
              "      <td id=\"T_bad79_row5_col3\" class=\"data row5 col3\" >0.9250</td>\n",
              "      <td id=\"T_bad79_row5_col4\" class=\"data row5 col4\" >0.8971</td>\n",
              "      <td id=\"T_bad79_row5_col5\" class=\"data row5 col5\" >0.8485</td>\n",
              "      <td id=\"T_bad79_row5_col6\" class=\"data row5 col6\" >0.8616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_bad79_row6_col0\" class=\"data row6 col0\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row6_col2\" class=\"data row6 col2\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row6_col4\" class=\"data row6 col4\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row6_col5\" class=\"data row6 col5\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row6_col6\" class=\"data row6 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_bad79_row7_col0\" class=\"data row7 col0\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row7_col2\" class=\"data row7 col2\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row7_col3\" class=\"data row7 col3\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row7_col4\" class=\"data row7 col4\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row7_col5\" class=\"data row7 col5\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row7_col6\" class=\"data row7 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_bad79_row8_col0\" class=\"data row8 col0\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row8_col2\" class=\"data row8 col2\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row8_col3\" class=\"data row8 col3\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row8_col4\" class=\"data row8 col4\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row8_col5\" class=\"data row8 col5\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row8_col6\" class=\"data row8 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_bad79_row9_col0\" class=\"data row9 col0\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row9_col2\" class=\"data row9 col2\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row9_col4\" class=\"data row9 col4\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row9_col5\" class=\"data row9 col5\" >1.0000</td>\n",
              "      <td id=\"T_bad79_row9_col6\" class=\"data row9 col6\" >1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_bad79_row10_col0\" class=\"data row10 col0\" >0.9718</td>\n",
              "      <td id=\"T_bad79_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row10_col2\" class=\"data row10 col2\" >0.9718</td>\n",
              "      <td id=\"T_bad79_row10_col3\" class=\"data row10 col3\" >0.9780</td>\n",
              "      <td id=\"T_bad79_row10_col4\" class=\"data row10 col4\" >0.9712</td>\n",
              "      <td id=\"T_bad79_row10_col5\" class=\"data row10 col5\" >0.9573</td>\n",
              "      <td id=\"T_bad79_row10_col6\" class=\"data row10 col6\" >0.9609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_bad79_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_bad79_row11_col0\" class=\"data row11 col0\" >0.0431</td>\n",
              "      <td id=\"T_bad79_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_bad79_row11_col2\" class=\"data row11 col2\" >0.0431</td>\n",
              "      <td id=\"T_bad79_row11_col3\" class=\"data row11 col3\" >0.0337</td>\n",
              "      <td id=\"T_bad79_row11_col4\" class=\"data row11 col4\" >0.0440</td>\n",
              "      <td id=\"T_bad79_row11_col5\" class=\"data row11 col5\" >0.0653</td>\n",
              "      <td id=\"T_bad79_row11_col6\" class=\"data row11 col6\" >0.0599</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bfa59bdea60e416b8afd543fb54376a0"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**5- Plot a Model**</p>\n"
      ],
      "metadata": {
        "id": "vUp7JD9d9x7_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(lr)"
      ],
      "metadata": {
        "id": "Ht_cNwfy4vLD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620,
          "referenced_widgets": [
            "f98d15710a314fd488fe926f7eff6c42",
            "a58f5466f5fa4765b39b0b802a41fb0c",
            "e4c02345ba44410f95c506bf17eb5198",
            "e35ee408320f40018d56d2971883f2d4",
            "9aede8dbf11a4c62a24c09a71d4a85aa",
            "2d7e559b3b4c4d96b415c2e84930f551",
            "ab91a7e928d149d69eb90b6a86bedec8"
          ]
        },
        "outputId": "28e73334-d841-467f-d940-dac40db6ffd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f98d15710a314fd488fe926f7eff6c42"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**6- Predict on new Data**</p>"
      ],
      "metadata": {
        "id": "NAUhIb4K574X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions =predict_model(lr,data=data_unseen);\n"
      ],
      "metadata": {
        "id": "0JpC6p2I6Rnr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "6c17d08a-34d9-4373-ae30-14549cdd4326"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0ea1541310>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_1eff4\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_1eff4_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_1eff4_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_1eff4_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_1eff4_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_1eff4_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_1eff4_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_1eff4_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_1eff4_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_1eff4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_1eff4_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_1eff4_row0_col1\" class=\"data row0 col1\" >1.0000</td>\n",
              "      <td id=\"T_1eff4_row0_col2\" class=\"data row0 col2\" >0</td>\n",
              "      <td id=\"T_1eff4_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
              "      <td id=\"T_1eff4_row0_col4\" class=\"data row0 col4\" >1.0000</td>\n",
              "      <td id=\"T_1eff4_row0_col5\" class=\"data row0 col5\" >1.0000</td>\n",
              "      <td id=\"T_1eff4_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
              "      <td id=\"T_1eff4_row0_col7\" class=\"data row0 col7\" >0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fHWeqFg8MMBo",
        "outputId": "9f5e3288-5ae7-4c62-eb76-9cfc1601f078"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width         species  \\\n",
              "0           5.8          2.7           5.1          1.9  Iris-virginica   \n",
              "1           6.8          3.2           5.9          2.3  Iris-virginica   \n",
              "2           6.7          3.3           5.7          2.5  Iris-virginica   \n",
              "3           6.7          3.0           5.2          2.3  Iris-virginica   \n",
              "4           6.3          2.5           5.0          1.9  Iris-virginica   \n",
              "\n",
              "  prediction_label  prediction_score  \n",
              "0   Iris-virginica            0.8141  \n",
              "1   Iris-virginica            0.9827  \n",
              "2   Iris-virginica            0.9817  \n",
              "3   Iris-virginica            0.9104  \n",
              "4   Iris-virginica            0.7621  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ec19464-4be6-4607-9d51-2853b0524661\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "      <th>prediction_label</th>\n",
              "      <th>prediction_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.8141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.9827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.9817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.9104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.7621</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ec19464-4be6-4607-9d51-2853b0524661')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1ec19464-4be6-4607-9d51-2853b0524661 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1ec19464-4be6-4607-9d51-2853b0524661');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-486df547-d93d-4e3a-b64c-fcc059d4424b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-486df547-d93d-4e3a-b64c-fcc059d4424b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-486df547-d93d-4e3a-b64c-fcc059d4424b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "predictions",
              "summary": "{\n  \"name\": \"predictions\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5.800000190734863,\n          6.800000190734863,\n          6.199999809265137\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2.700000047683716,\n          3.200000047683716,\n          3.4000000953674316\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.099999904632568,\n          5.900000095367432,\n          5.400000095367432\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.299999952316284,\n          1.7999999523162842,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09587160684999495,\n        \"min\": 0.7418,\n        \"max\": 0.9827,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9827\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**7-Check the residuals of trained model**</p>"
      ],
      "metadata": {
        "id": "KxzPcegptWzy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(lr, plot = 'auc')  # Example: Plotting confusion matrix\n"
      ],
      "metadata": {
        "id": "DNG_IlN3tgOU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "1a6e86b0-e3fb-4b42-9523-6a9e4f52b4d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**8-Check feature importance**</p>"
      ],
      "metadata": {
        "id": "qr31GBqNvFzr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(lr, plot = 'feature')"
      ],
      "metadata": {
        "id": "41PDE7NsugDN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# finalize the model\n",
        "final_best = finalize_model(lr)# save model to disk\n",
        "save_model(final_best, 'diamond-pipeline')"
      ],
      "metadata": {
        "id": "0hdS7dQIyZWC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abfe40c5-a6e9-4a3b-9349-270e998c77b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=Memory(location=None),\n",
              "          steps=[('label_encoding',\n",
              "                  TransformerWrapperWithInverse(exclude=None, include=None,\n",
              "                                                transformer=LabelEncoder())),\n",
              "                 ('numerical_imputer',\n",
              "                  TransformerWrapper(exclude=None,\n",
              "                                     include=['sepal_length', 'sepal_width',\n",
              "                                              'petal_length', 'petal_width'],\n",
              "                                     transformer=SimpleImputer(add_indicator=False,\n",
              "                                                               copy=True,\n",
              "                                                               fill_value=None,\n",
              "                                                               keep_empt...\n",
              "                                                               fill_value=None,\n",
              "                                                               keep_empty_features=False,\n",
              "                                                               missing_values=nan,\n",
              "                                                               strategy='most_frequent'))),\n",
              "                 ('actual_estimator',\n",
              "                  LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
              "                                     fit_intercept=True, intercept_scaling=1,\n",
              "                                     l1_ratio=None, max_iter=1000,\n",
              "                                     multi_class='auto', n_jobs=None,\n",
              "                                     penalty='l2', random_state=123,\n",
              "                                     solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                                     warm_start=False))],\n",
              "          verbose=False),\n",
              " 'diamond-pipeline.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**9-Dashboard**</p>"
      ],
      "metadata": {
        "id": "gFJM4kkuDvY9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dashboard(lr, display_format='inline') # Change right to align_end and pass False\n"
      ],
      "metadata": {
        "id": "P5AUCzFYxPd0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**10-ROC Curve**</p>"
      ],
      "metadata": {
        "id": "oFlgqb9wtGCN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After we make it, let’s create charts for analyzing the model."
      ],
      "metadata": {
        "id": "MBba6kCmKS5c"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sydjGLFn2l9q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "3a00bffd-b946-404d-b49d-6c71460f5807"
      },
      "source": [
        "plot_model(tuned_lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nqovv2Qo2uAD"
      },
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**11-Interpret the results**</p>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lqx_HXzP2zfW"
      },
      "source": [
        "In PyCaret, we can interpret the model by SHAP values and correlation plot with just one line of code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGJ1m7LB21Kl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "34bae177-d3aa-491b-b846-420a6b86088b"
      },
      "source": [
        "interpret_model(tuned_lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "This function only supports tree based models for binary classification: dt, rf, catboost, xgboost, lightgbm, et.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-9a5330975f1f>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minterpret_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuned_lr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pycaret/utils/generic.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    962\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mglobals_d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pycaret/classification/functional.py\u001b[0m in \u001b[0;36minterpret_model\u001b[0;34m(estimator, plot, feature, observation, use_train_data, X_new_sample, y_new_sample, save, **kwargs)\u001b[0m\n\u001b[1;32m   1903\u001b[0m     \"\"\"\n\u001b[1;32m   1904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1905\u001b[0;31m     return _CURRENT_EXPERIMENT.interpret_model(\n\u001b[0m\u001b[1;32m   1906\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1907\u001b[0m         \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pycaret/classification/oop.py\u001b[0m in \u001b[0;36minterpret_model\u001b[0;34m(self, estimator, plot, feature, observation, use_train_data, X_new_sample, y_new_sample, save, **kwargs)\u001b[0m\n\u001b[1;32m   2247\u001b[0m         \"\"\"\n\u001b[1;32m   2248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2249\u001b[0;31m         return super().interpret_model(\n\u001b[0m\u001b[1;32m   2250\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2251\u001b[0m             \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py\u001b[0m in \u001b[0;36minterpret_model\u001b[0;34m(self, estimator, plot, feature, observation, use_train_data, X_new_sample, y_new_sample, save, **kwargs)\u001b[0m\n\u001b[1;32m   4081\u001b[0m             \u001b[0mmodel_id\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mshap_models_ids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4082\u001b[0m         ):\n\u001b[0;32m-> 4083\u001b[0;31m             raise TypeError(\n\u001b[0m\u001b[1;32m   4084\u001b[0m                 \u001b[0;34mf\"This function only supports tree based models for binary classification: {', '.join(shap_models_ids)}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4085\u001b[0m             )\n",
            "\u001b[0;31mTypeError\u001b[0m: This function only supports tree based models for binary classification: dt, rf, catboost, xgboost, lightgbm, et."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**12-AUC Plot**</p>"
      ],
      "metadata": {
        "id": "yWcwMLxoISnO"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59PQJ7gD887c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "d6130b55-ab3c-4c58-dbd7-006387fd1633"
      },
      "source": [
        "plot_model(tuned_lr, plot = 'auc')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAH7CAYAAAAjETxNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC9HUlEQVR4nOzdd1hT598G8DthI0vUqkUFFzhAhltxgHvhnkWrdQ+oq26te6JWqAO3WKsi4l51VxxtnaCCExRRcSCyR8h5//Dl/IygEgUPkPtzXVya54zcySHhmyfPeY5MEAQBRERERESFjFzqAEREREREeYGFLhEREREVSix0iYiIiKhQYqFLRERERIUSC10iIiIiKpRY6BIRERFRocRCl4iIiIgKJRa6RERERFQosdAlIiIiokKJhS5RATRp0iTY2Nhk+alVqxYGDhyIK1euZLvdX3/9hYEDB6Ju3bqwtbWFs7MzRo8ejeDg4I/e1+HDh9G/f3/UqVMHdnZ2cHV1xeTJk3Hnzp0cZX379i2WL1+Odu3awd7eHrVq1UKnTp3g6+uLpKSkL3r8+cmDBw/QqVMn2NraYu3atbm+fx8fH9jY2CA1NTXX952dvn37okePHl+8/ZMnT2BjY4Pt27erdZ8f/i5XqVIFdevWhYeHB+7du/fFefIzGxsbeHl5SR2DqFDTljoAEX0Zc3Nz7N+/X7ytVCoRGRmJNWvW4Mcff8Sff/6JGjVqiMunT5+Offv2YcCAARg7diyKFi2Kp0+fwt/fHz179sT06dPRp08fcX1BEDBx4kQcO3YMgwYNwqRJk1CkSBHcvXsXa9euRffu3bFixQq4uLh8NOPjx4/Rv39/6OvrY+TIkahRowYSExNx6dIlrFq1CocOHYKfnx/MzMzy5Dn6FrZu3Yr79+9j+/btsLKykjrOV/Px8VFr/UuXLmHKlCk4deoUAKB06dIICgqCsbGxWvupXr06fH19xdsZGRl4+PAhli9fjj59+mDfvn34/vvv1dpnfhcUFARDQ0OpYxAVaix0iQoouVyOEiVKqLSVLFkSPj4+aNy4MbZu3YolS5YAAHbt2gV/f3+sWbNGpTD9/vvvUatWLZQsWRLz5s2Dg4MDqlWrBgDYvn079u3bB19fXzRt2lTcpmzZsmjUqBH69euH+fPno1GjRtDWzv6tZNy4cdDW1sbOnTtVCp8qVaqgdu3a6NGjB/z8/ODp6ZlbT8s3FxcXh++++w52dnZSR8kV6n7ouHbtmsptLS2tLL+XOaGtrZ1lu1KlSqFixYpo3Lgx/P39MXr0aLX3m599yfNEROrh0AWiQsbAwACWlpZ4/vy52LZ+/Xo0atToo72vHh4eMDIygp+fn9i2ceNGNGzYUKXIzaSrqwsfHx/s37//o0Xu5cuXERwcDA8Pj2x796pXr46jR4+KRW5gYCBsbGzw4MEDlfVcXV0xZswYAP/7WnzXrl3o1asXbG1tsWXLFtjY2CAiIkJlu+joaFStWhWbN28GACQkJGDOnDlo1aoV7Ozs0Lx5c6xduxaCIIjb/Pvvv3B3d0ft2rXh4OCAzp0749ChQ9k+vsxshw4dQlRUFGxsbMTe0AcPHmDYsGGoVasWbG1t0bZtW2zdulVlWxsbG6xduxZDhw6FnZ1djoeCfMy1a9fw448/wtHRETVq1Mg2+/379+Hu7o4aNWqgUaNGWLduHXx9fWFjYyOu8+HQhePHj6Nr165wcnKCk5MTevXqhQsXLgB4N4Tmt99+U3n82Q1dePjwIYYNGwYnJyfUrVsXI0aMyHK8PqZkyZIwNzdX+X1OS0vDihUr0K5dO9SoUQNNmjSBl5cX0tLSxHXS09Mxd+5c1K1bF46Ojhg5ciRCQ0NhY2ODwMBAAP/7nTt79iyaNWuGrl27itvv27cP3bt3h5OTE+rUqYMxY8YgOjpaXP727VtMnToVjRo1gq2tLZo0aYK5c+ciJSUFwLtvRNasWYNWrVqhRo0aqFevHkaNGoXIyEhxHx8OXYiOjsa4ceNQr1492Nraonnz5vD29oZCoRDXcXV1xbx587Bt2zY0a9YMDg4O6Nat2yeHHxFpMha6RIVMWloaIiMjxa95o6OjERERkW3BmklXVxcNGzbEpUuXAADPnj1DZGQkmjRp8tFtSpQoAQMDg48u/+effyCTyT65j7Jly37m0WRvw4YN6NatG/766y907NgRurq6OHr0qMo6R48ehUwmQ7t27QAAo0aNwsGDB/Hzzz/j0KFDGDx4MH7//XesXLkSABAfH4+hQ4eiSpUq8Pf3x/79+9GqVSuMGzcO169fzzZHQEAAmjVrhlKlSiEoKAg//fQTXr9+jR9++AGxsbFYu3YtDh48iI4dO2LevHkqHySAdz3tNWvWxJEjR1C+fPkvei6AdwXsjz/+CENDQ/zxxx/Ys2cPatasibFjx+LEiRMA3v1eDBkyBNHR0Vi/fj3WrVuHK1euYPfu3R/db3h4OEaPHo1WrVph37592LVrF2xtbTFkyBA8e/YMU6dOzfL4PxQbG4t+/fpBEARs3boVW7ZsQXx8PH766SckJyd/9rHFxMTgzZs3KsMWZs2ahQ0bNuDHH3/EwYMHMXHiROzatQu//vqruI6Pjw/+/PNPjBgxAoGBgahduzbGjRuX7X34+vpi/vz5WLNmDYB3Re6ECRPg4OCAwMBArFq1Cg8fPkT//v3FYnru3LkIDg6Gt7c3jh8/jjlz5uDEiRNYsGABgHe/G76+vvjll19w9OhRrF27FnFxcRg6dGi2GVJTU9GvXz+EhoZi2bJlOHz4MAYNGoR169aJ38xkOnfuHG7cuIE1a9bAz88Pb9++xYQJEz77XBJpIg5dICpEXr16BS8vLyQkJIjjbTN7wkqXLv3JbS0sLHDkyBEolUqx58rCwuKLs0RHR8PY2BgmJiZfvI+Psba2Rrdu3cTbTZs2xdGjRzFs2DCx7ciRI2jQoAFKlCiBGzdu4OLFi5g/fz7atm0LAChXrhzu37+PjRs3YsiQIQgPD0dSUhI6dOggFp3Dhg1D/fr1YWlpmW0Oc3Nz6OnpqXxd/8cff+Dt27fw9vbGd999BwAYOnQorl27hq1bt6Jfv37i9sbGxhgyZMhXPx9+fn7Q19fHb7/9Bj09PQDAtGnT8M8//+CPP/5A8+bN8d9//yEqKgpr1qxBnTp1AADe3t5wdXX96H5DQ0OhUCjQpUsXFC9eHAAwefJktGvXDiYmJihSpEiWx//mzRuVfQQGBuLNmzdYsGABzM3NAQAzZ87E6tWr8fTpU1SsWPGj9//kyRPMmjULBgYG4vGOjo5GYGAghg8fLvY8lytXDi9evMDChQsxevRolCxZEnv27EHz5s3x448/AgDKly+P58+fZ/nGAADatm2LunXrirfXrFmD2rVrY+rUqQAAKysrLFy4EJ06dcKxY8fQoUMH3Lp1C7Vr14ajoyOAd68vPz8/KJVKAMCtW7dQunRpNG/eHMC7YUK//fYbnj59CqVSCblctZ/p+PHjiIiIwK5du8Sx9eXKlcO9e/ewc+dOjBs3Drq6ugDefTsxd+5c8XbHjh3h4+ODhIQEGBkZffT5JNJE7NElKqBev34NR0dH8cfe3h4NGzbE3bt3sWrVKtjb2wMAZDIZAIh/gD9GEATI5XLIZDJxm68hk8lUhgXkJltbW5Xb7du3R2hoKB4/fgwAePr0Ka5fv46OHTsCAG7cuAEAcHZ2Vtmufv36SExMREREBCpVqgRLS0t4eHhg9erVuHHjBpRKJezt7dUatxoSEoJy5cqJRW4mR0dHPH78GAkJCR99HF8qJCQEdnZ2YpH7/n3evn0bAMTn5v2xxJk9+R/j5OQEc3NzuLu7Y9OmTQgLC4OWlhYcHR1RpEiRHGULDg5GmTJlxCIXACpWrAgvLy+VIjckJETl97lGjRpo1qwZkpKSsHnzZrFH9+bNm1AqlVly169fH4Ig4Pbt20hNTcWLFy+yPL8f+1bj/fUSEhLw8OHDLPuvWrUqzMzMxOezWbNm8Pf3x+TJk3HixAnEx8ejXLly4gmJLi4uiIiIQP/+/bFnzx48e/YM5ubmsLW1zVLkZj5+PT29LGO9HR0dkZycjIcPH4pt1atXF4tcAOJz+/bt22wfH5EmY48uUQFlZmaGnTt3irdv3ryJsWPHYuDAgSrDBTILhPfHBmYnMjISpUuXhkwmE3t/czqOMjvff/894uPjERMTo1Lk5IYPx/y6uLjA2NgYR48exZAhQ3DkyBEYGBiIvWmZxWXr1q1Vtsss/l++fAlra2vs2LEDGzZswN69e/Hbb7+hWLFi6N+/PwYPHpzj4j8hISHbMcmZPW2JiYni/3OrtzshIQHlypXL0l6kSBEkJiYCeDeEILPtfZ86NqVKlcKuXbuwYcMGbN68GQsXLoSFhQWGDx+O7t275yhbfHx8jopiGxsbrFixQrx98uRJLFmyBOPHjxc/tAH/O5Y//fSTSsGY+aHq5cuXaj/W949X5v5XrlyZZbq45ORkvHjxAgAwduxYVKxYEbt37xZPknNxccG0adNQsmRJNGnSBH5+fvDz88O8efMQHx8Pe3t7TJw4ETVr1sySISEhAUWKFMnye/b+702mD2dqyNwmrz5YEhVkLHSJCigtLS2Vr9QtLS1x7NgxzJs3Dw0bNhR7IYsXLw5ra2ucOHEi2zGUwLvxm5cuXRILwe+++w4VK1bE8ePHMWjQoGy3efz4MW7evIk2bdpkWwTWr18fwLu5e3v16pXtPo4dO4ZKlSqhYsWKHy0k3/8D/zG6urpo2bKlWOgePnwYrVq1EscQm5qaAgC2bNki/v99mV+7m5ub45dffsEvv/yCyMhIBAQEYPny5TA3N1cZKvEpJiYmePbsWZb2+Ph4AMiTr5aNjY1VeoozvV90Z/YAJicnq4ytziwKP6ZMmTL49ddf8euvv+LevXvYunUrpk2bhjJlyojH+FPMzc3x6NGjz66nq6ur8vvcv39/HDlyBNOmTcOePXvE/JnHz8vLC9bW1tneX3p6OgCIJ4Zl+txjBf5X9Pbv3z/bYj6zyJTJZOjUqRM6deqExMREnD17FkuWLMHYsWOxbds2AECtWrVQq1YtKBQKXLlyBb///jsGDx6MM2fOZPmQY2JigsTERAiCoPJayPy9yYshQESagEMXiAqRKVOmIDk5WTwhJlPmRSQOHjyY7XYrV65EUlKSyvjRgQMH4vr169merJSWloapU6di0aJFHy1Ea9Sogdq1a+P3339XOVs90+3btzFhwgTs2LEDwP8KjJiYGHGdR48e5ag4ASCOm/zvv/9w8+ZNcdgCALFH8MWLF7C0tBR/TExMYGBgAENDQ0RERIhzwQLvTpQbM2YMKleujLCwsBxlyHzckZGRWR7zlStXULFixRx/5a8Oe3t7hISEqFxUQhAEXL16VfwqPLOIDAkJEddJTk7G33///dH9hoaG4uLFi+LtypUrY/bs2TAyMlJ5Tj7Vk2htbY0nT56oFP9PnjxB7969cfny5Y9uJ5fLMXPmTISHh4sniQHvhhloaWnh6dOnKseyRIkSkMvlMDY2hrm5OUxNTbPMRHDs2LGP3l+mIkWKwNraGuHh4Sr7t7S0RFpaGooVK4bk5GQcOnQIcXFx4jZt27bFjz/+iNDQUADvThi7f/8+gHdTp9WtWxeTJ09GYmJitt+u1KhRA6mpqVkyX7lyBUZGRoVijmYiKbDQJSpESpUqBQ8PD+zduxfnz58X2zt16oSePXti0qRJWLx4MW7evImnT5/i8uXLmDRpEtavX4+5c+eqjJns2rUrunbtiunTp2P+/Pm4efMmnjx5grNnz6Jv3764e/cufvvtt0/2UC5atAh6enro0aMHAgIC8OjRI9y/fx9btmxB//794eTkJE4dVq1aNWhra2PDhg0IDw/H9evXxa+Bc6Ju3br47rvvMH/+fJQsWVLl5KLMq8Blnhn/5MkT/Pvvvxg0aBCGDRsGQRDw+PFjjBo1Cps2bUJERASioqIQGBiI8PBw1K5dO8fHoEuXLjAzM8OYMWMQHByM8PBweHt74++///6qE89evXqFly9fqvxknvjVt29fpKamYty4cbhz5w7u37+PX3/9FQ8fPsTAgQMBvOthNzMzw9KlS3H9+nXcuXMH48aNQ9GiRT96n9evX8eIESOwe/duREZGIjIyEhs3bkRSUpL49buJiQlevnyJy5cvZ1vAde3aFUWLFsUvv/yCu3fvIiwsDL/++qs4/dunVK9eHb1798batWvForF48eLo1q0bfv/9d+zduxeRkZG4ceMGPD094e7uLs7k0Lp1a5w8eVL8vdu6detHrxj4oaFDh+LkyZPw8fHBgwcPcP/+fSxatAidO3fG7du3oa2tjcWLF2PChAkIDg7Gs2fPcPXqVezfv1880S8wMBAjR45EUFAQnj59irt372LTpk0oVqxYtifgNWvWDBUrVsSUKVPw77//4vHjx9i6dSsCAgIwYMAA6Ojo5Cg7Eani0AWiQqZfv37Ys2cPZsyYgYMHD4pfU8+ePRuNGzfG9u3bsXv3biQmJqJo0aKoU6cOdu/ejSpVqmTZV+YFIXbu3ImffvoJKSkpKF26NBo1aoTly5d/9kpVFhYW2Lt3LzZs2IBNmzZhzpw50NPTg5WVFcaOHYuuXbuKf8C///57zJ49GytXroSbmxusrKwwceJEeHt75+hxy+VytGvXDps2bcLAgQOznPDj4+OD5cuXY/bs2Xj16hVMTU3RvHlzjBkzBjKZDI0bN8b8+fOxefNmrFixAjKZDJaWlpg2bRpatWqVowzAu6/Ot27disWLF2PAgAFITU1FhQoVsGjRInTq1CnH+/lQdrMjVKlSBfv27UOFChWwefNmLFu2DD179oRSqUTVqlWxZs0a1KtXD8C7XsfVq1djzpw5cHd3R6lSpTBo0CA8evToo0MLevfujeTkZKxfvx6zZ8+Gjo4OKlWqhBUrVogzA/Tu3RtBQUHo378/evfuLc5y8OHzsXDhQvTs2RO6urpwcnLCpk2bctS7PXr0aBw7dgxTp07F9u3bIZfLMWPGDHz33Xfw8fHB8+fPUaRIETg7O+OPP/4Qf98nTJiA5ORkzJ8/H1paWmjatClmzJiB3r17Zzlp70Pt27eHXC4X5xnW1taGnZ0d1q9fL564tnnzZixevBiDBw9GYmIiSpQogUaNGokf3ObMmQMvLy9MnToVr1+/homJCezt7bFx40bo6+tnuU9dXV1s2rQJixYtgoeHBxITE2FhYYHx48dneU6JKOdkAkevExFphMxxvO/3wo8YMQKPHj365IUxCqL09HTExcWhWLFiYtuJEycwcuRIlSm8iKhw49AFIiINoFAo4Obmhv79++PGjRuIjIzEn3/+idOnT+d4BoWC5Pfff4eLiwv27duHqKgo/Pvvv1i2bBmqV6+ea9O6EVH+xx5dIiINERERgSVLluDKlStITk5GmTJl0L17d/Tt2xdaWlpSx8tVCoUCK1euxIEDBxAdHQ1zc3PUqVMH48ePz/G4byIq+FjoEhEREVGhJPnQhXPnzqFBgwbiAP6PUSqVWL58OZo1a4batWtj4MCBn50An4iIiIg0l6SF7rp16zB37tyPXkf+fdu2bcOBAwewdu1anD59GlZWVhg5ciSvBENERERE2ZK00NXT00NAQECOCt2dO3eif//+qFixIoyMjDBmzBg8ePBAvIY9EREREdH7JJ1H9/2rMH1KSkoK7t+/j2rVqoltRkZGsLS0REhICBwcHD67j2vXrkEQBE66TURERJRPpaenQyaTwdHRMVf2VyAuGPH27VsIgpDlGvWmpqbilYE+RxAEpGco8SQ+6/Xg1SIAJRNjoKXM+Lr9EBEREREyAMRChiIQoP9dSchzsVOyQBS6mb5mPK6Ojg6exCcgQmEA+zLFvypH+thpkJ89CwBIq1IZQhlLKN+blBwZGciwtATeu/qOzr//quxDkMshvH/pzfR0ZNjYADLZu9tpadC5fl11G21tCB8U+xnvXUpSlpAA7du3VbfR04Pw3uTwgrY2lO8NFZG9fg3tBw9UtzEwgGBo+L/b+vpQWliIt+XPnkHrgxMBhSJFILx3tR/B2BjK774Tb2tFRED+4oXKNkpjY0BX93+3zcwgvPc8at29C3lsrOo2ZmbAe9MgKYsVg2BmJt7WvnkTsqQkKJVKpKalQk9XDyhe/H/Pq1KJjO+/B967IpP2lSuQZfzvg4sgk0EwN//fnSoU755n7f9/uWRkQOeDS4lmOTYZGciwtv7f7aQk6Ny8qbqNri4EY+P/NcjlyChfXrwpi42F9t27qtvo60N4L7ugpwdlmTL/28WLF9CKiFDdxtAQwv9fLQp4d6yUpUr9b5vISGg9e6a6jZERhPd+f5WmphCK/+91o/XgAeSvX6tsozQ1/d9zBEBpbq7yO64VGgp5fLzqNkWLAplXMBMEKEuWVHlOtG/cgCw1VXWbbF5rGdraSEiIh5GRMfQ/PDYfvtbS0pDx/qVnU1Oh88EQqM++1uLjoR0aqrpNbrzWDAygfO9qc/KnT6H15InqNh++1oyMoHxvuqwcvdaKFlX5Hf+a15rKNubmqq81Cwvgvcen9mtNoYDO1auqj19bGxnGxkhOToaBgQHkgqDxr7UMc3Pgvd9x7du3IUtQ7dT57Gvt+nXI0tJUt1H375qWlsrvSG691uS3bonv53K5POtrTUcHynLl/rfNq1fQfvhQ9X4+eK0pDQwgfO619sGxyfJaCw+H/OVLlW0++3ftzh3I375V3eZzr7WQEMj+/9LW4jrqvtY+fB9UKJBRqdL/7vcjr7VP/l1LTITOrVuq23zwWhO0tKC0shJvy968gfa9eyrbZOjpwePSVTyJT4Sxrg78//wTeiW+Q24pEIWumZkZ5HI5Yj94I46NjVW56k1O2JcpjiY2ZT6/4ic86NAaMf9f6JYePBAVho7/qv1R7ktKSkJoaCiqVq0Kw/de/FQ48XhrFh5vzcLjXfj9/vff6NGjB36eMAH6xb6uM/JDkk8vlhN6enqoXLkybr33ySEuLg6PHz/+JpdxTAq5geSw//XeyDI/RRERERFRjgmCgCsffPPWuHFjXL9+HWPHjs31GivfFrrR0dFo3bq1OFdu79694efnhwcPHiAhIQFeXl6oWrUq7Ozs8jRH3LmzCGvlgrud2iL1CeftJSIiIvoSr169Qr9+/dCiRQucOXNGZVmp94b55CZJhy5kFqkKhQIAcOLECQBASEgI0tPTER4ejrT/HzPUq1cvvHz5En379kViYiLq1q2L33//PU/zvdm/Fw8G/AAhNRXKpCQkXb8GvTJl8/Q+iYiIiAojQRBw6dIlAMDy5cvRtGnTPL9PSQvdkJCQjy4rU6YM7ty5I96WyWTw9PSEp6fnt4iGl5s3IMJzOKBUQm5oiErb/GHaojUAQMemsrje+/8nIiIiouyVKFECy5Ytw7lz5/Drr79+k/ssECejfUuCIODZkgWImj0DAKBtXgyVd++HUe264jrvjx/heF0iIiKirE6dOoW7d+9i2LBhYluHDh3QoUOHb5aBhS4Axdu3kGlpQW5oiMe/jMEL35UAAO3vSqLckuUAgIT//hHXTwu7m+1+iIiIiAhYt24dJk6cCC0tLdSpUwdOTk6S5ND4Qlfx9i2Cq1eE9Z5DSL4ZIha50NaG4kU0Hv7Y55PbC+np3yAlERERUcHRsmVLzJkzB/r6+lmmh/2WNL7QTbkbhozYWMQe2g+z9h1h1q4DFK9fISU8HIro55/dXvhggm0iIiIiTZOamgodHR3I///CJJaWlvDz84OtrS2KF8/duXHVofGFbqZnXotg1s4NFTf/CUEQkHI37KO9tS+unsPrcZMAAPL3rpxDREREpGmCg4MxbNgw9O3bF8OHDxfbv8WsCp+j0YVu6qMIPJk1XaVN/v+Xbyzi8PGxJLFJ0Xmai4iIiKggEAQB48aNQ1hYGObMmYO2bdvC8r3Ln0st314wIq8l3QxBaPPGiD9zSuooRERERAWSTCbDihUrULRoUcydOxflypWTOpIKjezRjb8QhHs9OiFDwsHRRERERAWNUqnEkSNH0LZtW3GK1WrVquHGjRswMjKSOF1WGtejq/X3Cdxxa/2uyNXSQulJ06SORERERJTvRUdHo1OnTujbty927Nihsiw/FrmAhhW6hmkp0B0/FEJKCuQGBqjsvwdmrdpIHYuIiIgo3zM2NsazZ88AANu3b4cgCBIn+jyNKnTNUuIhy8iAVtGisDn4F8xatZU6EhEREVGBYGhoiJUrV2LcuHEICAgoEFeH1bgxusrvSsH24DEYVKsOANAqYoQideqK/yciIiIiIDAwEFeuXMG8efPEtjp16qBOnToSplKPRhW6Kdq6UG7cLRa5AGBQrTqqnTovYSoiIiKi/GXDhg345ZdfAADOzs5o06ZgDvXUqKELMYamEEpbSB2DiIiIKF/r2rUrSpcujbJly8LMzEzqOF9Mo3p0iYiIiCir+Ph4aGtrw+D/L5xlZmaGnTt3wtLSEsbGxhKn+3Ia1aObHcXr14gJDEBMYAAUr19LHYeIiIjomwoKCkKjRo0wd+5clXZbW9sCXeQCLHSR8vA+HvTrhQf9eiHl4X2p4xARERF9UytXrsTjx4+xZs0ahIaGSh0nV2l8oUtERESkyZYtW4YKFSpg3bp1qFq1qtRxchULXSIiIiINkZaWhg0bNkChUIhtpUuXxj///IMuXbpImCxvaNTJaAbKNKRfO4Enz82hiHoKZXwCFI8ei8tfXD2H2KToz+4n9sZ/eRmTiIiIKNdFR0ejZ8+eCA4Oxps3bzB+/HhxmZaWloTJ8o5GFbpFE95CGDQIzz6y/PW4Sd80DxEREdG3Urx4cejr6wMALly4AKVSCbm8cH+5r1GFbm7LMNJHMdu6UscgIiIiypYgCOKlerW0tLBq1Sr89ddfGDJkSKEvcgENLHQT+vVE5Z49xaELmbTLWkBepIha+ypmWxcGxUrmdkQiIiKiryIIAjZt2oRTp07Bz89PLGorVKiAYcOGSZzu29G4QhdlKqJMEzepUxARERHlma1bt4pjcDdu3IhBgwZJnEgahb/PmoiIiEjD9OzZE9WqVUOVKlVQu3ZtqeNIRvN6dImIiIgKmZcvX0JLSwvm5uYAAD09PezYsUPlBDRNxB5dIiIiogLs0KFDaNiwIcaOHQtBEMT2MmXKaHSRC7DQJSIiIirQTpw4gVevXmH//v24du2a1HHyFRa6RERERAXY7Nmz0aBBAwQEBMDJyUnqOPkKC10iIiKiAiIxMRHz5s1DfHy82GZsbIyDBw/C1dVVwmT5k8adjKY0NZU6AhEREZHaYmJi0LJlSzx8+BCvXr3C8uXLpY6U72lcj65QrJjUEYiIiIjUVrRoUdjZ2QEAXr9+DYVCIXGi/E/jenSJiIiICgqFQgFt7Xflmkwmg5eXF9q2bYtu3bqJl/alj9O4Hl0iIiKi/E6hUMDLywstWrRAamqq2F6sWDF0796dRW4OaVyhK3v1SuoIRERERJ+0b98+zJ8/Hzdu3MCSJUukjlNgadzQBXlcnNQRiIiIiD6pc+fO+OOPP5CcnIzevXtLHafA0rhCl4iIiCi/iYyMhFKphKWlJQBALpdj06ZNMDY2hpaWlsTpCi6NG7pARERElJ9s374dDRs2xLBhw5CRkSG2m5mZscj9Six0iYiIiCT09OlTJCQk4L///sO///4rdZxChUMXiIiIiCT0888/4+7duxg8eDBq1aoldZxChT26RERERN/Imzdv8PPPP+PZs2dim7a2Nnx9fVnk5gH26BIRERF9A/Hx8XB2dsazZ8/w9OlT+Pv7cz7cPMYeXSIiIqJvwNjYGJ07dwYAlCpVCmlpaRInKvw0rkdXWayY1BGIiIhIQ8THx8PY2Fi8PW3aNLi6usLV1VXCVJpD43p0BVNTqSMQERFRIZecnIypU6eifv36iI2NFdv19fVZ5H5DGlfoEhEREeW1CxcuYPXq1Xj69ClmzZoldRyNpXFDF4iIiIjyWrNmzdC7d2+8fPkSEyZMkDqOxtK4Qlf+/LnUEYiIiKiQCQ0NRWpqKhwcHMS2ZcuWQVdXlzMrSEjjhi7IkpKkjkBERESFyPr16+Hi4oJBgwYhMTFRbNfT02ORKzGNK3SJiIiIclORIkWQlpaGqKgoXLlyReo49B6NG7pARERElJt69eqF+/fvo0ePHrCxsZE6Dr2HPbpEREREOfT06VP88MMPuH37ttgmk8kwffp0Frn5EHt0iYiIiHIgOTkZzZs3x/PnzxEVFYW//voLurq6UseiT2CPLhEREVEOGBgYwMPDAzKZDE2aNIEgCFJHos9gjy4RERHRR0RFRcHCwkK8PXToUDRo0AD29vYSpqKcYo8uERER0Qfi4uIwcuRI1K9fH48ePRLb5XI5i9wCROMK3YySJaWOQERERPncw4cP4e/vj4SEBMyePVvqOPSFNG/oQpEiUicgIiKifM7BwQFjx47Fo0ePsHDhQqnj0BfSvEKXiIiI6AP//vsv3r59ixYtWohtEydOhFyucV9+Fyo8ekRERKTRVq5cibZt22L48OF48eKF2M4it+DTuCMof/JE6ghERESUj9ja2kKpVCI9PR2hoaFSx6FcpHFDF2RpaVJHICIiIgkJggCZTCbebtKkCZYuXYoWLVqgTJkyEiaj3KZxPbpERESkue7du4dWrVrh7NmzKu0DBgxgkVsIsdAlIiIijZCeno6uXbvi8uXLGDVqFOLi4qSORHmMhS4RERFpBB0dHcydOxc6OjoYOHAginDK0UJP48boEhERkWYQBAE3b96EnZ2d2Obm5gYnJycOU9AQ7NElIiKiQufFixf44Ycf4OrqiqtXr6osY5GrOVjoEhERUaGTnJyMoKAgZGRkYOnSpVLHIYlw6AIREREVOpaWlpg3bx5u3bqFGTNmSB2HJKJxhW6GhYXUEYiIiCiXHT9+HNHR0XB3dxfb+vbtK2Eiyg80rtCFnp7UCYiIiCgXeXt7Y+bMmdDX10edOnVgbW0tdSTKJzhGl4iIiAq05s2bQ1dXFyYmJnjx4oXUcSgf0bweXSIiIirQUlNToaurK17Gt1q1ati0aRPq1KmDYsWKSZyO8hON69GVR0RIHYGIiIi+0LVr19CkSRPs2LFDpb1NmzYscikLSQvdqKgoDBkyBHXr1oWLiwuWLFkCpVKZZT2lUglvb2+4urrC0dERHTp0wOHDh7/oPmXZ7J+IiIjyP6VSiREjRuDu3buYNGkSoqOjpY5E+Zykha6HhwdKliyJEydOYNOmTThx4gS2bNmSZb3t27dj165dWL9+PS5fvoyxY8fil19+QVhYmASpiYiISApyuRwrVqyAiYkJZs2ahe+++07qSJTPSVbohoSEICwsDOPHj4exsTGsrKzQv39/7Ny5M8u6t27dQs2aNVGhQgVoaWnBxcUFZmZmuHPnjgTJiYiI6FvIyMjA6dOnVdrq1KmD4OBg9O/fXxyjS/Qxkp2MduvWLVhYWMDU1FRsq169OsLDw5GQkAAjIyOxvWnTppg5cyZCQ0NRsWJFnDt3DsnJyahTp47a95uRkYGkpKRceQyUfyUnJ6v8S4Ubj7dm4fHWDE+fPoWHhwf++ecfzJ49G1ZWVuIybW1t/i0vpARByNUPMJIVurGxsTAxMVFpyyx637x5o1LotmzZEqGhoejUqRMAwMDAAIsWLULp0qXVvt+EhHiEhoZ+eXAqUCJ48qFG4fHWLDzehVtcXBzu3bsHADh06BDq168vcSL6VnR1dXNtX5JOLyYIQo7W27t3L/bu3Ytdu3bBxsYGFy9exLhx41C6dGnUqFFDrfs0MjJG1apVvyQuFSDJycmIiIiAlZUVDAwMpI5DeYzHW7PweGuOFStW4L///kObNm14vDVE5oeb3CJZoWtubo7Y2FiVttjYWMhkMpibm6u0//HHH+jZs6dY1DZt2hT16tXD/v371S50tbS0YGho+FXZqeAwMDDg8dYgPN6ahce78BAEAYGBgbh//z4mTpwotrdr1w4uLi4IDQ3l8dYQuT3uWrJC19bWFs+ePUNMTIxY2IaEhKBSpUooUqSIyrpKpRIZGRkqbWlpad8sKxEREeUdHx8fzJw5EzKZDM7OzmjYsKHUkaiQkGzWhWrVqsHOzg5Lly5FQkICHjx4gE2bNqF3794AgNatW+Py5csAAFdXVwQEBCAsLAwKhQJBQUG4ePEimjVrpvb9Zlha5urjICIioq/TvXt3mJmZoVy5ctDW5kVbKfdI+tvk7e2N6dOno2HDhjAyMkKvXr3Qp08fAEB4eLh4RuXQoUOhUCgwcuRIxMTEwMLCAnPnzv2ygelaWrn5EIiIiEhNcXFxMDQ0FIva0qVLw9/fHzY2NjA2NpY4HRUmkha6pUqVwrp167Jd9v4cuTo6Ohg9ejRGjx79jZIRERFRXvj7778xatQo/Pjjjxg3bpzYXqtWLQlTUWEl6ZXRJMFLABMREUlCEAQsWLAAT548waJFi/D48WOpI1Ehp3GFrhbnXSQiIpKETCbD77//jrJly2LdunUoV66c1JGokNO4QpeIiIi+jbS0NGzdulVl3vyKFSvi8uXL6Nixo4TJSFPw1EYiIiLKdU+ePEGfPn1w8+ZNpKWlYeDAgeIyHR0dCZORJmGPLhEREeW6EiVKQPn/58WcPHkyx1dDJcpN7NElIiKiXKenp4fVq1fj4sWLGDRoUK5f8YooJ1joEhER0VdRKpXYsGEDrl+/jt9//10sau3s7GBnZydxOtJkLHSJiIjoq6xZswbTpk0D8O5qpl27dpU4EdE7HKNLREREX6Vv376wtLRE9erVYWNjI3UcIpHm9ehyjBAREdFXefHiBQwNDWFkZAQAMDY2xu7du2FhYQE9PT2J0xH9j8b16GaULy91BCIiogJr//79aNiwIWbMmKHSXqFCBRa5lO9oXKFLREREXy4wMBCvX7/Gli1bcOfOHanjEH0SC10iIiLKsaVLl8LJyQmBgYEcj0v5nuYVumlpUicgIiIqEBISErBkyRKkpqaKbcWKFcPx48fRpEkTCZMR5YzGnYym9eSJ1BGIiIjyvWfPnqFdu3aIiIhAcnKyyphcXvyBCgrN69ElIiKizypZsiQsLS0BABEREeLlfIkKEo3r0SUiIqLsZWRkQEtLCwAgl8vh4+ODS5cuoWvXruzFpQKJPbpEREQaLj09HYsXL0bnzp2RkZEhtpcpUwbdunVjkUsFFgtdIiIiDbdt2zYsXLgQQUFBWLVqldRxiHINhy4QERFpuB9++AFbt26Fnp4e2rdvL3UcolzDQpeIiEjDPHr0CHp6eihVqhQAQEdHBzt27IC5ubk4RpeoMODQBSIiIg2ydetWNGrUCJ6enhAEQWwvUaIEi1wqdDSu0BV0dKSOQEREJJmwsDAkJCTg9OnTuHHjhtRxiPKUxg1dUJYtK3UEIiIiyUybNg2PHz/GmDFj4ODgIHUcojylcT26REREmuL169cYP348YmNjxTYDAwNs3boVTk5O0gUj+kY0rkeXiIhIE7x69QqNGjVCdHQ04uPj4evrK3Ukom9O83p0k5KkTkBERJTnihcvDldXVwDvenEVCoXEiYi+PY3r0dV6/lzqCERERHkiKSkJhoaG4u0FCxagS5cuaNasmYSpiKSjeT26REREhUxSUhImTZoEFxcXJL33zaWJiQmLXNJoLHSJiIgKuGPHjmHt2rW4d+8eFi9eLHUconxD44YuEBERFTadOnVCQEAAlEolhg8fLnUconyDhS4REVEBc/v2bWhpacHGxgYAIJPJsG7dOhgYGEAmk0mcjij/4NAFIiKiAmT16tVwdXXFsGHDkJ6eLrYbGhqyyCX6AAtdIiKiAkQQBKSlpeHOnTu4du2a1HGI8jUOXSAiIipAhg0bhsePH+Onn36CtbW11HGI8jWN69EVDAykjkBERJQjT548Qb9+/fD48WOxTS6XY+HChSxyiXJA43p0laVLSx2BiIjos+Li4tC0aVPExMQgNjYWe/fuhVyucf1TRF+FrxgiIqJ8yMTEBD/99BPkcjlq1qyJjIwMqSMRFTga16NLRESUX7148QLfffedeHv8+PFo3bo1nJycJExFVHBpXI+uLC5O6ghEREQq3r59i2HDhqFhw4Z48eKF2K6rq8sil+graFyhK3/1SuoIREREKm7cuAF/f3+8fv0a8+fPlzoOUaHBoQtEREQSa9y4MQYNGoSEhATMmjVL6jhEhQYLXSIiom/s0qVLAIB69eqJbQsWLICWlpZUkYgKJY0bukBERCSlZcuWoV27dhgyZAji3jtvhEUuUe5joUtERPQNVahQAYIgIC4uDrdv35Y6DlGhxqELREREeUgQBMhkMvF2p06dEBkZiS5dusDCwkLCZESFH3t0iYiI8sjdu3fRpk0bXLt2TaXdw8ODRS7RN8BCl4iIKA8kJyejffv2+PfffzF8+HAkJydLHYlI42hcoas0NpY6AhERaQADAwNMnToVurq66NOnD3R1daWORKRxNG6MrlCihNQRiIioEBIEAXfu3EGVKlXEtn79+qFx48YoX768hMmINJfG9egSERHltufPn6N3795wdXXF3bt3xXaZTMYil0hCLHSJiIi+UkxMDM6cOYOUlBR4eXlJHYeI/p/GDV2QxcRIHYGIiAqZatWqYcqUKXj+/DmmT58udRwi+n8aV+jKY2OljkBERAXcX3/9heTkZHTs2FFs8/T0lDAREWVH4wpdIiKir7Fo0SIsWrQIJiYmqFmzJsqUKSN1JCL6CI7RJSIiUkPTpk0hl8thYGCAqKgoqeMQ0SewR5eIiOgT0tPToaOjI96uW7cufH194eLiAnNzcwmTEdHnsEeXiIjoI65evQpnZ2ccPXpUpb1r164scokKgK8qdBUKRW7lICIiylfS0tLw448/4t69exg9ejTevn0rdSQiUpPaha5SqYS3tzdcXFzg5OQE4N31vH/99VekpaXlekAiIiIp6OrqYtmyZTAyMsKUKVNgYmIidSQiUpPaha6Pjw92796Nvn37im1JSUm4fv06VqxYkavhiIiIvpWMjAycO3dOpa1Fixa4ceMG+vXrB5lMJlEyIvpSahe6+/btw+rVq/HTTz+JL/pixYph+fLl2LdvX64HzG3KokWljkBERPlMZGQk2rdvj06dOuHixYsqy4ry7wZRgaV2oRsTE4Nq1aplabe0tCwQ45cEvmEREdEHtLW1cefOHQiCgHXr1kkdh4hyidrTi33//fcIDQ1F1apVIQiC2H7hwgWUKFEiV8MRERF9C6VLl8aSJUvw8OFDjBkzRuo4RJRL1C503dzcMHLkSAwcOBCCIOCvv/7CzZs3sX37dgwYMCAvMhIREeUaQRDg7++PmJgYDB8+XGzv2rWrhKmIKC+oXegOHToUaWlp8Pb2Rnp6Ojw9PVG8eHEMGzasQBS68hcvpI5AREQSWrhwIZYsWQIdHR04OzvDzs5O6khElEfULnQVCgU8PT3h4eGBmJgY6OnpwcjICAqFAs+ePYOFhUVe5Mw1soQEqSMQEZGEevTogZUrV6JkyZJIT0+XOg4R5SG1T0arVasWAEAmk6FYsWIwMjICAKSkpKBz5865m46IiOgrxcXFqZxTUrFiRezcuRNnz54V54MnosIpxz26Fy9exMWLF6FQKLBs2bIsyx8/fswrpRERUb5y+vRpeHh4YOzYsfjpp5/E9oYNG0qYioi+lRwXurq6uoiIiEBGRgYOHjyYZbmhoSHGjx+fq+GIiIi+VEZGBqZPn46nT59ixowZaN++Pb777jupYxHRN5TjQrdmzZqoWbMmevToAX9//7zMRERE9NW0tLSwatUq9O7dG4sWLWKRS6SB1B6j+7EiV6FQwNXV9asDERERfYnU1FTs2rVLpa1GjRq4evUq2rdvL1EqIpKS2rMupKSkYNWqVbh+/TrS0tLE9pcvXyIlJSVXwxEREeVEREQE3N3dcfv2bcjlcpU5cfX09CRMRkRSUrtHd/78+QgMDESJEiUQEhKCcuXK4e3btyhevDjWrFmj1r6ioqIwZMgQ1K1bFy4uLliyZAmUSmW26z548AB9+/aFvb09mjRpgs2bN6sbnYiICqlixYoh4f+njzx06JDEaYgov1C70D19+jS2b9+OpUuXQktLC4sXL8bBgwdhbW2NR48eqbUvDw8PlCxZEidOnMCmTZtw4sQJbNmyJct6KSkpGDRoEJo0aYJLly7Bx8cHAQEBePDggbrxoeRliomICh1jY2OsWrUKixcvxvr166WOQ0T5hNqF7tu3b1G2bNl3G8vlUCqV0NLSwqhRo/D777/neD8hISEICwvD+PHjYWxsDCsrK/Tv3x87d+7Msu6RI0dgZGSEQYMGwcDAADVq1MDBgwdRsWJFdeNDMDZWexsiIso/lEolAgMDMWvWLJX2Bg0aYNCgQZDL1f7TRkSFlNrvBqVKlcK1a9cAAObm5rhx4wYAwMjICC/UuLzurVu3YGFhAVNTU7GtevXqCA8PF79+ynTlyhVYW1tj8uTJqFWrFlq3bo39+/erG52IiAqBFStWYPXq1Vi3bh1OnjwpdRwiysfUPhmtT58+cHd3x4ULF9CsWTN4enqiRYsWuH37NmxsbHK8n9jYWJiYmKi0ZRa9b968Ea+4BgDPnz/H5cuXMWfOHMyYMQNHjx7FxIkTUalSJVSrVk2t/AqFAklJSWptQwVPcnKyyr9UuPF4a5bu3btjw4YNKF26NMzMzPieXsjx9a1ZBEGATCbLtf2pXej2798f33//PUxMTPDLL78gKSkJFy9ehKWlJSZMmKDWvt6/JOPn1qtevTo6dOgAAOjcuTN27NiBo0ePql3oJt29g9DQULW2oYIrIiJC6gj0DfF4F04xMTEwMjKCrq6u2LZkyRJYWFgAAN/TNQRf35rj/df611K70AWAli1bikHmzZv3RXdsbm6O2NhYlbbY2FjIZDKYm5urtJcoUSLLuhYWFnj58qXa92uio4uqVauqvR0VLMnJyYiIiICVlRUMDAykjkN5jMe78Dpw4AAmT54Md3d3TJo0CcD/evZ4vDUDX9+a5d69e7m6P7UK3YSEBFy6dAl6enqoV68edHR0VJb7+fmhX79+OdqXra0tnj17hpiYGLGwDQkJQaVKlVCkSBGVdStWrIjt27erdGdHRUWhUaNG6sQH8O5KOYaGhmpvRwWTgYEBj7cG4fEuXARBwI4dOxAbG4s1a9ZgyJAhKFOmjLicx1uz8HhrhtwctgCocTJaeHg42rdvj1GjRmHw4MFo164doqOjAQCvX7/GoEGDsGTJkhzfcbVq1WBnZ4elS5ciISEBDx48wKZNm9C7d28AQOvWrXH58mUAgJubG968eYM1a9YgJSUFBw8exK1bt+Dm5qbOYyUiogJEJpPBx8cH1atXx+7du1WKXCKinMhxobtixQrY2dnh9OnTOHbsGMqWLQtvb2+cPXsWHTp0QHR09EcvD/wx3t7eePHiBRo2bIh+/fqhU6dO6NOnD4B3hXXmCQYlS5aEr68vjh49itq1a8PHxwcrV65EuXLl1Lo/IiLKv+Lj47FixQpkZGSIbWXKlMHff//9Rd/gERHleOjC9evXsWPHDpQqVQoAMGPGDLRr1w779u3DgAED4OnpmWUow+eUKlUK69aty3bZnTt3VG7XqVMH+/btU2v/RERUMERERKBTp054/PgxZDIZPD09xWW5/VUmEWmOHBe6b968EYtcALC0tAQA/PHHH3BwcMj1YEREpDksLCxQrFgxPH78GKGhobk+xRARaaYvmnUhk5aWFotcIiL6IkqlUryKmY6ODlatWoWbN2+ia9euEicjosLiqwpdIiIidaWnp2PJkiW4ffs2tm7dKvbc2tjYqHXhISKiz8lxoSsIAiIiIlQu8pBdW/ny5XM3YS7LKF1a6ghERBptzZo18PLyAgBs27YN7u7uEiciosIqx4VuWloa2rRpo9ImCILYljmeKt9foYaTTRMRSWrQoEH4448/UKJECc6mQER5KseFrp+fX17mICKiQio8PBxFixaFmZkZgHcT/+/duxclS5YUx+gSEeWFHBe6derUycscRERUyAiCgM2bN4vTUa5Zs0ZcVprDyIjoG9C4j9Lyx4+ljkBEpBFkMhkuX76MxMREBAYG5vo17ImIPkfjCl2ZQiF1BCIijbFgwQI0a9YMx48fR+XKlaWOQ0QaRuMKXSIiyhuvXr3C5MmTxcu3A4CJiQl27doFe3t7CZMRkabiPLpERPTVnjx5gmbNmuHly5cQBAELFy6UOhIR0Zf16MbGxiIwMBA+Pj5iW1RUVK6FIiKigsXCwgI1a9YEACgUCpX51YmIpKJ2j+7t27cxYMAAKJVKJCcnw8PDA5GRkejQoQM2bNggvtEREVHhlpKSAn19fQDvTjxbvnw5Bg4ciGbNmkmcjIjoHbV7dBcvXowuXbrg0qVL4vyHZcuWxejRo7Fs2bJcD0hERPlLUlISJk6ciLZt2yI9PV1sL1myJItcIspX1C50b9y4AU9PT2hpaYnXJweAPn364NatW7kajoiI8h9/f3+sW7cO169fVxnCRkSU36g9dMHAwEClwM2UkJCQbTsRERUu/fr1Q0BAAMzMzODu7i51HCKij1K7R9fW1hYrV65UaYuPj8e8efPg5OSUa8HySkbZslJHICIqUG7evInIyEjxtlwux/bt27F161Z89913EiYjIvo0tQvd8ePHY9euXWjQoAHS0tLQoUMHNGrUCP/88w9++eWXvMiYu3R0pE5ARFRgeHt7o1mzZhg5ciSUSqXYbmxszG/xiCjfU3vogrW1NQ4fPowDBw4gPDwc+vr6KF++PNq3b48iRYrkRUYiIpJIfHw80tPTceXKFdy+fRu2trZSRyIiyjG1C11vb2906dIFP/74Y17kISKifOSXX37Bixcv4OnpiYoVK0odh4hILWoPXdi9ezdatGiBvn37Ys+ePUhOTs6LXHlG6+FDqSMQEeVLT548wYABA/Dy5UuxTVdXFytWrGCRS0QFktqF7tmzZ7F582ZYWVlhwYIFaNiwISZPnoz//vsvL/IREdE38PLlSzg7O2Pfvn0YO3Ysr2xGRIXCF10CuG7dupgzZw7Onz+PxYsXIy0tDcOHD0fLli1zOx8REX0DJUqUQPfu3SGXy2FjY6Ny4hkRUUGl9hjd9+no6KBKlSoIDw9HZGQkbt++nVu5iIgoj8XExMDc3Fy8PXPmTPTq1YuXcieiQuOLenSfP3+OTZs2oXv37mjRogWOHDmCtm3b4uzZs7mdj4iIcllsbCyGDBkCFxcXxMXFie1FihRhkUtEhYraPbq9evVCcHAwihUrhg4dOmDevHmwtrbOi2xERJQHzp07h4CAAACAl5cXZs+eLXEiIqK8oXahW6ZMGYwaNQoNGjSAXP5FHcJERCShDh06oGvXrtDT08P48eOljkNElGdyVOg+evQIlpaWAICRI0eKbdkpX758LkUjIqLccPHiRRQpUgQ1atQQ21avXg1t7a86TYOIKN/L0bucm5sbbty4AQBo06ZNtpd9FAQBMpkMoaGhuZuQiIi+2IIFC+Dl5QVra2ucPn0a+vr6AMAil4g0Qo7e6TZs2CD+38/PL8/CfAsZ7HEmIg1SunRpCIKA58+fIywsDA4ODlJHIiL6ZnJU6NaqVUv8/6VLl+Dp6ZllncTERCxbtgx16tTJvXR5IZveaCKiwurHH39EdHQ0+vbti++//17qOERE31SOzyZTKpVIS0vDhg0bkJ6ejrS0NJWfR48ewd/fPy+zEhHRJ4SFhaFdu3a4d++e2CaTyTBx4kQWuUSkkXI8SGvt2rX47bffIJPJVE5oeF/VqlVzLVieUSikTkBElOvevn2LVq1aIT4+HiNGjMCRI0c4DpeINF6O3wWHDRsGFxcXdO3aFXPmzMmy3MDAAA0aNMjVcHlB6/FjqSMQEeU6U1NTjBkzBosWLULHjh2zPWmYiEjTqPVx38bGBj4+PnBxccmrPERElAOCICA8PBwVKlQQ2zw8PNC+fXtUqlRJwmRERPlHjgpdb29v8QS0a9eu4dq1ax9dd+zYsbmTjIiIsvXs2TP8/PPP+Pfff3H+/HlYWFgAALS0tFjkEhG9J0eF7uHDh8VC9+DBgx9dTyaTsdAlIspjjx49wsmTJyEIApYtW4alS5dKHYmIKF/KUaF79OhR8f+nTp3KszBERPR59erVg6enJxQKBaZOnSp1HCKifOuLTskNCgqCs7MzAODWrVvYu3cvKlasiF69euVqOCIietfZoKOjg2bNmoltM2bM4AlnRESfkeN5dDP5+vpi0qRJAICYmBj0798fYWFhWL9+PX7//fdcD0hEpMlmzZqFPn36YNSoUYiJiRHbWeQSEX2e2oXurl274OvrCwDYv38/ypYti61bt2L9+vXYv39/rgckItJkmdM2ymQyPHr0SOI0REQFi9pDF16/fo3q1asDAC5cuIDWrVsDAKysrPDy5cvcTZcHBE6gTkT5WEZGBrS0tMTbLVq0gLe3N9q1a4eiRYtKmIyIqOBRu0fX2NgYMTExSEhIwH///Sf2NsTExEBXVzfXA+Y2ZblyUkcgIspW5nvqpUuXVNrd3d1Z5BIRfQG1uzebN2+OAQMGQC6Xw9LSEra2tkhNTcW8efNQt27dvMhIRFToJSYmok+fPnj9+jVGjBiBoKAgGBoaSh2LiKhAU7tHd9KkSWjXrh2cnZ2xZs0aAIBSqcSbN28wbdq0XA9IRKQJihQpgvnz58PIyAjjxo2DgYGB1JGIiAo8tXt0dXV1MWTIEJU2AwMDbNy4MddC5amUFKkTEBEhIyMDV65cQZ06dcS2bt26oWnTpihRooSEyYiICg+1e3SVSiVWrVqFFi1aoFq1aqhWrRratGmDLVu25EW+XKf19KnUEYhIw4WHh6Nt27bo0KEDbt68KbbLZDIWuUREuUjtHl0fHx9s27YNnTt3RqVKlaBUKnH37l14e3tDT0+PF40gIvoMpVKJmzdvIj09HWvWrOEc5EREeUTtQnffvn1YvXo1atasqdLeokULzJs3j4UuEdFnVKxYEXPnzsWbN2/g6ekpdRwiokLri+bRdXR0zNJep04dREVF5UooIqLCQhAE7NixA4IgoE+fPmL7gAEDJExFRKQZ1C50v//+e9y8eRM1atRQab916xaKFy+ea8GIiAqDGTNmYOXKlShSpAjq16+P8uXLSx2JiEhjqF3ourm5YcSIEejXrx8qV64MALhz5w62bt2KLl265HpAIqKCrFu3bvD19UXJkiURHx8vdRwiIo2idqE7ZMgQZGRkYOPGjYiNjQXw7mppPXv2xM8//5zb+YiICpTExEQUKVJEvG1vb48///wT9evXV2knIqK8p3ahq6WlhVGjRmHUqFGIj49HamoqihUrBplMlhf5iIgKjJMnT8LT0xNz585F586dxfbmzZtLmIqISHOpNY/ukydPsGPHDuzevRsxMTEwNjZG8eLFC1SRK+jpSR2BiAqhtLQ0jBs3Ds+ePcP48eMRFxcndSQiIo2X4x7dkJAQuLu7QxAEKJVKLF26FFu3bkXFihXzMl+uU1pYSB2BiAohXV1d/P777xg8eDCWLl0KExMTqSMREWm8HPfo+vj4oHPnzrh69SquXr0KFxcXrFixIi+zERHlWykpKThw4IBKm7OzM65evYq2bdtKlIqIiN6X40L36tWr8PT0hLa2NnR1dTF69GjcuHEjL7MREeVL9+7dg6urK3788UecPHlSZZmBgYFEqYiI6EM5LnTT0tJgbm4u3i5RooQ460JBIktIkDoCERVwRYsWxatXrwAAgYGBEqchIqKPyfEY3YJ0wtmnyF+8kDoCERVwxYsXh7e3N549e4b+/ftLHYeIiD5C7enFiIg0iVKpxOrVq5GYmIgJEyaI7a1bt5YwFRER5USOC920tDT06tXrs207duzInWRERPnA7Nmz4e3tDblcDhcXF9SuXVvqSERElEM5LnQ7duyYZfgCr9lORIXdoEGDsHHjRlSsWBHGxsZSxyEiIjXkuNBduHBhXuYgIsoXXrx4gWLFikFLSwsAUKZMGRw4cADVqlWDjo6OxOmIiEgdal0ZjYioMNu9ezfq1auHVatWqbTb29uzyCUiKoBY6BIRAcjIyMCqVasQGxuLhQsX4uXLl1JHIiKir8RCl4gIgJaWFlauXAlra2v4+/ujRIkSUkciIqKvpHGFrlCkiNQRiCgfiIuLw9q1ayEIgthWpUoVXLhwAQ0bNpQwGRER5ZYvnkc3PT0dz58/R9myZXMzT55TliwpdQQiktidO3fQo0cPREZGwtDQEO7u7uIyuVzjPv8TERVaar+jp6SkYOLEiXB0dESbNm0AvOsZGTRoEOLi4nI9IBFRbitbtiz09PQAAFeuXJE4DRER5RW1C90lS5YgNDQUXl5e4vQ7wLsTOby8vHI1HBFRbnl/iIKhoSFWr16NDRs2YPny5RKmIiKivKR2oXvs2DF4e3urXP7SxMQECxYswF9//ZWr4fKCLDZW6ghE9A2lpaVh3rx5GDVqlEp7zZo10blzZ4lSERHRt6D2GN3ExERYWVllaTc3N0dSUlJuZMpT8pgYqSMQ0Tfk5eWFpUuXAgDatGmD9u3bS5yIiIi+FbV7dMuVK4d//vkHgOpXgUePHsX333+fe8mIiHLByJEj8f3338PZ2Rk1atSQOg4REX1Davfo9unTBx4eHujatSuUSiU2bdqEmzdv4tixY5g6dapa+4qKisKsWbNw48YNGBoaom3bthg3btwnz3qOjo5G69at8dNPP8HDw0Pd+ERUyD18+BClSpWCoaEhAMDU1BRHjhyBhYUFZ1QgItIwar/r9+zZExMnTsSlS5egpaWFNWvWICoqCl5eXujdu7da+/Lw8EDJkiVx4sQJbNq0CSdOnMCWLVs+uc3cuXNVToIjIgLefcO0efNmNG7cGLNnz1ZZVrZsWRa5REQa6Ivm0e3atSu6du36VXccEhKCsLAwbNq0CcbGxjA2Nkb//v2xZcsWDBgwINttzp49i/v376Np06Zfdd9EVDidOXMGSUlJ2Lx5M0aNGoUyZcpIHYmIiCSkdqG7d+/eTy7v1KlTjvZz69YtWFhYwNTUVGyrXr06wsPDkZCQACMjI5X1U1JSMHv2bMybN++zGT4lIyOjQJw0R18nOTlZ5V8q3JKTkyGTyTB79mwkJiZi5syZBeYEWVIfX9+ahcdbswiCAJlMlmv7U7vQnTRpUvY70taGvr5+jgvd2NhYmJiYqLRlFr1v3rzJUuiuXLkSDg4OqFev3lcVugkJ8QgNDf3i7algiYiIkDoC5ZE3b94gICAAAwYMgLb2u7eyxMREcdgCX+eFH1/fmoXHW3Po6urm2r7ULnSDg4NVbmdkZODhw4dYu3Yt+vXrp9a+3p+14VPu37+PXbt24cCBA2rtPztGRsaoWrXqV++H8rfk5GRERETAysoKBgYGUsehXPbw4UMMHz4cr1+/RpkyZTB8+HAebw3C17dm4fHWLPfu3cvV/ald6GZXZVevXh3Tp0/HwIEDsW/fvhztx9zcHLEfXLwhNjYWMpkM5ubmYpsgCJg5cyY8PDxQokQJdeNmITM3F8/GpsLPwMCAx7sQqlq1KqytrXHx4kUkJiaKf/x4vDULj7dm4fHWDLk5bAH4wpPRsmNiYoJHjx7leH1bW1s8e/YMMTExYmEbEhKCSpUqoUiRIuJ6T58+xX///Yd79+7B29sbAJCUlAS5XI5Tp05hz549auUUihVTa30iyh/S0tLED9paWlpYuXIlwsPD4eLiwrG4RESULbUL3aCgoCxtKSkpOHz4MEqVKpXj/VSrVg12dnZYunQpJk+ejOjoaGzatAk//fQTAKB169aYO3cuHB0dcfbsWZVtFyxYgFKlSmHQoEHqxieiAiYhIQEzZ87E/fv3ERgYKE4TZmVlle1VGomIiDKpXegOGjQIMpksy/haMzMzLFy4UK19eXt7Y/r06WjYsCGMjIzQq1cv9OnTBwAQHh6OpKQkaGlpZSmgDQwMYGRklCtDGYgof9u4cSM2btwIANi0aRMGDhwocSIiIioo1C50T548maVNX18f5ubmao+rKFWqFNatW5ftsjt37nx0O3UL6vfJXr364m2J6NsbNmwYAgICYGVlBTc3N6njEBFRAaJ2obt582a1L/Wbn8jj4qSOQESfEBwcjNKlS4vf2Ojq6uLAgQMwMTHJ9ZMUiIiocFP7mphHjhzB27dv8yILEWkwQRDg5eWF5s2bY9y4cSrDo0xNTVnkEhGR2tTu0Z0wYQImT56Mrl27omzZstDR0VFZXr58+VwLR0SaQyaTITo6GgqFAqdOnUJ4eDgqVKggdSwiIirAvqjQBYBTp06p9LBkXrKNVyMioi81c+ZMxMXFYeLEiSxyiYjoq6ld6Pr5+eVFDiLSMI8fP8a8efOwZMkS8XLgRYoUga+vr8TJiIiosMhxoWtvb48bN26gTp06eZmHiDRAZGQknJ2dkZCQAF1dXfj4+EgdiYiICqEcn4z24by5RERfqmzZsmjZsiW0tLRQunRpvr8QEVGeyHGPLs94JqKv8fbtW5iamoq3lyxZguHDh6NmzZoSpiIiosIsx4VuRkYG/P39P9nzIpPJ0KNHj1wJlleUxYpJHYFIo8TExGD8+PEIDQ3F6dOnoa+vDwAoWrQoi1wiIspTOS50FQoFZsyY8cl1CkKhK7zXo0REee/QoUPYu3cvAMDHxwe//PKLtIGIiEhj5LjQ1dPTw40bN/IyCxEVQu7u7ti3bx8sLCwwbNgwqeMQEZEGUXt6MSKiTwkKCkKpUqVQqVIlAO++6fnzzz+hq6srcTIiItI0Gjfrgvz5c6kjEBVas2bNgpubG4YPHw6FQiG2s8glIiIp5LjQ7dixY17m+GZkSUlSRyAqtDIv/PDw4UPcu3dP4jRERKTpcjx0Yc6cOXmZg4gKAQ8PD7x9+xbDhg1DqVKlpI5DREQaLsc9ukRE77t9+zY6deqEqKgosU1bWxszZ85kkUtERPkCC10iUtuLFy/QokUL/P333/j5558LzRh+IiIqXFjoEpHavvvuOwwaNAj6+vpo1qwZC10iIsqXOL0YEX2WIAh48uQJypYtK7ZNnjwZ7u7uqFy5soTJiIiIPo49ukT0SU+fPkX37t3RsmVLxMTEiO36+voscomIKF9joUtEn3Tr1i2cOnUK0dHRWLFihdRxiIiIckzjhi5klCwpdQSiAqVFixbo378/TExMMHnyZKnjEBER5ZjGFbooUkTqBET52uHDh1GsWDHUrVtXbFu6dClkMpmEqYiIiNSneYUuEX3UpEmTsHbtWlhZWeHvv/+GkZERALDIJSKiAoljdIlI5OTkBABITU3F48ePJU5DRET0dTSuR1f+5InUEYjyDaVSCbn8f593u3fvjrdv36JHjx4wNTWVMBkREdHX07geXVlamtQRiPKFf/75B87Ozrh165bYJpPJMHjwYBa5RERUKGhcoUtEQGxsLLp3746wsDAMGzYM6enpUkciIiLKdSx0iTSQmZkZpk2bBmNjY4wcORLa2ho3iomIiDQAC10iDaBQKHDjxg2VtkGDBuGff/5Br169OKsCEREVSix0iQq5+/fvo02bNmjfvj3Cw8PFdrlcjlKlSkmYjIiIKG+x0CUq5BISEnDjxg0kJibC19dX6jhERETfDAfmERVyDg4OmDx5MuRyOUaNGiV1HCIiom+GhS5RISIIArZt2wYzMzO0b99ebB8zZoyEqYiIiKShcYVuhoWF1BGI8swvv/yCjRs3wtzcHLVr10bJkiWljkRERCQZzRujq6cndQKiPNOxY0cAgLm5OV6/fi1xGiIiImlpXI8uUWGSnJwMAwMD8XajRo3g5+cHV1dXGBoaSpiMiIhIeprXo0tUSBw/fhy1atXCqVOnVNrbt2/PIpeIiAgaWOhqRURIHYHoqyUmJmLUqFF49uwZPD09kZycLHUkIiKifEfjCl0olVInIPpqRYoUwbJly/Ddd9/By8tLZfgCERERvaN5hS5RAZScnIzjx4+rtLVr1w6XL19G69atJUpFRESUv7HQJcrnbt++DRcXF/Tu3RuXL19WWWZkZCRRKiIiovyPhS5RPmdsbIynT59CqVTC399f6jhEREQFBqcXI8rnypYtCy8vLyQnJ6Nfv35SxyEiIiowWOgS5SMZGRlYvXo1dHV1MWTIELG9R48eEqYiIiIqmFjoEuUjkyZNwoYNG6Cnp4dGjRqhatWqUkciIiIqsFjoEuUjAwcOxB9//IGqVatCW5svT/p6giAgPT0dGRkZUkfJNampqeK/MplM4jSU13i8Cw8tLS3o6Oh80+OocX9JMywtpY5AJHr16hWKFSsmvuirVKmCffv2wdHRETo6OhKno4JOoVDgzZs3MDAwgJaWltRxco2uri7Kly8PXV1dqaPQN8DjXXikpaXh7du3KFq06DfrzNG4QheF6M2eCi5BEBAQEIAJEyZg7ty5+OGHH8RlderUkTAZFRaCIODNmzcoXrx4oesFy+yd1tfXL1QFPGWPx7twKVKkCF69evXN3ps4vRiRBNLS0rBkyRK8ffsW06ZNQ1xcnNSRqJBJT0+HgYFBoStyiahgk8lkMDAwQHp6+je5P80rdHkJYMoH9PT0sHLlSlSsWBF//vknTExMpI5EhUxGRgZ7v4goX9LS0vpm5w1oXKGrFREhdQTSQHFxcdi6datKW+3atXHx4kXUr19folRERESFm+aN0SX6xm7duoVevXohKioKxYoVQ9u2bcVlnFmBiIgo72hcjy7Rt1amTBko/3/IzLlz5yROQ0S5JSkpCf369YO9vT2uXLmi1rY2Njb4+++/8ygZEWVioUuUBwRBEP9vamqKVatWYdOmTViwYIGEqYgKBldXV1SvXh12dnbij6urKxYsWIDExESVdW/evIlRo0ahXr16sLe3R/PmzbF48eJsT/AMCgpC//79UbNmTTg6OqJDhw7YtGmT+EFUXefPn8e1a9dw+vRp1KxZ84v28S0dPnwYHTp0gKOjI7p06YKgoCC1to+Pj4e9vT3at2+fZVlgYCAaNmyY7XY9evSAj4+PStvBgwfRq1cvODo6ombNmujWrRt2796tVp7PefXqFQYOHAgbGxtxLt6PuXjxIrp16wYnJye0a9cO+/fvV1nu5+eHVq1awcnJCb1798bNmzdzNSvlHRa6RLkoNTUVc+bMwYwZM1TamzRpgo4dO0qUiqjgmTZtGkJCQhASEoLg4GD4+vri/PnzWLRokbjOhQsX4O7ujho1auDo0aO4fv06fH19cf/+ffTu3RsJCQniurt27YKHhwfc3NwQFBSES5cuYcKECdiyZQsmT578RRkTEhJgYmICc3Pzr368eS00NBQTJ07E+PHjcenSJfTv3x+jRo3C8+fPc7yP/fv3w8nJCS9fvsSNGze+OMtvv/2G+fPnY9CgQfjnn39w7tw5/PTTT1iwYAG8vb2/eL/vu3PnDrp16wYzM7PPrvvixQuMGDECvXr1wsWLFzF16lRMnz4dISEhAIBTp07Bx8cHixcvxoULF+Di4oJhw4YhKSkpV7JS3mKhS5SLZs2aheXLl2PlypUcpkCUS2QyGSpXrozBgwfj+PHjAAClUolZs2bhhx9+wJAhQ2BmZgaZTIaKFSvi999/R3JyMnx9fQG8Oxl0/vz5GD9+PLp06QIDAwPxMtve3t4wMjJCWlpatvd94sQJuLm5wcHBAa6urvDz8wPwrnCeNm0aXr16BTs7O/z3339Zto2JiYGnpydq1qwJZ2dnLFu2TOXbng/Xq1+/PmrVqoXBgwfj2bNn4vK1a9fCxcUF9vb2aNWqFfbt2yc+BwsXLoSzszMcHBzg5ub20fedXbt2oUmTJmjSpAn09PTg5uYGa2vrLD2Xn7J79260bdsWLVq0+OLe1/DwcKxZswbz589H8+bNoaurC0NDQ7Rt2xaLFy/O9vkBgM6dO8PBwUGll9/Ozg6tWrXKdv2YmBgsW7YMPXr0+GymAwcOwMrKCt26dYOenh4aNGgAV1dX7Nq1CwCwc+dOdOnSBfb29tDX18egQYMAAKdPn/6i54C+LZ4JQ5SLPD09sXPnTtjZ2aF8+fJSxyHK4m1yGsJevP1m91flO1OYGuTOFa3en3czLCwMkZGR6NevX5b1dHV10atXLwQEBGDcuHEICgqCQqFA9+7ds6xbo0YN1KhRI9v7CwsLw88//4wVK1agSZMmuHz5MoYNGwZLS0t0794dWlpaWLp0Kc6fP5/t9tOmTYO2tjbOnj2L2NhYuLu7o0yZMlmKryVLliAxMREnT56EIAgYPXo05s+fDx8fH1y9ehV+fn7w9/dH6dKlcf78eXh4eMDZ2RkXLlzAhQsXsH//fpiammLv3r2YOHEizp49m+XKirdu3UKTJk1U2qpVqyb2Wn5OaGgo7t27h9atW8PS0hLDhw/H5MmTYWBgkKPtM504cQIWFhZo2rRplmWurq5wdXXNdrs9e/aodcGIzNls/vnnn8+ue+vWLVSrVk2lrVq1ajhy5Ii4/P2TiOVyOapWrYqQkBC0a9cuR3lIOix0ib5CeHg4ypQpI/5RKVWqFP766y+UL18ecjm/MKH85W1yGirM24PY5Ox7L/OCmYEuHk7t/FXFrlKpxJ07d7Bu3Tp06NABABAZGQkDAwOULFky220qVKiAJ0+eQBAEPHnyBBYWFmpfQnb37t2oX78+mjdvDuBd8dS0aVMcPnw4S9H4oTdv3uD06dPYvXs3jIyMYGRkhOXLl2c708qsWbOgUChgaGgIAGjevDnWrFkD4N24WLlcDn19fchkMjg7O+PKlSuQy+WIi4uDtra2eInnrl27onPnztm+98TGxsLU1FSlzdTUFPfv38/Rc7Fr1y64uLjA2NgYtWvXhqmpKY4dO4ZOnTrlaPtMkZGRsLKyUmubvBYbG5vl98jMzAxv3rwRl2f33GUup/xN8/4Ss/igXKBUKrF27Vo4Oztj+fLlKssqVqzIIpfoK82dO1flK+q+ffuibdu2mDBhgrhORkbGR7/qFgRB5apwX3LC2ZMnT1CxYkWVNktLS0RFReVoW6VSiTJlyohtjo6OsLOzy7Luo0eP4OHhgdq1a8POzg6zZ88Wh1LUr18f1apVg6urK4YPH47t27cjJSUFANCuXTtoa2ujcePGGD16NPbu3fvJSfg/9lx9TmpqKg4cOCCeZyCTydChQwcEBASovS+ZTPbFJ/9J6UufO5KexvXoZuSzT5JUMAmCgH379iE5ORne3t4YOHAgihUrJnUsok8y/f/e1YIwdGHatGno3bs3gHezJYwcORIdO3aEtrY2MjIyYGVlhbS0NERGRqJcuXJZtg8PD4eVlRVkMhmsrKwQFRWFpKQksdc0Jz42bjcnl1XO/LD7uaJOqVRi6NChqFmzJo4dOwZzc3Ps2rULv/32G4B3wzDWrFmDsLAwnDx5Etu2bcPGjRsRGBgIMzMz+Pv74+rVqzh9+jS8vb2xfft2bNu2LUvPcdGiRREbG6vSFhsbm6MT6Y4ePYq4uDiMGzdOfOwZGRlIS0vD48ePUa5cOejo6CA5OTnb7ePj46GnpwcAsLKywunTp7N8EPmczp07q4xbzvT999/j2LFjOd5PdrJ7bt68eSM+Nx977ipXrvxV90vfhsYVukS5QUtLC7///jtGjBgBLy8vFrlUYJga6KKuZQmpY6jF2dkZzZo1w/Tp08WTwaytrWFlZQU/Pz9MmzZNZX2FQgF/f3/xa/UGDRpAX18ffn5+GDZsmMq6d+/exejRo7Fjx44sl+IuV64cHj58qNL28OFDlC1b9rOZLSwsIJfLER4eDkdHRwDApUuXkJSUpDIO9dWrV4iKisJvv/0mFla3b98Wl6enpyM1NRVVqlRBlSpVMHToULRt2xYXLlxA06ZNoVQq4eTkBCcnJwwfPhwNGzZEWFgYbG1tVfLY2tpmmRIrp2NMAwIC0LVrVwwdOlSlffTo0di9ezfGjBmDChUqIDExEY8ePYKlpaW4TkxMDB4/fgwbGxsA74ZlLFmyBAcPHhSHoWQKCgqCt7c3/vzzzyyFurpjdNVhZ2eX5eS6mzdvwt7eHsC75+7WrVvo3LkzgHdF/u3bt9GtW7dcz0K5j9+vEuVAdHQ0Fi5cqNI7U758eRw+fBjVq1eXMBmRZpgyZQrCwsKwc+dOAO96VWfMmAF/f394eXkhJiYGgiDgwYMHGDBgAIyNjTFw4EAAgJGREaZMmQIfHx/4+voiISEBaWlpOHv2LAYPHgwXF5csRS4AuLm54fz58zh9+jQUCgXOnTuHM2fO5GhcqpmZGZo1a4aVK1ciNjYWT58+xfTp0xEdHa2ynrm5OQwNDXH9+nVxiEBoaCgSEhKQmJiIjRs3YvDgweI0YA8ePMDbt29Rrlw5zJs3DxMnThQf+61bt6BUKvH9999nydOjRw9cuHABZ86cQWpqKgICAhAREQE3NzcAwPHjx9GnT58s2z169Aj//fcffvjhB1haWqr8dOvWDXv27EFGRgaqV68OZ2dnTJw4EQ8ePEBGRgYePHiAsWPHol69emjcuDEAoGzZshgxYgSmT5+OXbt2ISUlBcnJyTh48CDGjBkj9trntQkTJmDTpk0AgA4dOiAqKgq7du1Camoqzp49i7Nnz4onDfbu3Rt79+7F9evXkZycjNWrV0NXVzfbE+ooHxI0RHBwsPDf/n3C8c1rpI5C30BiYqJw+fJlITEx8av3dfPmTaFixYpC0aJFBV9f31xIR7ktN493YZGUlCQkJSVJHeOLuLi4CH/++WeW9j///FNwcnISoqKihISEBEGhUAjBwcHC0KFDhTp16gh2dnZC8+bNBS8vLyE+Pj7L9mfPnhXc3d0FR0dHwcnJSejSpYuwZ8+eT2YJDAwU2rVrJzg4OAjt27cXjhw5Ii7bvXu30KBBg49uGxcXJ3h4eAgODg6Cs7Oz4OXlJWRkZAiCIAjW1tbC2bNnBUEQhL179woNGzYUnJychOnTpwsvXrwQWrZsKTRo0EBITU0VZsyYIdStW1ewt7cXWrduLfj7+wuCIAixsbHCzz//LNSqVUtwcHAQOnbsKBw/fvyjeY4dOya0bNlSqF69utCxY0fh33//VXksDRs2zLLN0qVLBTc3t2z3FxsbK9jZ2QlnzpwRBOHd75y3t7fQsmVLwd7eXmjevLkwe/ZsIS4uLsu2e/fuFbp16yY4ODgItWvXFn744Qfh9OnT2d6PQqEQj3dOTZ06VbC1tRWqV68uWFtbC7a2toKtra14vN3d3YUlS5aI6//777+Cm5ubUL16daFly5bCsWPHVPa3bds2oUmTJoKtra3Qu3dv4c6dOznOQll96v3pxo0bQnBwcK7dl0wQNGOEdUhICFIjwhEfchkuU2ZLHYfyWFJSEkJDQ1G1alW1xuRlJzU1Fc2bN8etW7fg6emJmTNn5k5IyjW5ebwLi8zxkupO/1QQZGRkICUlJc++ytZUAwcOxIYNG6SOkQWPd+Hzqfen4OBgyGSybE/c/BIco0uUDYVCIX59pqenB19fX7x8+fKzUwoRERVEt27dgoWFhdQxiHIdC12i9yQkJGD69OmIiYnB5s2bxbOCP5xMnIioMKlevTpmz+a3nVT48GQ0ovesWLECW7ZswYEDBxAYGCh1HCIiIvoKLHSJ3vPzzz/DysoKbm5uPKOWiIiogOPQBdJoN27cQPny5cWphYyMjHD8+HGYm5urNZk5ERER5T/s0SWNpFQqsWjRIrRo0SLLZPPFihVjkUtERFQIsNAljSSXy3H37l0oFArs2bMHT58+lToSERER5TKNG7og6OhIHYHyiSVLlkChUGDmzJnZXkmIiIiICjaN69FV5uAa5VT4REREwNPTEykpKWKbubk5tmzZgvLly0uYjIiIiPKKpIVuVFQUhgwZgrp168LFxQVLliyBUqnMdt3t27ejVatWcHR0RMeOHXHixIlvnJYKqjt37qBRo0b4448/sGDBAqnjEFEhkZSUhH79+sHe3h5XrlxRa1sbGxv8/fffeZSMiDJJWuh6eHigZMmSOHHiBDZt2oQTJ05gy5YtWdY7duwYli5divnz5+Pff/+Fu7s7Ro8ejcjISAlSU0FjbW2NOnXqQEtLC0WKFJE6DhF9hqurK6pXrw47Ozvxx9XVFQsWLEBiYqLKujdv3sSoUaNQr1492Nvbo3nz5li8eDHi4uKy7DcoKAj9+/dHzZo14ejoiA4dOmDTpk0f7WD5nPPnz+PatWs4ffo0atas+UX7+JbS09OxaNEiVKlS5YuK7PDwcFSpUgVDhgzJsszHxwc9evTIdruGDRuqzEuuVCrxxx9/oFOnTnB0dESdOnXQt29fnDx5Uu1Mn/Lo0SN06dIFDRs2/Oy6hw8fRocOHeDo6IguXbogKChIJe/y5cvRrFkz1K5dGwMHDmT9UYBIVuiGhIQgLCwM48ePh7GxMaysrNC/f3/s3Lkzy7opKSkYO3YsatasCR0dHXTv3h1FihTB9evX1b/jpKSvD0/5miAISEhIEG/LZDJ4e3vjr7/+woQJEyRMRkQ5NW3aNISEhCAkJATBwcHw9fXF+fPnsWjRInGdCxcuwN3dHTVq1MDRo0dx/fp1+Pr64v79++jdu7fK+8CuXbvg4eEBNzc3BAUF4dKlS5gwYQK2bNmCyZMnf1HGhIQEmJiYwNzc/Ksfb15LSkpCnz59EBsbC0EQvmgfu3btQsuWLXHx4kVER0d/cZZJkybBz88PEydOxOXLl3Hy5Em0adMGo0ePxu7du794v++7ePEi3N3dUaZMmc+uGxoaiokTJ2L8+PG4dOkS+vfvj1GjRuH58+cAgG3btuHAgQNYu3YtTp8+DSsrK4wcOfKLn0f6tiQ7GS3zutqmpqZiW/Xq1REeHo6EhAQYGRmJ7R07dlTZNi4uDomJiShZsqT6dxwVhSQWu4VWTEwMJk+ejKdPn2LBggVITk4GABQtWhRFixblsS+kMo9z5r8EpKamQldXFxkZGVJHUZsgCFAqlSrZK1SogIEDB2Lx4sWYMWMGlEolZs2ahT59+mDgwIEA3vW8WVlZYcWKFWjbti1Wr16NsWPHIi4uDvPnz8fYsWNV/p40aNAAy5cvx/79+5GcnAxdXd0sWU6cOIGVK1fi8ePHKFq0KH788Uf07dsXAQEBmD17NhQKBezs7LB+/XrUqlVLZduYmBjMnj0bFy5cgL6+Pjp37ozRo0eL0xdmPsY3b95g1qxZuHz5MtLT0+Hg4ICZM2eidOnSAID169dj+/btePPmDUqVKoVhw4bBzc0NSqUSS5YswaFDh5CYmIhy5cph3LhxcHZ2zvI44uPj0blzZ/Ts2ROBgYFZnt/PUSgU2Lt3LxYtWoS4uDgEBgaq9OwqlUoIgpDtPt8/nhcuXMD+/fuxZ88eWFtbAwAMDQ3Rs2dPaGtrIyEhIcs+BEFAvXr1ss1Vq1YtrF+/Pkt7TEwMNmzYgODgYFy+fPmTj9Xf3x+NGzcWn7d27dph69at2Lt3LwYPHowdO3agX79+sLKyAvDuwkL169fHtWvXYG9v/+knjrKlVCqRlpaW7YcFQRBydYpPyQrd2NhYcZL+TJlF75s3b1QK3fcJgoBp06bB3t4ederUUft+ExLiERoaqn5gKhACAgJw6NAhAMCBAwfQqVMnaQPRNxURESF1hHwluxMt0zNSEJfy+ptlMNEvBh0tfbW2EQQBCoVC5eRR4F2vpCAISE1NRVhYGCIjI9G9e/cs6wFAt27dsGfPHowYMQJnzpyBQqFAhw4dsqxrbW2N8ePHQ6lUZll29+5djB07FosWLYKzszOuXbuG0aNHo3Tp0mjfvj2USiV8fHxw/PhxAMiy/bRp06CtrY3Dhw8jLi4OgwYNQsmSJdGlSxcAQFpaGlJSUsTi8cCBAxAEARMnTsS8efPg5eWFGzduwM/PD1u2bEGpUqVw6dIljB8/HrVq1cI///yDCxcuYOfOnTAxMcHBgwcxadIkHDlyBDofzDBkZGSEjh07ihkz7zunTp06BblcDgcHB7Ru3RobNmxAv379xOUKhSLb5zBTeno6UlJScOzYMdSsWRPlypXLsm67du2yfR4B4NKlSx/Nlt36mVe2zBw7/anHGhISgkaNGqmsY2Njgxs3biA2NhYPHjxApUqVxOVaWlooW7Ysrl27Bhsbm4/ulz4uNTUV4eHhH12e3YfOLyXp9GLqdvunp6dj0qRJuH//Pvz8/L7oPo2MjFG1atUv2pbyv8mTJ+O///5D5cqV0bJlS1hZWcHAwEDqWJTHkpOTERERweP9nsweXX39/xWZaYoUHA72QVpGzgucr6WrpY/OTr9AVzvnxa5MJoO2traYXalU4s6dO/Dz80OHDh2gp6eHyMhIGBgYoFy5ctnuo1KlSnj69Cn09PQQHR0NCwsLGBsbq5X90KFDqFevHtq0aQMAaNy4MZo0aYKTJ0+iefPmYjH5/nOcKTY2Fn///Tf8/f1RvHhxFC9eHMuWLVN5XJnHJ7Nn2NDQEADQokUL+Pr6Ql9fHykpKdDS0oKZmRkMDAzg4uKC//77D3K5HCkpKdDR0RGX9ezZE927d4dc/vlRiR/+bnzOgQMH0L59exgaGqJt27ZYuHAhbt68KfZia2trQy6Xf3SfOjo60NfXx7Nnz1ChQgW17jvzw42enp7aPX2fOkaZ4uLiYG5urrKOubk5wsPDxV7H4sWLqywvWrQo4uPj1XocpKpChQrQ09PL0n7v3r1cvR/JCl1zc3PExsaqtMXGxkImk2U73iklJQUjRoxAcnIytm3bhqJFi37R/WppaYlvJlTwnTt3DhUqVICFhYXYdvDgQSiVSoSGhsLAwIDHW4PweP9PZkGgpaUltmkJWh9bPU9paWmp5PgcmUyG+fPnY+HChQDeFboGBgbo27cvRo4cKT62jIwMyOXybIufzHYtLS3I5XIolUq1MgDvZgaqVKmSynZWVla4evWquN/M+/jQ06dPoVQqYWlpKS7/8IQ1uVwOLS0tPHnyBAsXLkRwcDBSUlKgVCphZmYGLS0tODs7o1q1amjevDnq16+Pxo0bo2PHjjA0NET79u2xf/9+uLi4oGHDhmjatCnatWuXo8eZed85ER0djaCgIIwdOxZaWlowMTFB8+bNERgYiLp164r7+9hzIZPJxPuTyWQQBEGtY5E57OBj+/+UT+V6P9+H62S2ZX5oyO75Uuc5JFWZH4qy65jI7SuTSlbo2tra4tmzZ4iJiREL25CQEFSqVCnLmfGCIGDMmDHQ1tbG5s2bs/0EQJpFEARMnToVa9asgaurK3bt2iW+OPT19TkWlygbutr66FZ7Et4mv/hm92lq8J1avbmZpk2bht69ewN4N1vCyJEj0bFjR2hrayMjIwNWVlZIS0tDZGRktr264eHhsLKygkwmg5WVFaL+//wMdT4IpaWlZduekz/EmQXS52Z0UCqVGDp0KGrWrIljx47B3Nwcu3btwm+//QbgXc/rmjVrEBYWhpMnT2Lbtm3YuHEjAgMDYWZmBn9/f1y9ehWnT5+Gt7c3tm/fjm3btkFbO/f+vO/evRsZGRni8QDefcOqra2N6dOnw8jICDo6OtmOkRcEAYmJiWLPp5WVFW7evKl2ho+N0a1duzY2btyo9v7eV7Ro0Ww73szNzWFmZga5XJ7t8mLFin3V/dK3IdmsC9WqVYOdnR2WLl2KhIQEPHjwAJs2bRJfSK1bt8bly5cBvPvK5P79+1ixYgWLXALwv682AeD69et49OiRxImICgZdbX2UMC73zX6+pMj9kLOzM5o1a4bp06eLQ96sra1hZWWV7TA2hUIBf39/cchBgwYNoK+vn+26d+/eRdu2bbOdjqxcuXJ4+PChStvDhw9RNgcXHrKwsIBcLlcZh3jp0iWcOnVKZb1Xr14hKioKffv2FTt9bt++LS5PT09HQkICqlSpgpEjR2Lv3r2QyWS4cOECUlNTkZycDCcnJ4wbNw4HDx7E3bt3ERYW9tl8OSUIAgIDAzFixAjs3btX/Dl48CCKFCmCw4cPA3j3NfTjx49VZroAgLCwMCQnJ4snnrVs2RLXrl3D1atXs9zXzp074eHhkW2OS5cu4fr16+JMHJk/X1vkAu863j4svkNCQmBvbw89PT1UrlwZt27dEpfFxcXh8ePHqFGjxlffN+U9SefR9fb2xosXL9CwYUP069cPnTp1Qp8+fQC8+zSe2Su3e/duREVFoU6dOirzKk6bNk3K+CSxKVOmYMiQITh//rx4NiwRFU5TpkxBWFiYOAWlTCbDjBkz4O/vDy8vL8TExEAQBDx48AADBgyAsbGxOBuDkZERpkyZAh8fH/j6+iIhIQFpaWk4e/YsBg8eDBcXlywnRwOAm5sbzp8/j9OnT0OhUODcuXM4c+ZMjk5yNTMzQ7NmzbBy5UrExsbi6dOnmD59epZpuczNzWFoaIjr168jNTUVBw4cQGhoKBISEpCYmIiNGzdi8ODB4lRXDx48wNu3b1GuXDnMmzcPEydOFB/7rVu3oFQqv+iS5n/88QfGjBmTpf3SpUt49uwZfvjhB1haWoo/VlZWcHNzQ0BAAADAxcUF5cqVwy+//IKoqChkZGTg5s2bmDx5Mnr06IFKlSoBAOrUqYMuXbpg+PDh+Ouvv5Ceno74+Hj88ccfWLhw4Tc7gfjHH38Ui/QePXrgwoULOHPmDFJTUxEQEICIiAi4ubkBAHr37g0/Pz88ePAACQkJ8PLyQtWqVWFnZ/dNstJXEjREcHCw8N/+fcKpedOljkJf4ObNm0K3bt2EmJiYHK2fmJgoXL58WUhMTMzjZJQf8HhnlZSUJCQlJUkd44u4uLgIf/75Z5b2P//8U3BychKioqKEhIQEQaFQCMHBwcLQoUOFOnXqCHZ2dkLz5s0FLy8vIT4+Psv2Z8+eFdzd3QVHR0fByclJ6NKli7Bnz55PZgkMDBTatWsnODg4CO3btxeOHDkiLtu9e7fQoEGDj24bFxcneHh4CA4ODoKzs7Pg5eUlZGRkCIIgCNbW1sLZs2cFQRCEvXv3Cg0bNhScnJyE6dOnCy9evBBatmwpNGjQQEhNTRVmzJgh1K1bV7C3txdat24t+Pv7C4IgCLGxscLPP/8s1KpVS3BwcBA6duwoHD9+PNsse/bsEWxtbQVbW1vB2tpaqF69umBraytMnTpVEARB8Pb2Fnr27Jllu7FjxwrDhw/Pdp/3798XrK2thXv37gmCIAgxMTHCnDlzBBcXF8HBwUFo3bq18Ntvvwmpqakq22VkZAibN28W2rdvL9SoUUOoW7euMGjQIOHKlSvZ3o9CoRCPd04NGDBAsLW1FapVqyZYW1uLj/3ff/8VBCHr79ixY8eEli1bCtWrVxc6duworicIgqBUKoUVK1YI9evXF2rUqCEMHjxYePbsWY6zUFafen+6ceOGEBwcnGv3JRMEzZjxOCQkBKkR4Yh7cBuuoydJHYfU8PjxY9SpUwdpaWno0qVLtnMmfigpKQmhoaGoWrUqT07SADzeWWWOlyyMs1BkZGQgJSUF+vr6PBkol6SlpeHnn3/G6tWrpY6SBY934fOp96fg4GDIZLJc6zGXdOiCFJSlSkkdgdRUrlw59OjRAwYGBqhTpw6vRkNElMvOnDkDR0dHqWMQ5TpJ59Elyo5SqUR0dLR4VSAAmDt3Ljw8PFC5cmUJkxERFU4tW7aUOgJRntC4Hl3K3548eYKuXbuiffv2KmfvmpiYsMglIiIitWhcoSt7+1bqCPQJFy5cwNmzZxEeHp4vx4oRERFRwaFxQxfkr7/dNd5Jfd27d8eRI0dQoUIFeHp6Sh2HiIiICjCNK3Qpf9m/fz8qVaqEatWqAXg3N+aGDRtydK12IiIiok9hNUGSGT16NPr3749hw4apXGqTRS4RERHlBlYUJJkqVaoAAN68eYPHjx9LnIaIiIgKGxa69M18OP/tkCFDMHPmTJw/f168PCQRUUGRlJSEfv36wd7eHleuXFFrWxsbG/z99995lIyIMrHQpW/i0qVLaNy4McLDw8U2uVwOT0/PbK8xT0Say9XVFdWrV4ednZ344+rqigULFiAxMVFl3Zs3b2LUqFGoV68e7O3t0bx5cyxevBhxcXFZ9hsUFIT+/fujZs2acHR0RIcOHbBp0yYolcovynn+/Hlcu3YNp0+fRs2aNb9oH9/SX3/9BTc3Nzg6OqJVq1bw9/dXa3ulUokmTZqgbt26KsPNAOCff/6BjY0NUlNTs2w3ZswYTJqkekXS3D4W2UlMTMT48eNhY2ODBw8efHLd0NBQuLu7o2bNmmjZsiU2btyosvzw4cPo0KEDHB0d0aVLFwQFBeVaTspbLHQpzz1//hydOnXCrVu3MHLkyFx9IyOiwmnatGkICQlBSEgIgoOD4evri/Pnz2PRokXiOhcuXIC7uztq1KiBo0eP4vr16/D19cX9+/fRu3dvlbm4d+3aBQ8PD7i5uSEoKAiXLl3ChAkTsGXLFkyePPmLMiYkJMDExATm5uZf/XjzWnBwMMaPHw9PT0/8999/mDJlCmbPno3Lly/neB/nzp2DgYEBSpcujRMnTnxxlrw4Fh+Kjo5Gly5dcnTJ4JSUFAwdOhT16tXDuXPnsHz5cvj6+uKvv/4C8K4InjhxIsaPH49Lly6hf//+GDVqFJ4/f54rWSlvsdClPFeqVCmMGTMGpqamGDBgAGQymdSRiKgAkclkqFy5MgYPHozjx48DeNe7OGvWLPzwww8YMmQIzMzMIJPJULFiRfz+++9ITk6Gr68vACAuLg7z58/H+PHj0aVLFxgYGEBPTw+NGjWCt7c3jIyMsvRQZjpx4gTc3Nzg4OAAV1dX+Pn5AXhXrE2bNg2vXr2CnZ0d/vvvvyzbxsTEwNPTEzVr1oSzszOWLVuW7SXMM9erX78+atWqhcGDB+PZs2fi8rVr18LFxQX29vZo1aoV9u3bJz4HCxcuhLOzMxwcHODm5oZz585l+zhiY2MxdOhQNG/eHNra2mjSpAmsra3VKnQDAgLQqlUrtGrVCrt3787xdu/70mMxYsQIODg4qPTyZ/5k582bN/jll1/g4eHx2UxnzpxBeno6hg8fDkNDQ1SvXh3du3fHzp07Abw71k2aNEGTJk2gp6cHNzc3WFtbY//+/V/0HNC3pXHTiymNjaWOUOilp6fj3r174pRhADB27Fj069dP5bK+RPTtKd6+RcrdsG92f/rWVaBtapor+0pPTxf/HxYWhsjISPTr1y/Lerq6uujVqxcCAgIwbtw4BAUFQaFQoHv37lnWrVGjBmrUqJHt/YWFheHnn3/GihUr0KRJE1y+fBnDhg2DpaUlunfvDi0tLSxduhTnz5/Pdvtp06ZBW1sbZ8+eRWxsLNzd3VGmTBn06NFDZb0lS5YgMTERJ0+ehCAIGD16NObPnw8fHx9cvXoVfn5+8Pf3R+nSpXH+/Hl4eHjA2dkZFy5cwIULF7B//36Ymppi7969mDhxIs6ePQsdHR2V+2jcuDEaN24s3lYoFHj58iVKliz58Sf8Pa9fv8bp06cxevRo6OnpwcfHB0+fPsX333+fo+0zfemxWLVqFfT19XPUQwu8O9m5SpUqePLkyWfXvXXrFmxsbFT2Xa1aNezatUtc3qRJE5VtqlWrhpCQkBxlIWlpXKErlCghdYRC7c6dOxgxYgQePXqE8+fPi2+iOjo6LHKJJKZ4+xbB1SsiIzb2m92nlpkZatx68FXFrlKpxJ07d7Bu3Tp06NABABAZGQkDA4OPFmoVKlTAkydPIAgCnjx5AgsLC+jq6qp1v7t370b9+vXRvHlzAED9+vXRtGlTHD58OEvh86E3b97g9OnT2L17N4yMjGBkZITly5dDWzvrn91Zs2ZBoVDA0NAQANC8eXOsWbMGABAfHw+5XA59fX3IZDI4OzvjypUrkMvliIuLg7a2NgwMDKClpYWuXbuic+fOOZqi0cvLC4aGhmjbtm2Onou9e/fCxsYGFStWBAA4OTkhMDAQo0aNytH2mb70WOSl2NjYLOeKmJmZITY2FkqlErGxsTD94PfX1NQU9+/f/5Yx6Qtx6ALlqhcvXuDatWuIiYnB2rVrpY5DRAXU3LlzVb6e7tu3L9q2bYsJEyaI62RkZGQ7FAB4N8vL+8OkvuTcgCdPnoiFXSZLS0tERUXlaFulUokyZcqIbY6Ojtl+1f7o0SN4eHigdu3asLOzw+zZs8Wv7+vXr49q1arB1dUVw4cPx/bt25GSkgIAaNeuHbS1tdG4cWOMHj0ae/fuRUZGxidzCYKAJUuW4ODBg1i9ejX09PQ++1iAd8MWOnbsKN7u2LEjAgMDP/r8f0pBOU/j/d+fL3mclD9oXI8u5a1GjRrB09MTxYoVw4gRI6SOQ0Tv0TY1RY1bDwrE0IVp06ahd+/eAN593T1y5Eh07NgR2trayMjIgJWVFdLS0hAZGYly5cpl2T48PBxWVlaQyWSwsrJCVFQUkpKSxF7TnPjYuN2cnGeQ2av6uaJOqVRi6NChqFmzJo4dOwZzc3Ps2rULv/32G4B3wzDWrFmDsLAwnDx5Etu2bcPGjRsRGBgIMzMz+Pv74+rVqzh9+jS8vb2xfft2bNu2LdueY6VSicmTJyM4OBjbt29H2bJlP/s4AODy5ct4+PAhli5diuXLl4v7SklJwaVLl1C/fn1xqERycnKW4jk+Pl7sMf3SYzFixAhcvXo122VfO4TA3NwcERERKm2xsbEwMzODXC5H0aJFEfvBtyCxsbEF4iRE0sBCVxYTI3WEQkMQBPj5+cHKykrla7yZM2dKF4qIPknb1BRGtetKHUMtzs7OaNasGaZPny6eDGZtbQ0rKyv4+flh2rRpKusrFAr4+/ujU6dOAIAGDRpAX18ffn5+GDZsmMq6d+/exejRo7Fjx44sX1+XK1cODx8+VGl7+PBhjgpECwsLyOVyhIeHw9HREcC7aRaTkpLg6uoqrvfq1StERUXht99+Ewun27dvi8vT09ORmpoqjjkdOnQo2rZtiwsXLqBp06ZQKpVwcnKCk5MThg8fjoYNGyIsLAy2trZZMs2fPx/37t3D9u3bYWZm9tnHkGn37t1wdnbGjBkzVNoXLFiAgIAA1K9fH1ZWVpDL5bh9+zYaNGggrpOamoqwsDC4uLgA+PJjoe4YXXXY2tpi+/btUCgU4geEkJAQ2Nvbi8tv3rypsk1ISAjatWuX61ko92nc0AX5NxybVtiNGjUKY8aMwciRI/H27Vup4xBRITZlyhSEhYWJZ8LLZDLMmDED/v7+8PLyQkxMDARBwIMHDzBgwAAYGxtj4MCBAAAjIyNMmTIFPj4+8PX1RUJCAtLS0nD27FkMHjwYLi4u2c7n7ebmhvPnz+P06dNQKBQ4d+4czpw5IxbQn2JmZoZmzZph5cqViI2NxdOnTzF9+nRER0errGdubg5DQ0Ncv34dqampOHDgAEJDQ5GQkIDExERs3LgRgwcPFqeyevDgAd6+fYty5cph3rx5mDhxovjYb926BaVSme0JYleuXMH+/fuxdu3abIvc4OBgtG7dOksvdkJCAo4ePYqePXvC0tJS5adXr144fvw44uLiYG5uju7du2POnDkIDg5GRkYGnj59ismTJ6No0aLo1q3bVx2L3LZ06VIsXLgQANCkSRMYGRlh9erVSE5Oxo0bNxAQECB+o9CjRw9cuHABZ86cQWpqKgICAhAREQE3N7c8z0lfT+N6dCn3tGnTBtu3b0eRIkXw4sWLLIP1iYhyS/HixTF27FgsWbIEjRs3hqmpKerVq4dt27Zh5cqVaNOmDZKTk1GyZEm0bt0aQ4cOhYGBgbh9165dUaJECaxbtw6+vr7ikIYxY8Z8tHB1dHTEvHnzsHTpUowdOxZlypSBl5cX6tSpk6PMCxYswNSpU+Hi4gIjIyN06tQJPXv2VFlHW1sbM2fOxJIlS7BixQq0a9cOPj4+cHd3R8uWLXH69Gk8ffoUnTp1QkpKCkqXLo3x48ejatWqGDduHH799Ve0atUKCoUClpaWWLp0abZfqe/evRvx8fFiz2qm2rVrY+PGjUhOTla5oE+mQ4cOQU9PL8t2wLuhaqampjhw4AB++OEHzJgxA9u2bcOUKVMQFRUFMzMz1K5dG+vXr1cZzvAlx0Jdq1atwurVq8WxtR07doRMJsPw4cMxYsQIvHz5Ury4RebwkF9//RVr165F8eLFMWbMGDRt2hTAu28PvLy8sGDBAkRFRaFSpUrw9fVFCZ7cXiDIBA0ZYR0SEoLUiHDEh1yGy5TZUscpkFJTU7OMvQoICEC7du1U/qDkB0lJSQgNDUXVqlXVGgdGBROPd1bJyckAkO9em7khIyMDKSkpefZVtqYaNGgQ1q9fL3WMLHi8C59PvT8FBwdDJpN9dI5kdWnc0AX6MseOHYOTk1OW67l369atUP4hJSLSJC9fvswy9y5RYcBClz7rzZs34pV6RowYoTJpOxERFXwlSpTA6tWrpY5BlOtY6NJnFS1aFPPmzUOpUqUwd+5cfuonIiKiAoGFLmWRnJyMoKAglTZ3d3dcunQJLVq0kCgVERERkXpY6JKKkJAQNG3aFN27d8edO3fEdplM9k2mfCEiIiLKLRpX6Cp5JZNP0tHRwePHj5Gamort27dLHYeIiIjoi2ncPLqCGleD0URVqlTB3Llzoaenhx9++EHqOERERERfTOMKXfqfjIwMrFy5EiVLllSZxDzzakJEREREBRkLXQ3m4eGBHTt2wNjYGA0aNMjR9duJiIiICgqNG6Mr/+A645pswIABkMvlqFy5MufGJaICadWqVXB3d5c6BhHlUxpX6MoSE6WOIJnY2FiV27Vr18bevXtx9OhRVKhQQZpQRERfYcSIEfjjjz+kjlHgTZo0CVWrVoWdnZ3406BBA3h6eiIyMlJl3RcvXmDWrFlwcXFBjRo10KBBA4wbNw7h4eFZ9vvw4UOMGzcODRo0gL29PVxdXTF37twsf48KqqCgINjY2GDWrFlZlk2aNAljxozJ0p6amgobGxv8888/Km2rVq1CmzZtYG9vj3r16mHw4MG4fPlyruYNCQlBixYt0KNHj8+u6+fnh1atWsHJyQm9e/fGzZs3VfLOmDEDjRs3Rt26deHp6Yk3b97katbconGFriYSBAE7duyAg4MDDh8+rLLM2dkZ2tocwUJEpOlat26NkJAQ8Wf//v2Qy+UYOnQoMjIyAADR0dHo1q0bEhIS4Ofnh+DgYOzbtw+lS5dGt27dcPv2bXF/oaGh6NatG0qVKoX9+/fj6tWrWLlyJe7cuYPevXsjJSVFqoeaa3bt2oV27drh0KFDSE1N/aJ9KBQKDB48GKdOncLixYtx7do1HDp0CPb29ujfvz/Onz+fK1n3798PDw8PWFpafnbdU6dOwcfHB4sXL8aFCxfg4uKCYcOGISkpCQCwfPly3Lp1Czt37sSxY8cgCAImT56cKzlzGwtdDZCQkIA5c+YgLi4O48ePLxRvLkRUeNnY2ODQoUPo0qULatSogSFDhuD58+cYOHAgHB0d0a1bNzx9+hQA4OPjo9I7FRQUBDc3Nzg4OKBjx464ePEiAOCff/6Bo6MjNm/eDCcnJ1y7dg0AsGPHDrEXrXXr1lk6Az4UFBSELl26wNHREY0aNYK3tzcA4MGDB7CxsUFUVJS4rkKhQN26dXHo0CEAwOHDh9GxY0c4ODigWbNm2Llzp7jupEmTMHXqVPTt2xft27cHADx+/BgDBw5E3bp1UbduXYwdOxZxcXHiNmfOnEHTpk3h6OiIyZMnY8WKFejbt6+4/FP3lxP/196dx+WU948ff13tUpZEyJaQUMmWrClDEWFiZIZhbDO2287c+FnuZB+GkcRgZhiyja2xdNmGwdgmkmUoN012FVJarqvfH+7O16WMLlvU+/l49NB1rs85n/c5n8r7+pz3Ocfa2prx48cTExOjzNbOnz8fGxsb5syZo1zXUbp0aUaPHo2Hh4fOzOa0adNo1qwZY8aMwdraGkNDQxwdHVmyZAl169blzp07ufYbHR3NJ598Qt26dWnbti07d+4E4Pjx4zg4OOgklCNGjGD8+PEAbN68GV9fX2bOnEndunVZsmQJnp6eOts+f/48jo6O3L59G61Wy8KFC2ndujUuLi58/PHHnDp1Ks/HJzExkX379jFs2DBKlixJREREntd91i+//EJUVBRLly7FyckJAwMDSpUqxZAhQxg2bFius9/x8fE6s+/Pfk2cODHXftLS0ggLC8PFxeWlMYWFhdGlSxdcXFwwMzOjX79+AOzfv5/MzEw2btzIoEGDKFeuHCVKlGD48OEcOHCA2+9heahM5RUClpaWLFy4kPHjx/Pdd99hZmaW3yEJIfJR8ok/XtrGoqGb8r02LY2Us5H/2F5lYkJRF1fltebRI1IvntfZjj7WrVtHSEgIKSkpdOjQgf79+zNr1iwqVapEjx49+Omnn5g8ebLOOrdv32bo0KFMnz6djz76iO3btzN48GD27dsHQEZGBteuXePIkSOYmpqyb98+5syZw9KlS3FxcSEiIoIxY8Zgb2+Pg4NDjphSUlIYOnQo//73v/H39+evv/6ie/fu1KlTB09PT6pXr45arebzzz8H4MSJE6SlpdGqVSuioqKYMGECixYtwt3dnT///JP+/ftTvXp16tWrB8DevXuZMWMGHh4eAEycOBFbW1sOHTpEcnIyffv2JTg4mPHjx3Pnzh2GDh3KqFGj6NGjB9u2bWP27NlK3HnpLy+evX5Dq9USERGR47hn69WrF926deP27dsYGRlx+vRpfvrppxztLCwsmDFjRq7bSE1NZeDAgfTp04effvqJEydO8OWXX/Lzzz/nKd47d+5gamrKiRMnePDgAYsWLeLixYvUrFkTgIiICBo0aICNjQ0rV64kPDyc5cuXU758ecLCwvjqq684cOAA5ubmL+1r69atODo6UqVKFTp06MDGjRuVDyn62LNnD97e3pQqVSrHewMGDMh1HVtbW6KiovTqp2vXrnluGx0dTbt27ZTXBgYGODo6EhUVhaOjI48ePaJ27drK+/b29piZmREdHY2NjY1ecb1tkugWQA8ePECtVvPxxx8ry7y8vDhy5AjGxsb5GJkQ4n1woVXTf26gUtHw0f8lOBl3br90HZNKlXE5H6O8Tok6w8U2HjRMznylGNu3b0+ZMmUAqFq1KrVr16ZWrVoANGrUiCtXruRYZ+fOnVSsWFH5D7pLly6Ympqi1Wqf7kdGBj169FA+7GcnJg0aNACgXbt2rFixgt27d+ea6Jqbm/Pbb79RtGhRVCoVDg4OODg4cO7cOTw9PfH29tZJdNVqNR4eHpibm7N582Y8PDxo1qwZAA0aNMDHx4etW7cqiaetrS2tWrVS+gsNDUWlUmFiYoKVlRXNmzfn9OnTABw7dgxzc3N69uyJoaEh/v7+bNy4UVk3L/29zO3bt5kxYwa1atXC3t6e+/fvk5ycTJUqVXJtb2dnBzydic7+vyZ7WV4dPnyYjIwMevfujaGhIU2bNuWbb77BzMyM5OTkl67/6NEj+vfvj7GxMdbW1jRo0AC1Wq0kumq1moCAAODp+Pfu3VvZn549e/LDDz9w4MABnSTvRTZu3Khsy8/Pj8WLF/P3339ToUIFvfY5Li6Ohg0b6rXO25aUlETx4sV1lhUvXpzExERlhvn5p6UWK1bsvazTlUS3gDl9+jS9evXixo0b2NjYKH/kAElyhRAfjHLlyinfm5qa6swSmZqakp6enmOd69ev50gy2rdvr/O6fPnyyvd///03jRs31nm/cuXKOuUHz9u5cyerVq0iPj4erVZLRkaGkij7+PgQHBysJAlqtVo5jXz9+nWOHj2Kk5OTsq2srCydv9G2trY6fZ07d4558+Zx6dIlMjIy0Gg01KlTB4C7d+9StmxZDA0NlfZ16tRRHt2el/6et2vXLtRqtdI2IyODzp07M3XqVFQqldIuMzP3Dy9ZWVnA00fGZ7fP/pCRV9evX8+xX56enjx58oR79+69dP1ixYphYWGhvPb29mb9+vUMGTKEa9euERMTg7e3t9LX9OnTCQoKUtprtVpu3rz50n4iIyP573//i4+PDwAVK1akbt26bN68mWHDhuV5f+Hp8cqugX6fZI/nq77/vpBEt4ApX748qampwNNi8n/6oyaEKJwc9+t3cYtxGZuXrqMyMdF5be7konc/Ott7JrGCp6dOX8bAwOClidWzF9/mlixn9x0fH68kRAArVqwgMzOTKVOmMHfuXD766COMjY3p0aOH0sbe3h57e3v279+Pvb09ycnJtGjRAgAzMzMCAgKYNGnSC2N7Nrl78OABAwYMICAggGXLlmFhYcGCBQs4cuQI8DQhe/5C4mePUV76e563tzfz588H4N69e/j4+ODu7k7p0qUBKFWqFMWLFycmJibXWeHsOl47OzsllsuXL+t1KjsvY/is5xPE549J27ZtCQwMJD4+nj179tC4cWOsrKyAp8coMDCQtm3b5rm/bBs2bCAzMxMvLy9lWUZGBrdv32bIkCEYGBhgbGyca33to0ePlP7h6Yer3M5Q/JPnfz6f5efnR2BgoF7be17JkiVzxJ6UlET16tWV45eUlETRokWV9x88eJBr+UV+k0S3gClbtqxyccTzMxlCCAHoXTdrYGqq9zqGlpavXJ/7qipUqMChQ4d0lq1evZqWLVvm2r5SpUrExsbqLIuNjaV169a51kAuXboUOzs75bR2WlpajqSvbdu27N+/n5iYGLy8vDA1NVX6en57t27donTp0joJ7rNxPH78mL59+yozlM/e0aBUqVLcunWLrKws5UNBVFSUkujp29/zrK2tGTVqFEFBQTRv3hwrKytUKhVt2rRhzZo1+Pv75/gw8vPPP+Pm5qYkO40aNWLlypU5JlxSU1P59NNPmTBhAvXr19d5r2LFisTHx5Oeno7J/z48bd26lSpVqijHMjU1Vfk+Li4u1zKTZ49TgwYNOHDgABEREToXLlasWJFLly7pJLp5KT14/Pgxv/76K1OnTtU5I5Camoq/vz9Hjx6ladOmVK1ald9//x2NRqNzzCMjIzE2NlbKOtq2bcukSZMYMWJEjr7nz59PWlqacsFdtlep0dVHnTp1iI6OpnPnzsDTDxTnz5/H39+fihUrUrx4caKjo5WzEH/99Rfp6enKGYf3SaG764L2f59MC4K0tDSmTJnCN998o7O8ffv2kuQKIQodX19fbt68yfr160lPTyc8PJxvvvlGZ9bpWX5+fmzfvp3IyEgyMjLYvHkzly9ffuHfT1tbW27dusXNmze5d+8eU6ZMoUyZMjpXmvv4+PDHH3+wf/9+nTpPf39/Tp8+zaZNm0hPT+fChQt07dqV3bt359pX+fLlMTAw4M8//yQlJYVVq1Zx79497t27R2ZmJg0bNiQhIYF169aRnp7Opk2buHbt2iv3l5tPPvkEOzs7ndnBf/3rXyQmJjJgwAClv3v37hEYGMiBAwd0LlSbMGECkZGRjBw5klu3bqHVarlw4QL9+vXDzMwMZ2fnHH22aNECc3NzQkJCSEtL4/jx40ydOhVDQ0NsbW0xNDRk9+7dZGZm8ssvv+SpzMDHx4fw8HAuXLjARx99pCzv3r07a9asITIyEo1Gw6+//oqvr69yR4958+Yxc+bMHNv79ddfMTU1pXPnzlSuXFn5qlmzJp6enkqtdOfOnUlPT2fKlCncv3+fjIwMjh07RlBQEEOGDFFqXP38/GjQoAG9evXi2LFjaDQaEhISWLhwIatXr85TvfCb4O3trdy3NyAggC1bthAZGUlqaipLlizBxMQEDw8PDA0N6datGyEhIdy8eZPExES++eYbPvroI6ytrd9JrPoodIlulqVlfofwxowePZqFCxcyc+ZMzpw5k9/hCCFEvrK2tub7779n1apVNGzYkNDQUBYvXqycan1e+/btGThwIGPHjsXNzY2ff/6ZFStWvPBiq7Zt29KiRQvatWvHJ598goeHB1999RVqtZo5c+YAT8sXypQpw927d2na9P8u4LO3t2fevHksX76cBg0aMHToUPr27fvCJMbGxoaRI0fy73//m1atWvHgwQPmzp1Leno6PXr0oGLFikyfPp2FCxfStGlTLl68iJ+fnzLLqm9/uVGpVEydOpU9e/awf/9+4OmtxDZs2EC5cuXo1asXzs7O+Pn58fDhQzZu3Ii9vb2yfs2aNVm/fj1arZbOnTvj6urK8OHDady4MStWrMj1uhETExNWrlzJwYMHadiwIZMmTSIwMJBq1aphbW3N6NGjWbBgAY0bN+bChQt52p82bdoQGRlJ06ZNdS6w8vf3p0ePHgwZMoT69euzfPlyvvvuO6WO++7du9y9ezfH9jZt2kSHDh2UGednffzxx6jVapKSkihRogTr169Xap0bNWpEUFAQvXr10rmbgoGBAUuXLqVLly5MnjyZevXq0bFjR2JiYli3bl2uHwheRdu2bXFycmLJkiWcPXtWuR1Zdk361atXlfvktmjRgpEjRzJ8+HAaNWrEkSNHCA0NVcothg0bhouLC35+fnh5eVG0aFGmT5/+RuJ801RZH0o18WuKiooi7b9XSXqcQOvuvfM7nDciNjZWeSrJwoULc1zIUJilpKRw4cIFHB0d83SbGPFhk/HOKbtWv0iRIvkcyZun0Wh48uQJZmZmeToNX1Clp6djbGysJLfjxo1Dq9UqSXdBkV/jHRcXx/fff8+UKVPeWZ+FxT/9fTp79iwqlUrnQsrXUehmdD9k165d0ynSr1q1Kmq1mo0bN0qSK4QQhUhKSgru7u78/PPPaLVaoqOj2bt37wvrkYX+1Gr1e3fbL6G/QpfoGvyv9uZDotVqCQkJwd3dne+//17nvZo1a+a4IEAIIUTBZm5uzrfffsuGDRuoV68eQ4cO5YsvvpDrM96gPn36yPEsAArdXRdUH+DjbzMzM1mzZg1PnjxhxowZdO/eHcsCVGsshBBCf82aNZNbSArxEoVuRvdDZGJiQkhICPXr12fHjh2S5AohhBBC5IEkuu+hW7dusXDhQp2njtSuXZs9e/Yoj8AUQgghhBD/rNCVLrzvTp8+TdeuXUlMTKR8+fL4+/sr70ktrhBCCCFE3smM7numRo0ayn3+Ll++nM/RCCGEEEJ8uGRG9z2g1WqV54JbWFgQEhJCenq6XGQghBBCCPEaJNHNR48ePWLixIkYGBgwf/58ZXmjRo3yMSohhBBCiIJBEt18NH36dH766Sfg6aMoW7dunc8RCSGEEEIUHIWuRlfzv2dYvw/Gjh2LjY0NnTp1ol69evkdjhBCCKFwcHCgTp06ODk54eTkhLOzM23atGHx4sVkZmbqtD169Ch9+/alYcOGuLi40K5dO5YsWUJ6enqO7e7YsYPu3bvj6upK/fr18ff3Z9OmTe9qt966UaNG4eDgwNmzZ3O85+DgwG+//ZZj+dq1a/H09NRZFhsby6hRo2jSpAkuLi54enoSGBhIUlLSG4s1KyuL77//njp16rB27dp/bJuUlMTw4cNp0qQJzZo1Y8KECTx55tkEFy5c4LPPPqN+/fq0adOGFStWvLE4X0ehS3QxM8u3rs+ePUtaWpry2srKiv3797NixQqsrKzyLS4hhBAiN8HBwURFRREVFUVkZCSzZ89m7dq1Ok/p3Lx5M4MGDcLb25v9+/fz559/MmvWLPbv30+/fv10kuIFCxYQFBREv379+OOPPzh06BBffPEFM2bMYOHChfmxi2/UgwcPUKvV+Pj4vFbyfuHCBfz9/Slbtizbtm3j9OnTLF68mEuXLhEQEKCTYL6OgQMHcuzYMYoVK/bStpMmTSI1NZUdO3awadMmYmJimDt3LgBPnjxh4MCBNG7cmEOHDjF//nyWLl3Knj173kicr6PwJbr5IDMzk6CgILy8vJgxY4bOe2XLls2nqIQQ4v3k4OBAeHg4Xbp0wdnZmQEDBnDr1i369u2Lq6sr/v7+3Hjmce6rVq2idevWuLq64uPjo/Ofq0ajYe7cuTRt2pSGDRvyr3/9S5kRGz9+PBMmTKBnz574+voCTxOVsWPH0qxZM1xdXRkwYAB///33C2PNyspi7ty5tGzZEldXVzp37syJEycAmDNnDj179tRpHxERQYMGDUhPT+fJkydMmzYNDw8P6tatS8+ePbly5YrOcVi1ahXNmjUjNDQUgG3bttGuXTtcXV3x9PTk559/1tnXadOm4erqioeHB+Hh4bRp04bNmzcDvLS/lzEwMKBu3br06NGDiIgIAJKTkwkMDGTMmDF07doVCwsLDAwMcHJyIiQkhHPnzrFx40YArl69SkhICEFBQbRu3RoTExPMzc1p164ds2fP1rl3/PN++uknvLy8qFevHn379lXGZPz48YwYMUJpl5aWhoODA3/88QcAPXv2ZM6cOXTo0IEBAwbQtWtXvvvuO51tBwYG0rdvXwDi4+P58ssvcXNzo2HDhowdO5bk5OQ8H6Nt27ZRq1YtevbsSXh4+CsnpNOmTaNZs2aMGTMGa2trDA0NcXR0ZMmSJdStW5c7d+7kWCc4OFiZfX/+K/tn8nl169YlNDQUs5dMAt67dw+1Ws2IESOwsrLCxsaGQYMGsWnTJjIyMjhw4AAZGRl89dVXmJubU7t2bbp27UpYWNgr7f+bJInuO2BoaMjJkyfRaDSsWrWKhISE/A5JCFGIJZ/446Vfz9Kmpb20/eMzf+qso3n0KMd29LFu3TpCQkLYtm0bR48epX///owaNYpDhw6h0WiU6xtOnDjBvHnzCA4O5vTp0/Tv35/Ro0crf2d/+uknIiIiCAsL48CBA6SmpvKf//xH6Wfv3r188cUXbN++HYCJEydy9+5dtm3bxqFDhzAzM2P48OEvjHPr1q1s2bKFsLAwTp48iZeXF8OGDUOj0eDt7c2pU6d0TjVHREQoSd7cuXM5f/48YWFhHDt2DCcnJ4YMGaKT8KnVarZs2UL//v2Ji4tj3LhxTJw4kdOnTzN9+nT+85//cPHiRWVfd+7cyfr169m2bRs7d+7USYjy0l9eZGRkKN8fPnwYrVZL165dc7SzsrKiQ4cO7N69W9kXW1tbPDw8crT19PTkX//6V679qdVqVq1axeLFizl27BjlypVj9OjReY43PDyc6dOns3TpUry9vVGr1Trv7927l/bt25OVlcWgQYMoV64cBw4cYNeuXdy+fZtZs2blua+NGzfSsWNH6tWrR/HixZV918f9+/c5ffo0n332WY73LCwsmDFjBpUqVcrx3qBBg5TZ9+e/GjZsmGtfgwYNytM9+i9cuIChoSEODg7Kstq1a5OSkkJsbCzR0dE4ODhgaGiovF+rVi3OnTuXl11+qwpdomtw/fo771OlUrFo0SJ8fHw4ePCglCkIIfLVhVZN//nLU/fWhhl3br90nSsB/jrrpESd4UKrpq8cY/v27SlTpgxVqlShatWqODk5UatWLSwsLGjUqBHX//e3vH79+vz+++/UqFEDlUqFr68vaWlp/PXXX8DT0+oBAQFUqFCBokWLMmnSJDp06KD0Y2trS6tWrVCpVCQlJREREcHw4cOxsrLCwsKCYcOGERUVRVxcXK5xdujQgZ07d1K2bFkMDQ1p3749CQkJ3LhxAycnJ8qVK8f+/fuBp2f3Dhw4gI+PD1qtVjnlb2NjoyTUN27c0Knt9PHxwdraGpVKRYUKFTh27BhNmjRBpVLh7u5OqVKliI6OBuDgwYP4+vpSvXp1ihUrxqhRo0hNTQXIc3//JDMzk5MnTxIWFqYcw+vXr1O+fHmMjY1zXcfOzk4Zq7i4OKpUqZKnvp71yy+/4O3tTc2aNTExMWHEiBF8/vnnaLXaPK3v7OyMs7MzKpUKb29vLl68SHx8PADnzp3j7t27tG7dmqioKC5fvsyYMWMoUqQIpUqVYujQoWzbti1PHwaioqKIiYnBx8cHlUqFn5/fK5UvZP+s2dnZ6b3u25KUlISFhYVOUpx9z//ExESSkpJylD+UKFGCpKSkPI/T21Lo7rqgeq6A/m2IjY0lNDSU6dOnK59ubG1tWbNmzVvvWwghCoJy5cop35uammJjY6PzOvsiJ41Gw+LFi9m1a5fO2bLs9+Pi4qhQoYKyvGLFilSsWFF5bWtrq3x/48YNsrKysLe3V5Zlz5zFx8frrJctNTWVoKAgfvvtNx48eJCj/+wZxM6dO3P8+HFUKhVNmzbl/v37PH78OMeMmlar5ebNm7i4uABQ/pkLqFUqFWvXrmXjxo3cuXOHrKws0tPTlb7u3r1Ly5YtlfZ2dnZYWFgA5Lm/5z3bXqPRYGVlxYABA/j888+VNhqNJtd1n407+99XSXri4uJ0LtguVaoUPj4+eV7/2TG2tbXFyckJtVrN559/TkREBM2bN6dYsWLExcWh0Whwc3PTWV+j0ZCYmPjSSaoNGzbg4eFBiRIlAPDz8yM4OJi4uLhcf3ZeJPt45XeC+Dx9Z/7h/Xiia6FLdN+2yMhIfH19SUlJoVy5ci88FSOEEPnFcf/verU3LmPz0nVUJiY6r82dXPTuR2d7z/0Hmf1QnectXryYnTt3EhISQs2aNcnKyqJWrVo62/mnhOHZU6253SHg2e0EBwezZMkS4GkCunv3bqZOncqlS5dYs2YNlStXJi4ujo8++khZz8fHh88++4wnT56wZ88e2rRpg5GRkVITuW7dOurUqZOn+DZs2EBoaCjBwcE0bNgQQ0NDncRWq9XmmFnNPm557e95wcHBtGjRAoCwsDC+/fZbOnXqpLxftWpV4uPjefLkSa51nrGxsVStWhWAKlWqsH//frKysvRKgAwMDPKcZOWWdD97DOHpmDyb6H711VfA0w9Q5ubm/Pnnnzm28TKpqamEh4eTlpaGq6ursjwrK4tNmzYp5S/Gxsa51u0+evQIU1NT4P8+XF2+fFnnA97LPPvz+bwVK1a8sHwhL6ysrEhOTkaj0SjHM7skp1SpUlhZWfHf//5XZ52kpCRKlCjxwt/dd6XQlS68bXXq1KFmzZoYGRm90qcfIYR42ywaur3061kGpqYvbV/UxVVnHUNLyxzbeRuioqLw8vKiVq1aGBgYKKfxs1WsWJGrV68qr69du/bCs2vZs26xsbHKsuzvK1WqpFMDmV17efbsWTp27EiVKlVQqVQ5+q9Tpw7W1tYcOXIEtVpNu3btALC0tKREiRJcunRJp/0/XfgWFRVFgwYNaNy4MYaGhty9e1enBrdUqVLKKfnsfX348OEr9/e8bt26UalSJZ2Lqps0aUKRIkVYt25djvYPHz4kPDxc2efWrVtz7949duzYkaPt4cOH6datW47blgFUqFBBJ4lKSEhgxYoVZGRkYGJiopRnAEqZxD9p27Ytp0+f5syZM8THxyu39apUqRIpKSk6ZSrJyckkJia+dJu7du3CyMiI7du3s2XLFuVr+PDhbNmyRfmwZWdnl+NnBJ5OktWoUQOAkiVL0qhRI1auXJmjXWpqKl26dOHUqVM53nuVGt28cnR0JCsrS6kHh6c/j8WKFcPOzo46depw6dIlnfGLiop64ZmCd0kS3deUlZVFSkqK8trIyIiQkBClzksIIcTbY2try8WLF0lNTeXKlSssX74cS0tLbt++DcDHH3/M2rVriY2N5fHjx8yZM4eTJ0/muq1SpUrRrFkzvv32W5KSknjw4AELFizAzc1Np5TiWRUqVCAqKor09HQiIyMJDw8H0ElAvb29+f7778nKytJ58mX37t1ZsmQJMTExZGRksGrVKvz9/XUSt+f3NTY2lgcPHhAfH09gYCDly5dX9tXNzY3t27dz9epVHj16xPz58zE3N3/l/p6nUqmYNm0a4eHhHDp0CABzc3MmTJjAvHnzWLFiBcnJyWi1Ws6ePcunn35K/fr16dixI/D0g8SgQYOYNGkSGzZs4MmTJ8rtqkaMGIGfnx9GRjlPNHfp0oU9e/Zw5swZ0tPTlVIVY2NjqlSpwpkzZ7h16xaPHj1ixYoVOWZwczuOtWvXZvbs2bRs2ZKiRYsCUKNGDVxdXZk+fToJCQk8fPiQyZMnM3bsWGVdb2/vXH9+NmzYQIcOHbCzs6Ny5crKV0BAAPfv3+fw4cPA09t5/fjjj+zevZu0tDQePnxIaGgoR48e1ckZJkyYQGRkJCNHjuTWrVtotVouXLhAv379MDMzw9nZOU9j9joiIiLo0aMH8HRGt23btixYsICEhARu3brF4sWL8ff3x8jIiJYtW2JhYcGSJUtITU3lzJkzbNy4kYCAgLce58tI6cJruHfvHiNHjiQ1NZX169crp2KqVauWz5EJIUThMHDgQEaMGEHjxo2pXr06M2bMwMbGhsDAQKysrOjZsycJCQkEBASQlZWFu7s7kyZNeuH2Zs2axdSpU/Hx8cHAwAB3d/cct4V81qhRoxg7diyNGjXCxcWF2bNnA09n11avXk3t2rXx9vZm2bJlfPbZZzpJ2KBBg3j48CE9evQgIyMDR0dHli1bRpEiRXLtKyAggOPHj9OyZUtsbW2ZMmUK586dY8GCBZQuXZp+/foRExODn58f5cqV4+uvv+b48ePKqWN9+8tNjRo16N27N5MnT2b79u0ULVqUTp06Ubp0aaWsIiMjgwoVKuDn50efPn10Tl0PGTKEihUrsnr1aoKCgjA2NqZGjRrMmTMn17sxwNM7MgwaNIhhw4aRkpKCq6sr8+bNA8Df358jR47g7e1NmTJlmDhxYo67KuTG29ubWbNm5bh377x585g2bRpeXl6YmJjg7u7OzJkzlfevXr2ao/QgNjaWU6dO5fpzVaJECby8vNi0aRMtWrTA19cXS0tLQkJCmDhxIsbGxjg6OrJy5Uqdi89q1qzJ+vXrWbRoEZ07dyYlJYWyZcvi6+tL//79X3jxnz5OnDjBF198ATwt2wkMDCQoKIiGDRuyYsUKHj16xLVr15T206ZNY/LkyXh5eWFsbIyvr69yazcTExNCQkKYPHkyoaGhWFtbM2LEiBeO6bukyiok59ejoqJI++9VHkWdpNW/p72RbQYFBSk3Sw4ODqZ79+5vZLvi9aWkpHDhwgUcHR11ZjREwSTjnVP2LJ0+ScyHQqPRKDWhL5u9K2zS09Mx+V+9dEZGBnXr1mX58uW4u7vnc2Sv7n0a72+//ZZWrVq9kxnVguyf/j6dPXsWlUqFk5PTG+lLShdew8iRI3FwcKBPnz7KzcaFEEKI/LBlyxZatWrF1atXycjIYOnSpVhaWr6xhEE8nQWtWbNmfoch9CClC3o4ePAgzs7OlCxZEnh6FatarVbqe4QQQoj80rFjR2JiYujVqxfJyclUq1aNxYsXK7cYE69v9erV+R2C0FOhS3Q1etzLLptWq2X8+PEsX76cjz/+mGXLlinvSZIrhBDifWBgYMCoUaMYNWpUfocixHuj8JUuvEIBt4GBgXJnhQMHDnDr1q03HZUQQgghhHjDCt2M7qvKvjr066+/pkyZMvkdjhBCCCGEeInCN6ObB1FRUfTs2ZPHjx8ry4oVK8b8+fMlyRVCfBAMDQ1f+mhWIYTID88+Ye1tK3SJruEzT7zJzfnz52ndujXh4eFMmTLl3QQlhBBvmLGxMampqfKERiHEeyUrK4vU1NQ3ci/gvJDShec4OjrSpk0b9u3bh4ODQ36HI4QQr0SlUlGyZEnu3btHkSJF8v3+o2+SVqslLS0NQOdhBKJgkvEuODQaDampqZQsWVJ5yNbbVugTXa1WS0JCAtbW1sDT/xy++eYbHj58iL29fT5HJ4QQr87IyAhra2syMjIKVBlDeno6V69epWrVqpiZmeV3OOItk/EuOExMTChatOg7S3KhkCe6cXFxDBkyhKSkJCIiIpSnyZQuXZrSpUvnc3RCCPH6VCqV8retoMguxzA1NS2QT34TumS8xevI13MA8fHxDBgwADc3N1q1asWcOXPQarW5tv3xxx9p27Yt9erVIyAggHPnzr12/zt37uTQoUNERUWxcuXK196eEEIIIYR4f+Rrojt06FBsbGxQq9WsXLkStVrNDz/8kKPdvn37WLRoEbNnz+bIkSO0atWKL7/8Urm37avq168fLVu2ZNSoUfTp0+e1tiWEEEIIId4v+ZboRkVFcfHiRUaPHo2lpSVVqlShd+/ehIWF5WgbFhZGly5dcHFxwczMjH79+gGwf/9+vfp8AtxOeqi8NjAwYOPGjUyYMKHAndoTQgghhCjs8i3RjY6OxtbWluLFiyvLateuzdWrV0lOTs7RtlatWsprAwMDHB0diYqK0qvPJJUB3+1S65RHFKQrkYUQQgghxP/Jt4vRkpKSKFasmM6y7KQ3MTERCwsLnbbPJsTZbRMTE/PcX0ZGBqVLl+bfM2YRGRmJkVGhvg6vwMu+eOHy5cvv9OpOkT9kvAsXGe/CRca7cMnIyHij45yv2Z4+NzJ/3Zueq1QqjI2NqVip0mttR3wYCuKV5uLFZLwLFxnvwkXGu3BRqVQFI9G1srIiKSlJZ1lSUhIqlQorKyud5SVLlsy1bfXq1fPcn6ur66uGKoQQQgghPkD5VqNbp04dbt68SUJCgrIsKiqKatWqUbRo0Rxto6OjldcajYbz58/j4uLyzuIVQgghhBAflnxLdGvVqoWTkxPz5s0jOTmZmJgYVq5cSUBAAADe3t6cPHkSgICAALZs2UJkZCSpqaksWbIEExMTPDw88it8IYQQQgjxnsvXGt2FCxcyadIkmjZtioWFBd27d6dHjx4AXL16VblPbosWLRg5ciTDhw/n/v37ODk5ERoaKo8CFEIIIYQQL6TKet2rvIQQQgghhHgP5euT0YQQQgghhHhbJNEVQgghhBAFkiS6QgghhBCiQJJEVwghhBBCFEiS6AohhBBCiAKpQCW68fHxDBgwADc3N1q1asWcOXPQarW5tv3xxx9p27Yt9erVIyAggHPnzr3jaMXr0Ges165dS9u2bXF1dcXPzw+1Wv2OoxVvgj5jnu327du4urqyaNGidxSleFP0Ge+YmBh69uyJi4sLLVu2ZNWqVe82WPHa8jreWq2WhQsX4unpiaurKx06dODXX3/Nh4jF6zh06BBNmjRhxIgR/9hOq9Uyf/58vLy8aNiwIX379iUuLk6vvgpUojt06FBsbGxQq9WsXLkStVrNDz/8kKPdvn37WLRoEbNnz+bIkSO0atWKL7/8Urlvr3j/5XWsd+/ezbx58wgKCuL48eN89tlnDB8+XO9fFJH/8jrmzwoMDMTQ0PAdRSjepLyO95MnT+jXrx8tW7bk2LFjLFq0iI0bNxITE5MPUYtXldfxXrt2LRs2bGD58uWcPHmSkSNHMmbMGC5evJgPUYtXsWzZMgIDA6lcufJL265Zs4bt27cTGhrK/v37qVKlCoMHD0afO+MWmEQ3KiqKixcvMnr0aCwtLalSpQq9e/cmLCwsR9uwsDC6dOmCi4sLZmZm9OvXD4D9+/e/67DFK9BnrJ88ecLIkSOpX78+xsbGdO3alaJFixIZGfnuAxevTJ8xz3bw4EGuXLkiT1D8AOkz3jt37sTCwoJ+/fpRpEgRnJ2d2bFjB/b29vkQuXgV+ox3dHQ09evXp2rVqhgaGtKqVStKlCjBpUuX8iFy8SpMTU3ZuHFjnhLdsLAwevfujb29PRYWFowYMYKYmBjOnDmT5/4KTKIbHR2Nra0txYsXV5bVrl2bq1evkpycnKNtrVq1lNcGBgY4OjoSFRX1zuIVr06fsfbz81Oetgfw8OFDHj9+jI2NzTuLV7w+fcYcnn7AmTZtGpMnT8bIKF8fAClegT7jferUKWrUqMHXX39NgwYN8Pb2Ztu2be86ZPEa9BlvDw8Pjh8/zoULF0hPT2fv3r2kpqbSqFGjdx22eEW9evXC0tLype2ePHnClStXdPI1CwsLKleurFe+VmAS3aSkJIoVK6azLPuXJjExMUfbZ3+hsts+3068n/QZ62dlZWUxceJEXFxc5I/iB0bfMV+8eDF169alcePG7yQ+8WbpM963bt1i7969NGnShEOHDjFw4EDGjRvH+fPn31m84vXoM95t2rThk08+oVOnTjg5OTFq1ChmzJhBuXLl3lm84t148OABWVlZr52vFaipDn1qNuTJxx82fccvIyOD8ePHc+XKFX788ce3FJV4m/I65leuXGHDhg1s3779LUck3qa8jndWVha1a9emQ4cOAHTu3Jl169axa9cunZkg8X7L63hv2bKFLVu2sGHDBhwcHDh69CijRo2iXLlyODs7v+UoRX543XytwMzoWllZkZSUpLMsKSkJlUqFlZWVzvKSJUvm2vb5duL9pM9Yw9PTHwMHDuTGjRusWbMGa2vrdxSpeFPyOuZZWVlMmTKFoUOHUrp06XccpXhT9PkdL126dI7ToLa2tty9e/dthyneEH3Ge/Xq1XzyySc4OztjamqKh4cHjRs3lnKVAqhEiRIYGBjk+rNRqlSpPG+nwCS6derU4ebNmyQkJCjLoqKiqFatGkWLFs3RNjo6Wnmt0Wg4f/48Li4u7yxe8er0GeusrCxGjBiBkZERq1atomTJku86XPEG5HXMb9y4wYkTJ1i4cCFubm64ubkRHh7O8uXL6dy5c36ELl6BPr/j9vb2/PXXXzqzPvHx8dja2r6zeMXr0We8tVotGo1GZ1l6evo7iVO8W6amplSvXl0nX3v48CHXr1/Xa/a+wCS6tWrVwsnJiXnz5pGcnExMTAwrV64kICAAAG9vb06ePAlAQEAAW7ZsITIyktTUVJYsWYKJiYlcnf2B0Gest2/fzpUrV/j2228xNTXNz7DFa8jrmJctW5aDBw+ydetW5cvT05Pu3bsTGhqaz3sh8kqf3/GOHTuSmJhISEgIT548YceOHURHR9OxY8f83AWhB33G29PTk40bN3Lx4kUyMzM5fPgwR48excvLKz93Qbwht2/fxtvbW7kFaEBAAD/++CMxMTEkJyczd+5cHB0dcXJyyvM2C1SN7sKFC5k0aRJNmzbFwsKC7t27K1fcX716VblPbosWLRg5ciTDhw/n/v37ODk5ERoaipmZWX6GL/SQ17HetGkT8fHxOS4+8/PzIzAw8J3HLV5dXsbc0NCQsmXL6qxXpEgRLCwspJThA5PX33EbGxuWLl3K9OnTCQ4Opnz58ixevJhKlSrlZ/hCT3kd74EDB5KZmcngwYNJSEjA1taWwMBA3N3d8zN8oYfsJDUzMxNAeYhTVFQUGRkZXL16VZml7969O3fv3qVnz548fvwYNzc3vvvuO736U2XJVVlCCCGEEKIAKjClC0IIIYQQQjxLEl0hhBBCCFEgSaIrhBBCCCEKJEl0hRBCCCFEgSSJrhBCCCGEKJAk0RVCCCGEEAWSJLpCCCGEEKJAkkRXCFGo/fHHHzg4OBATE5PfobyWiRMn8tlnn73wfScnJzZs2PAOIxJCiPxXoJ6MJoQoXHr27MnJkycxMsr5p8zT05Nvv/02H6LStXnzZr7++mtMTEx0llevXp2+ffvSvn37N9LP80/627p1K3Xr1qVy5crA06cOvU0ODg4YGRlhYPB/8yfm5uZUr16dgQMH0rx58zxv69atWxw6dIiuXbu+jVCFEIWIJLpCiA+at7c38+fPz+8wXurkyZOYmpoCkJqayvbt2xk9ejRarZYOHTq80b6ysrKYMWMGs2fPVhLdd2HixIkEBAQorx89esSPP/7Il19+yc8//4yLi0uethMREcH27dsl0RVCvDYpXRBCFGgpKSlMmTIFd3d3nJ2dad26NatWrXph+6NHj9KtWzfq169PgwYN6NOnD1euXFHeP3HiBD179qRRo0bUr1+fr776iri4OL1iKlKkCN26daNRo0bs3btXWR4REUGXLl2oV68ebm5ujB49moSEBOX9NWvW0LZtW+rWrYu7uztff/01ycnJAIwfP55u3brx6NEjnJycSExMZNCgQUo5g4ODA2vXrmX9+vU4OTkp62UbNWoUPXr0AODBgwdMmDABDw8PXFxc6NChA+Hh4XrtI4ClpSWDBw+mbNmybN++XVn+22+/0bVrV1xcXGjUqBH9+/fn2rVrAMyaNYugoCDOnj2Lk5MTv//+u3JsunbtqhybMWPG6BwbIYTIjSS6QogCbd68eRw+fJhffvmFM2fOMHHiRGbMmMGhQ4dytM3IyGDw4MF06dKF48ePc+DAAezs7Jg4cSIAMTEx9O3bFy8vLw4fPoxarcbc3Jw+ffqQnp6ud2wajUYpuzh+/DhDhw6lV69eHDt2jE2bNhEbG8vw4cMBiIyMZObMmcyfP5/IyEi2bNnC1atXCQ0N1dmmpaUlu3btAiA4OJjVq1frvO/j44NKpUKtVivLUlJS2LdvH507dwZg8ODB3Lx5k3Xr1nHy5Em++uorxo4dy5EjR/Tex6ysLDQajTKbfefOHQYNGkSbNm04ffo0ERERZGZmMmbMGADGjRuHn58fzs7OREVF0bRpU44ePcrIkSPp3bs3x48fZ+vWrdy5c4chQ4boHY8QonCRRFcIUaCNGzeOzZs3U7ZsWVQqFR4eHpQuXZrIyMgcbdPT03ny5AlmZmYYGhpiYWHBpEmTWLduHQBhYWFUq1aN3r17Y2JiQsmSJZkwYQJxcXGcOnUqzzElJyezdu1aTp06RZcuXQBYvXo17u7udOrUCRMTEypUqMCgQYP4448/uHHjBg8fPgSezgYD2NjYsHbtWkaOHKnX8bC0tMTT05OdO3cqy9RqNVlZWfj4+HDx4kVOnDjBuHHjKFu2LMbGxrRr145mzZqxZcsWvfpKSEhg9uzZ3Lt3Dz8/PwDKlCnD4cOH6dOnD4aGhhQvXpy2bdty7tw5MjMzc93O6tWr8fDwoH379hgZGVG2bFlGjx7NqVOn9J5NF0IULlKjK4T4oO3atUtndjLbwIEDGTJkCLdv32bOnDmcPHmSR48eAU8T2rS0tBzrFC1alJEjRzJp0iRCQkJwd3fno48+okmTJgDExsZy4cIFnJycdNYzMjLi77///sc4GzRooHxvbm6Ovb09wcHByravXbtG48aNddapVq0aANevX6dx48a0bt2a9u3b4+zsTOPGjfH19VXa6KNTp04MGTKEBw8eULx4cbZv306bNm2wsLAgNjYWAH9/f511srKyqFu37j9uNzAwkKCgIKV9RkYGzZo1Y9WqVdSoUUNpFx4ezrp16/j777/JzMxEq9Wi0Wh0ZrifFRsby7Vr13Icd0NDQ/7++28qVqyo9zEQQhQOkugKIT5o/3QxmlarpV+/flhbW7N27VoqVaqESqWiZcuWL9xev3798Pf35/fff+fQoUMMHjwYT09P5s2bh5mZGc2bN89RLpAXz16MlpvcEm+tVguASqXCxMSE+fPnExcXx6FDhzhw4AChoaH8v//3/+jevbtesTRr1ozixYsTERGBp6cnR44c4fvvvwdQYjx48CBWVlZ6bffZi9EePHiAr68vNWrU0Enyt27dSmBgIIGBgbRr144iRYqwYcMGpTwkN2ZmZnzyySdMnjxZr3iEEEJKF4QQBdb9+/f573//y6effkrlypVRqVTcvHmT27dvv3CdhIQESpQoQfv27Zk5cybBwcHs2LGDpKQk7OzsuHjxIhqNRmmv0WheOpubF1WqVOHSpUs6yy5fvqy8l5mZycOHD6lYsSI9evQgNDSUAQMGsGbNGr37MjIyon379uzatYs9e/ZgY2ODm5sbAHZ2dgCcO3dOZ534+Hid/X6Z4sWLM336dFatWqVT23vq1Cns7Oz4+OOPlTKMM2fO/OO27OzsiI6O1lmWmprKnTt38hyPEKJwkkRXCFFgWVlZYWlpyenTp8nMzOTSpUtMnTqVihUrcvPmzRztT506pVxoptFoSE9PJzIyEmtra4oXL05AQABJSUnMnj2bhw8fkpyczNy5c/H3989xFwN9BQQEcOzYMbZs2UJGRgbXrl1j8eLFtGrVChsbG5YtW0aPHj2UB1skJSXx119/UbVq1RzbMjc3B56e8s+u7X1ep06d+OOPP9i6dSudO3dGpVIBULVqVVq2bMns2bOJiYlBo9Hw+++/07FjR5263rxo0aIF/v7+jBs3jsTERAAqV67MrVu3uHbtGsnJyaxevVrZpxs3bgBP65Dv3LlDYmIiqamp9O7dm7Nnz7JixQpSUlJITExk4sSJ9O7dW5n1FkKI3EiiK4QosAwNDZk5cyYHDhygQYMGTJo0iSFDhtC7d2/27t2rXOmfrX79+owfP57p06dTr149mjdvzvHjxwkJCUGlUlG+fHlCQ0M5c+YMzZs3p3nz5vz111/88MMPWFhYvFasLVu2ZMaMGaxcuZJGjRrRp08f3NzcmDdvHgB9+/alSZMm9O7dG2dnZ3x9fbGwsMj1dL6VlRUdO3Zk7ty5fPrpp7n2V6tWLapUqcKff/5Jp06ddN6bNWsWderUoUePHri4uDBt2jTGjh2Lr6+v3vs1fvx4TExMlNKEgIAAmjVrRqdOnWjTpg23bt1iyZIl1KhRgy5dunDx4kX8/PzIzMykZcuWqNVqnJ2dWbBgAVu3bsXNzQ0vLy8yMjJYtmyZzgMqhBDieaqsrKys/A5CCCGEEEKIN00+CgshhBBCiAJJEl0hhBBCCFEgSaIrhBBCCCEKJEl0hRBCCCFEgSSJrhBCCCGEKJAk0RVCCCGEEAWSJLpCCCGEEKJAkkRXCCGEEEIUSJLoCiGEEEKIAkkSXSGEEEIIUSBJoiuEEEIIIQokSXSFEEIIIUSB9P8B5LKZjYz9T+MAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**12-Precision-Recall Curve**</p>"
      ],
      "metadata": {
        "id": "1v-qrZHHIocm"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCQGhPFRBIIN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "481f2ff1-bcd8-4025-8e95-09e4012aec6d"
      },
      "source": [
        "plot_model(tuned_lr, plot = 'pr')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIFL9s6BCShq"
      },
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**13-Confusion Matrix**</p>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jrox--iDCUsq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "16d4155c-ba41-4a98-d7dc-f9d9b2649c5d"
      },
      "source": [
        "plot_model(lr, plot = 'confusion_matrix')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**14-Cross Validation**</p>"
      ],
      "metadata": {
        "id": "3v-EZ0PoJwoN"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8X4dVnJJ7Fnv"
      },
      "source": [
        "\n",
        "\n",
        "Evaluate the model on the holdout set used for validation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGeJgsxK7Hd_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "2c312cb9-c0b6-4ed6-e4ed-240c8e0e3c6f"
      },
      "source": [
        "val_rf_pred = predict_model(tuned_lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0e0f92ef90>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_c318d\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_c318d_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_c318d_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_c318d_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_c318d_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_c318d_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_c318d_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_c318d_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_c318d_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_c318d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_c318d_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_c318d_row0_col1\" class=\"data row0 col1\" >0.9778</td>\n",
              "      <td id=\"T_c318d_row0_col2\" class=\"data row0 col2\" >0.9985</td>\n",
              "      <td id=\"T_c318d_row0_col3\" class=\"data row0 col3\" >0.9778</td>\n",
              "      <td id=\"T_c318d_row0_col4\" class=\"data row0 col4\" >0.9792</td>\n",
              "      <td id=\"T_c318d_row0_col5\" class=\"data row0 col5\" >0.9778</td>\n",
              "      <td id=\"T_c318d_row0_col6\" class=\"data row0 col6\" >0.9667</td>\n",
              "      <td id=\"T_c318d_row0_col7\" class=\"data row0 col7\" >0.9674</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2jHuRce4ZyJ"
      },
      "source": [
        "The predictions (Label) made is as follows:-\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPpVAqui4dXa"
      },
      "source": [
        "val_rf_pred"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHqgTlqP4u5_"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**14-Make Predictions on Test Data**</p>"
      ],
      "metadata": {
        "id": "yX0mFC1Kt41j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are almost done! We finalize the model and predict it with the original dataset. Let’s read the final dataset. As we can see, it has added labels and scores."
      ],
      "metadata": {
        "id": "OtyC7sc1M8c6"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NLEEGb04mXt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "899ee950-9678-4ca0-b671-75535e9a6538"
      },
      "source": [
        "# predictions on new datasetRESULT_TEXT\n",
        "pred_new_rf = predict_model(tuned_lr, data = data_unseen) #new_data is pd dataframe\n",
        "#pred_new_rf = pred_new_rf.rename(columns={‘RESULT_TEXT’:’prediction’})\n",
        "pred_new_rf = pred_new_rf.rename(columns={'RESULT_TEXT':'Prediction'})\n",
        "pred_new_rf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0e1bc4c7d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_4ccc4\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_4ccc4_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_4ccc4_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_4ccc4_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_4ccc4_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_4ccc4_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_4ccc4_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_4ccc4_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_4ccc4_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_4ccc4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_4ccc4_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_4ccc4_row0_col1\" class=\"data row0 col1\" >1.0000</td>\n",
              "      <td id=\"T_4ccc4_row0_col2\" class=\"data row0 col2\" >0</td>\n",
              "      <td id=\"T_4ccc4_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
              "      <td id=\"T_4ccc4_row0_col4\" class=\"data row0 col4\" >1.0000</td>\n",
              "      <td id=\"T_4ccc4_row0_col5\" class=\"data row0 col5\" >1.0000</td>\n",
              "      <td id=\"T_4ccc4_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
              "      <td id=\"T_4ccc4_row0_col7\" class=\"data row0 col7\" >0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width         species  \\\n",
              "0           5.8          2.7           5.1          1.9  Iris-virginica   \n",
              "1           6.8          3.2           5.9          2.3  Iris-virginica   \n",
              "2           6.7          3.3           5.7          2.5  Iris-virginica   \n",
              "3           6.7          3.0           5.2          2.3  Iris-virginica   \n",
              "4           6.3          2.5           5.0          1.9  Iris-virginica   \n",
              "5           6.5          3.0           5.2          2.0  Iris-virginica   \n",
              "6           6.2          3.4           5.4          2.3  Iris-virginica   \n",
              "7           5.9          3.0           5.1          1.8  Iris-virginica   \n",
              "\n",
              "  prediction_label  prediction_score  \n",
              "0   Iris-virginica            0.8141  \n",
              "1   Iris-virginica            0.9827  \n",
              "2   Iris-virginica            0.9817  \n",
              "3   Iris-virginica            0.9104  \n",
              "4   Iris-virginica            0.7621  \n",
              "5   Iris-virginica            0.8372  \n",
              "6   Iris-virginica            0.9426  \n",
              "7   Iris-virginica            0.7418  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa984a44-51fa-4861-adc2-03898d8688a2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "      <th>prediction_label</th>\n",
              "      <th>prediction_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.8141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.9827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.9817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.9104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.7621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.8372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.9426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.7418</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa984a44-51fa-4861-adc2-03898d8688a2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fa984a44-51fa-4861-adc2-03898d8688a2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fa984a44-51fa-4861-adc2-03898d8688a2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-98d835c8-1e1e-4f5f-8e85-b7bf381486a5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98d835c8-1e1e-4f5f-8e85-b7bf381486a5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-98d835c8-1e1e-4f5f-8e85-b7bf381486a5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0b6d9af4-6bfd-4887-aca5-f559033450d3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('pred_new_rf')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0b6d9af4-6bfd-4887-aca5-f559033450d3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('pred_new_rf');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pred_new_rf",
              "summary": "{\n  \"name\": \"pred_new_rf\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5.800000190734863,\n          6.800000190734863,\n          6.199999809265137\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2.700000047683716,\n          3.200000047683716,\n          3.4000000953674316\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.099999904632568,\n          5.900000095367432,\n          5.400000095367432\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.299999952316284,\n          1.7999999523162842,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09587160684999495,\n        \"min\": 0.7418,\n        \"max\": 0.9827,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9827\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_final = finalize_model(lr)\n",
        "predict_rf = predict_model(rf_final,data)\n",
        "\n",
        "predict_rf.head()"
      ],
      "metadata": {
        "id": "ju5COIRoNAOg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "e19ae1d8-d0e1-4435-8057-536e06c62626"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7e0e1cc15490>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_1a3d6\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_1a3d6_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_1a3d6_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_1a3d6_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_1a3d6_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_1a3d6_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_1a3d6_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_1a3d6_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_1a3d6_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_1a3d6_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_1a3d6_row0_col0\" class=\"data row0 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_1a3d6_row0_col1\" class=\"data row0 col1\" >0.9718</td>\n",
              "      <td id=\"T_1a3d6_row0_col2\" class=\"data row0 col2\" >0.9981</td>\n",
              "      <td id=\"T_1a3d6_row0_col3\" class=\"data row0 col3\" >0.9718</td>\n",
              "      <td id=\"T_1a3d6_row0_col4\" class=\"data row0 col4\" >0.9724</td>\n",
              "      <td id=\"T_1a3d6_row0_col5\" class=\"data row0 col5\" >0.9718</td>\n",
              "      <td id=\"T_1a3d6_row0_col6\" class=\"data row0 col6\" >0.9577</td>\n",
              "      <td id=\"T_1a3d6_row0_col7\" class=\"data row0 col7\" >0.9580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width          species  \\\n",
              "0           5.8          2.7           3.9          1.2  Iris-versicolor   \n",
              "1           5.7          2.8           4.5          1.3  Iris-versicolor   \n",
              "2           7.7          3.0           6.1          2.3   Iris-virginica   \n",
              "3           5.8          2.6           4.0          1.2  Iris-versicolor   \n",
              "4           7.0          3.2           4.7          1.4  Iris-versicolor   \n",
              "\n",
              "  prediction_label  prediction_score  \n",
              "0  Iris-versicolor            0.9565  \n",
              "1  Iris-versicolor            0.8601  \n",
              "2   Iris-virginica            0.9883  \n",
              "3  Iris-versicolor            0.9570  \n",
              "4  Iris-versicolor            0.8740  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d07f81b-b96c-42da-8b76-3c3e2f60e9bf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "      <th>prediction_label</th>\n",
              "      <th>prediction_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "      <td>0.9565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.3</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "      <td>0.8601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>0.9883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "      <td>0.9570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "      <td>Iris-versicolor</td>\n",
              "      <td>0.8740</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d07f81b-b96c-42da-8b76-3c3e2f60e9bf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d07f81b-b96c-42da-8b76-3c3e2f60e9bf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d07f81b-b96c-42da-8b76-3c3e2f60e9bf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1d13b0a4-5483-42c3-bf08-6701f8c884cd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d13b0a4-5483-42c3-bf08-6701f8c884cd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1d13b0a4-5483-42c3-bf08-6701f8c884cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "predict_rf",
              "summary": "{\n  \"name\": \"predict_rf\",\n  \"rows\": 142,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          7.199999809265137,\n          6.0,\n          4.300000190734863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.9000000953674316,\n          2.4000000953674316,\n          2.700000047683716\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          4.900000095367432,\n          4.599999904632568,\n          5.800000190734863\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          1.2000000476837158,\n          2.5,\n          1.7000000476837158\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-versicolor\",\n          \"Iris-virginica\",\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-versicolor\",\n          \"Iris-virginica\",\n          \"Iris-setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11914241536642907,\n        \"min\": 0.5136,\n        \"max\": 0.999,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          0.5482,\n          0.9881,\n          0.9961\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6gWngOp5nVV"
      },
      "source": [
        "RandomForestRegressor submission Scores(RMSE) : 4234.403588306586.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**15-Finalize and Save pipline**</p>"
      ],
      "metadata": {
        "id": "SB6bb_FAuCny"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdn3dsRUEpgV"
      },
      "source": [
        "**Finalize and Save Pipeline**\n",
        "\n",
        "Let’s now finalize the best model i.e. train the best model on the entire dataset including the test set and then save the pipeline as a pickle file.\n",
        "\n",
        "Caution: One final word of caution. Once the model is finalized using finalize_model(), the entire dataset including the test/hold-out set is used for training. As such, if the model is used for predictions on the hold-out set after finalize_model() is used, the information grid printed will be misleading as you are trying to predict on the same data that was used for modeling. In order to demonstrate this point only, we will use final_rf under predict_model() to compare the information grid with the one above in section 11\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlF9n42bEsLY"
      },
      "source": [
        "# finalize the model\n",
        "final_best = finalize_model(lr)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6NFnz7vFUXP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "476306b8-feef-4a18-a81c-874d62af51c7"
      },
      "source": [
        "# save model to disk\n",
        "save_model(final_best, 'diamond-pipeline')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=Memory(location=None),\n",
              "          steps=[('label_encoding',\n",
              "                  TransformerWrapperWithInverse(exclude=None, include=None,\n",
              "                                                transformer=LabelEncoder())),\n",
              "                 ('numerical_imputer',\n",
              "                  TransformerWrapper(exclude=None,\n",
              "                                     include=['sepal_length', 'sepal_width',\n",
              "                                              'petal_length', 'petal_width'],\n",
              "                                     transformer=SimpleImputer(add_indicator=False,\n",
              "                                                               copy=True,\n",
              "                                                               fill_value=None,\n",
              "                                                               keep_empt...\n",
              "                                                               fill_value=None,\n",
              "                                                               keep_empty_features=False,\n",
              "                                                               missing_values=nan,\n",
              "                                                               strategy='most_frequent'))),\n",
              "                 ('actual_estimator',\n",
              "                  LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
              "                                     fit_intercept=True, intercept_scaling=1,\n",
              "                                     l1_ratio=None, max_iter=1000,\n",
              "                                     multi_class='auto', n_jobs=None,\n",
              "                                     penalty='l2', random_state=123,\n",
              "                                     solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                                     warm_start=False))],\n",
              "          verbose=False),\n",
              " 'diamond-pipeline.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RAvAJItdFe13"
      },
      "source": [
        "save_model function will save the entire pipeline (including the model) as a pickle file on your local disk. By default, it will save the file in the same folder as your Notebook or script is in but you can pass the complete path as well if you would like:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzC0_ZK7FoxU"
      },
      "source": [
        "save_model(final_best, 'E:/Major Revision/finarf'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_model(final_best);"
      ],
      "metadata": {
        "id": "QZEDn6CXLLUV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**16-Load the save model**</p>"
      ],
      "metadata": {
        "id": "50HWSabbVtcw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "saved_final_lr = load_model('diamond-pipeline')"
      ],
      "metadata": {
        "id": "4D2iRcy7V6qO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fac03e62-fd1f-4f42-d4a7-54efde365750"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQgWLzcm4s3i"
      },
      "source": [
        "\n",
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**2-Regression**</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Installation**</p>"
      ],
      "metadata": {
        "id": "tOoxy3OrSjBA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pycaret"
      ],
      "metadata": {
        "id": "qCXvQKJqSiNh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4888f2e3-e19d-475a-e6ac-7d55a2c181ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pycaret in /usr/local/lib/python3.11/dist-packages (3.3.2)\n",
            "Requirement already satisfied: ipython>=5.5.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (7.34.0)\n",
            "Requirement already satisfied: ipywidgets>=7.6.5 in /usr/local/lib/python3.11/dist-packages (from pycaret) (7.7.1)\n",
            "Requirement already satisfied: tqdm>=4.62.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (4.67.1)\n",
            "Requirement already satisfied: numpy<1.27,>=1.21 in /usr/local/lib/python3.11/dist-packages (from pycaret) (1.26.4)\n",
            "Requirement already satisfied: pandas<2.2.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (2.1.4)\n",
            "Requirement already satisfied: jinja2>=3 in /usr/local/lib/python3.11/dist-packages (from pycaret) (3.1.6)\n",
            "Requirement already satisfied: scipy<=1.11.4,>=1.6.1 in /usr/local/lib/python3.11/dist-packages (from pycaret) (1.11.4)\n",
            "Requirement already satisfied: joblib<1.4,>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (1.3.2)\n",
            "Requirement already satisfied: scikit-learn>1.4.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (1.4.2)\n",
            "Requirement already satisfied: pyod>=1.1.3 in /usr/local/lib/python3.11/dist-packages (from pycaret) (2.0.4)\n",
            "Requirement already satisfied: imbalanced-learn>=0.12.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (0.13.0)\n",
            "Requirement already satisfied: category-encoders>=2.4.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (2.7.0)\n",
            "Requirement already satisfied: lightgbm>=3.0.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (4.5.0)\n",
            "Requirement already satisfied: numba>=0.55.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (0.60.0)\n",
            "Requirement already satisfied: requests>=2.27.1 in /usr/local/lib/python3.11/dist-packages (from pycaret) (2.32.3)\n",
            "Requirement already satisfied: psutil>=5.9.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (5.9.5)\n",
            "Requirement already satisfied: markupsafe>=2.0.1 in /usr/local/lib/python3.11/dist-packages (from pycaret) (3.0.2)\n",
            "Requirement already satisfied: importlib-metadata>=4.12.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (8.6.1)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (5.10.4)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.11/dist-packages (from pycaret) (3.1.1)\n",
            "Requirement already satisfied: deprecation>=2.1.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (2.1.0)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from pycaret) (3.5.0)\n",
            "Requirement already satisfied: matplotlib<3.8.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (3.7.5)\n",
            "Requirement already satisfied: scikit-plot>=0.3.7 in /usr/local/lib/python3.11/dist-packages (from pycaret) (0.3.7)\n",
            "Requirement already satisfied: yellowbrick>=1.4 in /usr/local/lib/python3.11/dist-packages (from pycaret) (1.5)\n",
            "Requirement already satisfied: plotly>=5.14.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (5.24.1)\n",
            "Requirement already satisfied: kaleido>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from pycaret) (0.2.1)\n",
            "Requirement already satisfied: schemdraw==0.15 in /usr/local/lib/python3.11/dist-packages (from pycaret) (0.15)\n",
            "Requirement already satisfied: plotly-resampler>=0.8.3.1 in /usr/local/lib/python3.11/dist-packages (from pycaret) (0.10.0)\n",
            "Requirement already satisfied: statsmodels>=0.12.1 in /usr/local/lib/python3.11/dist-packages (from pycaret) (0.14.4)\n",
            "Requirement already satisfied: sktime==0.26.0 in /usr/local/lib/python3.11/dist-packages (from pycaret) (0.26.0)\n",
            "Requirement already satisfied: tbats>=1.1.3 in /usr/local/lib/python3.11/dist-packages (from pycaret) (1.1.3)\n",
            "Requirement already satisfied: pmdarima>=2.0.4 in /usr/local/lib/python3.11/dist-packages (from pycaret) (2.0.4)\n",
            "Requirement already satisfied: wurlitzer in /usr/local/lib/python3.11/dist-packages (from pycaret) (3.1.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from sktime==0.26.0->pycaret) (24.2)\n",
            "Requirement already satisfied: scikit-base<0.8.0 in /usr/local/lib/python3.11/dist-packages (from sktime==0.26.0->pycaret) (0.7.8)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.11/dist-packages (from category-encoders>=2.4.0->pycaret) (1.0.1)\n",
            "Requirement already satisfied: sklearn-compat<1,>=0.1 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn>=0.12.0->pycaret) (0.1.3)\n",
            "Requirement already satisfied: threadpoolctl<4,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from imbalanced-learn>=0.12.0->pycaret) (3.6.0)\n",
            "Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.11/dist-packages (from importlib-metadata>=4.12.0->pycaret) (3.21.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (75.1.0)\n",
            "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (0.19.2)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (5.7.1)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (3.0.50)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (2.18.0)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (0.1.7)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.11/dist-packages (from ipython>=5.5.0->pycaret) (4.9.0)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.11/dist-packages (from ipywidgets>=7.6.5->pycaret) (6.17.1)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.11/dist-packages (from ipywidgets>=7.6.5->pycaret) (0.2.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.11/dist-packages (from ipywidgets>=7.6.5->pycaret) (3.6.10)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from ipywidgets>=7.6.5->pycaret) (3.0.13)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret) (4.56.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret) (1.4.8)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret) (11.1.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret) (3.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib<3.8.0->pycaret) (2.8.2)\n",
            "Requirement already satisfied: fastjsonschema>=2.15 in /usr/local/lib/python3.11/dist-packages (from nbformat>=4.2.0->pycaret) (2.21.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.11/dist-packages (from nbformat>=4.2.0->pycaret) (4.23.0)\n",
            "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /usr/local/lib/python3.11/dist-packages (from nbformat>=4.2.0->pycaret) (5.7.2)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.11/dist-packages (from numba>=0.55.0->pycaret) (0.43.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas<2.2.0->pycaret) (2025.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.11/dist-packages (from pandas<2.2.0->pycaret) (2025.1)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly>=5.14.0->pycaret) (9.0.0)\n",
            "Requirement already satisfied: dash>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from plotly-resampler>=0.8.3.1->pycaret) (3.0.1)\n",
            "Requirement already satisfied: orjson<4.0.0,>=3.8.0 in /usr/local/lib/python3.11/dist-packages (from plotly-resampler>=0.8.3.1->pycaret) (3.10.15)\n",
            "Requirement already satisfied: tsdownsample>=0.1.3 in /usr/local/lib/python3.11/dist-packages (from plotly-resampler>=0.8.3.1->pycaret) (0.1.4.1)\n",
            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.11/dist-packages (from pmdarima>=2.0.4->pycaret) (3.0.12)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.11/dist-packages (from pmdarima>=2.0.4->pycaret) (2.3.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.27.1->pycaret) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.27.1->pycaret) (3.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.27.1->pycaret) (2025.1.31)\n",
            "Requirement already satisfied: Flask<3.1,>=1.0.4 in /usr/local/lib/python3.11/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (3.0.3)\n",
            "Requirement already satisfied: Werkzeug<3.1 in /usr/local/lib/python3.11/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (3.0.6)\n",
            "Requirement already satisfied: typing-extensions>=4.1.1 in /usr/local/lib/python3.11/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (4.12.2)\n",
            "Requirement already satisfied: retrying in /usr/local/lib/python3.11/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (1.3.4)\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.11/dist-packages (from dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (1.6.0)\n",
            "Requirement already satisfied: debugpy>=1.0 in /usr/local/lib/python3.11/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.5->pycaret) (1.8.0)\n",
            "Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.11/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.5->pycaret) (6.1.12)\n",
            "Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.11/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.5->pycaret) (24.0.1)\n",
            "Requirement already satisfied: tornado>=6.1 in /usr/local/lib/python3.11/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.6.5->pycaret) (6.4.2)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.4 in /usr/local/lib/python3.11/dist-packages (from jedi>=0.16->ipython>=5.5.0->pycaret) (0.8.4)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret) (25.3.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret) (2024.10.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret) (0.36.2)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.11/dist-packages (from jsonschema>=2.6->nbformat>=4.2.0->pycaret) (0.23.1)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.11/dist-packages (from jupyter-core!=5.0.*,>=4.12->nbformat>=4.2.0->pycaret) (4.3.7)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.11/dist-packages (from pexpect>4.3->ipython>=5.5.0->pycaret) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.11/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.5.0->pycaret) (0.2.13)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib<3.8.0->pycaret) (1.17.0)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.11/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (6.5.7)\n",
            "Requirement already satisfied: itsdangerous>=2.1.2 in /usr/local/lib/python3.11/dist-packages (from Flask<3.1,>=1.0.4->dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (2.2.0)\n",
            "Requirement already satisfied: click>=8.1.3 in /usr/local/lib/python3.11/dist-packages (from Flask<3.1,>=1.0.4->dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (8.1.8)\n",
            "Requirement already satisfied: blinker>=1.6.2 in /usr/local/lib/python3.11/dist-packages (from Flask<3.1,>=1.0.4->dash>=2.9.0->plotly-resampler>=0.8.3.1->pycaret) (1.9.0)\n",
            "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (23.1.0)\n",
            "Requirement already satisfied: nbconvert>=5 in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (7.16.6)\n",
            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.8.3)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.18.1)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.21.1)\n",
            "Requirement already satisfied: nbclassic>=0.4.7 in /usr/local/lib/python3.11/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.2.0)\n",
            "Requirement already satisfied: notebook-shim>=0.2.3 in /usr/local/lib/python3.11/dist-packages (from nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.2.4)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (4.13.3)\n",
            "Requirement already satisfied: bleach!=5.0.0 in /usr/local/lib/python3.11/dist-packages (from bleach[css]!=5.0.0->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (6.2.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.7.1)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.3.0)\n",
            "Requirement already satisfied: mistune<4,>=2.0.3 in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (3.1.3)\n",
            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.10.2)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.5.1)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.11/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (21.2.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.11/dist-packages (from bleach!=5.0.0->bleach[css]!=5.0.0->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.5.1)\n",
            "Requirement already satisfied: tinycss2<1.5,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from bleach[css]!=5.0.0->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.4.0)\n",
            "Requirement already satisfied: jupyter-server<3,>=1.8 in /usr/local/lib/python3.11/dist-packages (from notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.16.0)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.17.1)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.6)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.11/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.22)\n",
            "Requirement already satisfied: anyio>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (4.9.0)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.11/dist-packages (from jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.8.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio>=3.1.0->jupyter-server<3,>=1.8->notebook-shim>=0.2.3->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.3.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Dataset**</p>"
      ],
      "metadata": {
        "id": "znrPeKKWTG2D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from pycaret.regression import *\n",
        "\n",
        "# Load dataset\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "data = fetch_california_housing(as_frame=True)\n",
        "df = data.frame\n",
        "\n",
        "# Display first few rows\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HxPyY6BoTQ_c",
        "outputId": "d4b5f078-e933-4fec-add1-6aa9b26c4047"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  MedHouseVal  \n",
              "0    -122.23        4.526  \n",
              "1    -122.22        3.585  \n",
              "2    -122.24        3.521  \n",
              "3    -122.25        3.413  \n",
              "4    -122.25        3.422  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e67474e0-7ede-4955-bcb1-f7d964b1dbdf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>MedHouseVal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "      <td>4.526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "      <td>3.585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "      <td>3.521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.422</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e67474e0-7ede-4955-bcb1-f7d964b1dbdf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e67474e0-7ede-4955-bcb1-f7d964b1dbdf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e67474e0-7ede-4955-bcb1-f7d964b1dbdf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c590b390-7a7f-41a4-a60c-03187586e2ba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c590b390-7a7f-41a4-a60c-03187586e2ba')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c590b390-7a7f-41a4-a60c-03187586e2ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452732,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.585557612111637,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243205,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47391085679546435,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.4621217653375,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213591,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571117,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.003531723502581,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MedHouseVal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1539561587441483,\n        \"min\": 0.14999,\n        \"max\": 5.00001,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          1.943,\n          3.79,\n          2.301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Setting Up PyCaret Environment**</p>"
      ],
      "metadata": {
        "id": "3l1c1AahTWmJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize PyCaret regression setup\n",
        "regression_setup = setup(data=df, target='MedHouseVal', session_id=123)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "TaQt5aalUM0X",
        "outputId": "b9c12c2f-adef-43ed-deb1-c9e654aed98e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7db48f73b690>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_82be2_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_82be2\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_82be2_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_82be2_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_82be2_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_82be2_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_82be2_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_82be2_row1_col1\" class=\"data row1 col1\" >MedHouseVal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_82be2_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_82be2_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_82be2_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_82be2_row3_col1\" class=\"data row3 col1\" >(20640, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_82be2_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_82be2_row4_col1\" class=\"data row4 col1\" >(20640, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_82be2_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_82be2_row5_col1\" class=\"data row5 col1\" >(14447, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_82be2_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_82be2_row6_col1\" class=\"data row6 col1\" >(6193, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_82be2_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_82be2_row7_col1\" class=\"data row7 col1\" >8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_82be2_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_82be2_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_82be2_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_82be2_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_82be2_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_82be2_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_82be2_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_82be2_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_82be2_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_82be2_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_82be2_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_82be2_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_82be2_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_82be2_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_82be2_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_82be2_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_82be2_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_82be2_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_82be2_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_82be2_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_82be2_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_82be2_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_82be2_row18_col1\" class=\"data row18 col1\" >0069</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVcEsi-46IHU"
      },
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Comparing Regression Models**</p>\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model = compare_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645,
          "referenced_widgets": [
            "d871a38d36a846799417b39a65e53b4c",
            "2009f88ed8f4461d8e0eb0831ff8ba79",
            "d0b7cbd000534e518f8b8e5b39b62cbc",
            "8247267110714c71b74bdd7b3cb5424b",
            "e4e519d2695f4fba9360ae89637fb382",
            "1987a03e88bc4996b2813d448dbce65a",
            "46c6bf976d574385a33f229ae6b2ddb4",
            "7d6b587c5cad4a26adcfa660f1f50721",
            "8b557138e0fa4d5ea6fae0076642d2c5",
            "edfbb5e8b86045119a711ada35f85bc7",
            "264f083602464f99b5a03b8d58ca0e43"
          ]
        },
        "id": "cHdfouQbUgFp",
        "outputId": "27aa370a-b83f-4d51-a4d3-ee32ebbe17b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7db3447bad90>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_5b555 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_5b555_row0_col0, #T_5b555_row1_col0, #T_5b555_row1_col1, #T_5b555_row1_col2, #T_5b555_row1_col3, #T_5b555_row1_col4, #T_5b555_row1_col5, #T_5b555_row1_col6, #T_5b555_row2_col0, #T_5b555_row2_col1, #T_5b555_row2_col2, #T_5b555_row2_col3, #T_5b555_row2_col4, #T_5b555_row2_col5, #T_5b555_row2_col6, #T_5b555_row3_col0, #T_5b555_row3_col1, #T_5b555_row3_col2, #T_5b555_row3_col3, #T_5b555_row3_col4, #T_5b555_row3_col5, #T_5b555_row3_col6, #T_5b555_row4_col0, #T_5b555_row4_col1, #T_5b555_row4_col2, #T_5b555_row4_col3, #T_5b555_row4_col4, #T_5b555_row4_col5, #T_5b555_row4_col6, #T_5b555_row5_col0, #T_5b555_row5_col1, #T_5b555_row5_col2, #T_5b555_row5_col3, #T_5b555_row5_col4, #T_5b555_row5_col5, #T_5b555_row5_col6, #T_5b555_row6_col0, #T_5b555_row6_col1, #T_5b555_row6_col2, #T_5b555_row6_col3, #T_5b555_row6_col4, #T_5b555_row6_col5, #T_5b555_row6_col6, #T_5b555_row7_col0, #T_5b555_row7_col1, #T_5b555_row7_col2, #T_5b555_row7_col3, #T_5b555_row7_col4, #T_5b555_row7_col5, #T_5b555_row7_col6, #T_5b555_row8_col0, #T_5b555_row8_col1, #T_5b555_row8_col2, #T_5b555_row8_col3, #T_5b555_row8_col4, #T_5b555_row8_col5, #T_5b555_row8_col6, #T_5b555_row9_col0, #T_5b555_row9_col1, #T_5b555_row9_col2, #T_5b555_row9_col3, #T_5b555_row9_col4, #T_5b555_row9_col5, #T_5b555_row9_col6, #T_5b555_row10_col0, #T_5b555_row10_col1, #T_5b555_row10_col2, #T_5b555_row10_col3, #T_5b555_row10_col4, #T_5b555_row10_col5, #T_5b555_row10_col6, #T_5b555_row11_col0, #T_5b555_row11_col1, #T_5b555_row11_col2, #T_5b555_row11_col3, #T_5b555_row11_col4, #T_5b555_row11_col5, #T_5b555_row11_col6, #T_5b555_row12_col0, #T_5b555_row12_col1, #T_5b555_row12_col2, #T_5b555_row12_col3, #T_5b555_row12_col4, #T_5b555_row12_col5, #T_5b555_row12_col6, #T_5b555_row13_col0, #T_5b555_row13_col1, #T_5b555_row13_col2, #T_5b555_row13_col3, #T_5b555_row13_col4, #T_5b555_row13_col5, #T_5b555_row13_col6, #T_5b555_row14_col0, #T_5b555_row14_col1, #T_5b555_row14_col2, #T_5b555_row14_col3, #T_5b555_row14_col4, #T_5b555_row14_col5, #T_5b555_row14_col6, #T_5b555_row15_col0, #T_5b555_row15_col1, #T_5b555_row15_col2, #T_5b555_row15_col3, #T_5b555_row15_col4, #T_5b555_row15_col5, #T_5b555_row15_col6, #T_5b555_row16_col0, #T_5b555_row16_col1, #T_5b555_row16_col2, #T_5b555_row16_col3, #T_5b555_row16_col4, #T_5b555_row16_col5, #T_5b555_row16_col6, #T_5b555_row17_col0, #T_5b555_row17_col1, #T_5b555_row17_col2, #T_5b555_row17_col3, #T_5b555_row17_col4, #T_5b555_row17_col5, #T_5b555_row17_col6, #T_5b555_row18_col0, #T_5b555_row18_col1, #T_5b555_row18_col2, #T_5b555_row18_col3, #T_5b555_row18_col4, #T_5b555_row18_col5, #T_5b555_row18_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_5b555_row0_col1, #T_5b555_row0_col2, #T_5b555_row0_col3, #T_5b555_row0_col4, #T_5b555_row0_col5, #T_5b555_row0_col6 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_5b555_row0_col7, #T_5b555_row1_col7, #T_5b555_row2_col7, #T_5b555_row3_col7, #T_5b555_row4_col7, #T_5b555_row5_col7, #T_5b555_row6_col7, #T_5b555_row7_col7, #T_5b555_row8_col7, #T_5b555_row9_col7, #T_5b555_row10_col7, #T_5b555_row11_col7, #T_5b555_row12_col7, #T_5b555_row13_col7, #T_5b555_row14_col7, #T_5b555_row15_col7, #T_5b555_row17_col7, #T_5b555_row18_col7 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_5b555_row16_col7 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_5b555\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_5b555_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_5b555_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_5b555_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_5b555_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_5b555_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_5b555_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_5b555_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_5b555_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row0\" class=\"row_heading level0 row0\" >lightgbm</th>\n",
              "      <td id=\"T_5b555_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_5b555_row0_col1\" class=\"data row0 col1\" >0.3167</td>\n",
              "      <td id=\"T_5b555_row0_col2\" class=\"data row0 col2\" >0.2248</td>\n",
              "      <td id=\"T_5b555_row0_col3\" class=\"data row0 col3\" >0.4740</td>\n",
              "      <td id=\"T_5b555_row0_col4\" class=\"data row0 col4\" >0.8309</td>\n",
              "      <td id=\"T_5b555_row0_col5\" class=\"data row0 col5\" >0.1432</td>\n",
              "      <td id=\"T_5b555_row0_col6\" class=\"data row0 col6\" >0.1785</td>\n",
              "      <td id=\"T_5b555_row0_col7\" class=\"data row0 col7\" >2.4620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row1\" class=\"row_heading level0 row1\" >xgboost</th>\n",
              "      <td id=\"T_5b555_row1_col0\" class=\"data row1 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_5b555_row1_col1\" class=\"data row1 col1\" >0.3178</td>\n",
              "      <td id=\"T_5b555_row1_col2\" class=\"data row1 col2\" >0.2332</td>\n",
              "      <td id=\"T_5b555_row1_col3\" class=\"data row1 col3\" >0.4826</td>\n",
              "      <td id=\"T_5b555_row1_col4\" class=\"data row1 col4\" >0.8244</td>\n",
              "      <td id=\"T_5b555_row1_col5\" class=\"data row1 col5\" >0.1463</td>\n",
              "      <td id=\"T_5b555_row1_col6\" class=\"data row1 col6\" >0.1790</td>\n",
              "      <td id=\"T_5b555_row1_col7\" class=\"data row1 col7\" >0.4190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
              "      <td id=\"T_5b555_row2_col0\" class=\"data row2 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_5b555_row2_col1\" class=\"data row2 col1\" >0.3325</td>\n",
              "      <td id=\"T_5b555_row2_col2\" class=\"data row2 col2\" >0.2578</td>\n",
              "      <td id=\"T_5b555_row2_col3\" class=\"data row2 col3\" >0.5076</td>\n",
              "      <td id=\"T_5b555_row2_col4\" class=\"data row2 col4\" >0.8063</td>\n",
              "      <td id=\"T_5b555_row2_col5\" class=\"data row2 col5\" >0.1495</td>\n",
              "      <td id=\"T_5b555_row2_col6\" class=\"data row2 col6\" >0.1847</td>\n",
              "      <td id=\"T_5b555_row2_col7\" class=\"data row2 col7\" >3.7950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
              "      <td id=\"T_5b555_row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_5b555_row3_col1\" class=\"data row3 col1\" >0.3379</td>\n",
              "      <td id=\"T_5b555_row3_col2\" class=\"data row3 col2\" >0.2668</td>\n",
              "      <td id=\"T_5b555_row3_col3\" class=\"data row3 col3\" >0.5163</td>\n",
              "      <td id=\"T_5b555_row3_col4\" class=\"data row3 col4\" >0.7995</td>\n",
              "      <td id=\"T_5b555_row3_col5\" class=\"data row3 col5\" >0.1537</td>\n",
              "      <td id=\"T_5b555_row3_col6\" class=\"data row3 col6\" >0.1910</td>\n",
              "      <td id=\"T_5b555_row3_col7\" class=\"data row3 col7\" >10.4350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row4\" class=\"row_heading level0 row4\" >gbr</th>\n",
              "      <td id=\"T_5b555_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_5b555_row4_col1\" class=\"data row4 col1\" >0.3730</td>\n",
              "      <td id=\"T_5b555_row4_col2\" class=\"data row4 col2\" >0.2891</td>\n",
              "      <td id=\"T_5b555_row4_col3\" class=\"data row4 col3\" >0.5375</td>\n",
              "      <td id=\"T_5b555_row4_col4\" class=\"data row4 col4\" >0.7828</td>\n",
              "      <td id=\"T_5b555_row4_col5\" class=\"data row4 col5\" >0.1635</td>\n",
              "      <td id=\"T_5b555_row4_col6\" class=\"data row4 col6\" >0.2123</td>\n",
              "      <td id=\"T_5b555_row4_col7\" class=\"data row4 col7\" >2.9080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
              "      <td id=\"T_5b555_row5_col0\" class=\"data row5 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_5b555_row5_col1\" class=\"data row5 col1\" >0.4640</td>\n",
              "      <td id=\"T_5b555_row5_col2\" class=\"data row5 col2\" >0.5259</td>\n",
              "      <td id=\"T_5b555_row5_col3\" class=\"data row5 col3\" >0.7246</td>\n",
              "      <td id=\"T_5b555_row5_col4\" class=\"data row5 col4\" >0.6045</td>\n",
              "      <td id=\"T_5b555_row5_col5\" class=\"data row5 col5\" >0.2124</td>\n",
              "      <td id=\"T_5b555_row5_col6\" class=\"data row5 col6\" >0.2512</td>\n",
              "      <td id=\"T_5b555_row5_col7\" class=\"data row5 col7\" >0.1590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row6\" class=\"row_heading level0 row6\" >br</th>\n",
              "      <td id=\"T_5b555_row6_col0\" class=\"data row6 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_5b555_row6_col1\" class=\"data row6 col1\" >0.5337</td>\n",
              "      <td id=\"T_5b555_row6_col2\" class=\"data row6 col2\" >0.5339</td>\n",
              "      <td id=\"T_5b555_row6_col3\" class=\"data row6 col3\" >0.7304</td>\n",
              "      <td id=\"T_5b555_row6_col4\" class=\"data row6 col4\" >0.5999</td>\n",
              "      <td id=\"T_5b555_row6_col5\" class=\"data row6 col5\" >0.2246</td>\n",
              "      <td id=\"T_5b555_row6_col6\" class=\"data row6 col6\" >0.3178</td>\n",
              "      <td id=\"T_5b555_row6_col7\" class=\"data row6 col7\" >0.0240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row7\" class=\"row_heading level0 row7\" >lr</th>\n",
              "      <td id=\"T_5b555_row7_col0\" class=\"data row7 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_5b555_row7_col1\" class=\"data row7 col1\" >0.5337</td>\n",
              "      <td id=\"T_5b555_row7_col2\" class=\"data row7 col2\" >0.5339</td>\n",
              "      <td id=\"T_5b555_row7_col3\" class=\"data row7 col3\" >0.7305</td>\n",
              "      <td id=\"T_5b555_row7_col4\" class=\"data row7 col4\" >0.5998</td>\n",
              "      <td id=\"T_5b555_row7_col5\" class=\"data row7 col5\" >0.2246</td>\n",
              "      <td id=\"T_5b555_row7_col6\" class=\"data row7 col6\" >0.3177</td>\n",
              "      <td id=\"T_5b555_row7_col7\" class=\"data row7 col7\" >0.6600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row8\" class=\"row_heading level0 row8\" >ridge</th>\n",
              "      <td id=\"T_5b555_row8_col0\" class=\"data row8 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_5b555_row8_col1\" class=\"data row8 col1\" >0.5337</td>\n",
              "      <td id=\"T_5b555_row8_col2\" class=\"data row8 col2\" >0.5339</td>\n",
              "      <td id=\"T_5b555_row8_col3\" class=\"data row8 col3\" >0.7304</td>\n",
              "      <td id=\"T_5b555_row8_col4\" class=\"data row8 col4\" >0.5998</td>\n",
              "      <td id=\"T_5b555_row8_col5\" class=\"data row8 col5\" >0.2246</td>\n",
              "      <td id=\"T_5b555_row8_col6\" class=\"data row8 col6\" >0.3177</td>\n",
              "      <td id=\"T_5b555_row8_col7\" class=\"data row8 col7\" >0.0240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row9\" class=\"row_heading level0 row9\" >lar</th>\n",
              "      <td id=\"T_5b555_row9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_5b555_row9_col1\" class=\"data row9 col1\" >0.5419</td>\n",
              "      <td id=\"T_5b555_row9_col2\" class=\"data row9 col2\" >0.6005</td>\n",
              "      <td id=\"T_5b555_row9_col3\" class=\"data row9 col3\" >0.7738</td>\n",
              "      <td id=\"T_5b555_row9_col4\" class=\"data row9 col4\" >0.5507</td>\n",
              "      <td id=\"T_5b555_row9_col5\" class=\"data row9 col5\" >0.2305</td>\n",
              "      <td id=\"T_5b555_row9_col6\" class=\"data row9 col6\" >0.3127</td>\n",
              "      <td id=\"T_5b555_row9_col7\" class=\"data row9 col7\" >0.0250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row10\" class=\"row_heading level0 row10\" >en</th>\n",
              "      <td id=\"T_5b555_row10_col0\" class=\"data row10 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_5b555_row10_col1\" class=\"data row10 col1\" >0.6809</td>\n",
              "      <td id=\"T_5b555_row10_col2\" class=\"data row10 col2\" >0.7712</td>\n",
              "      <td id=\"T_5b555_row10_col3\" class=\"data row10 col3\" >0.8780</td>\n",
              "      <td id=\"T_5b555_row10_col4\" class=\"data row10 col4\" >0.4218</td>\n",
              "      <td id=\"T_5b555_row10_col5\" class=\"data row10 col5\" >0.2776</td>\n",
              "      <td id=\"T_5b555_row10_col6\" class=\"data row10 col6\" >0.4521</td>\n",
              "      <td id=\"T_5b555_row10_col7\" class=\"data row10 col7\" >0.0220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row11\" class=\"row_heading level0 row11\" >ada</th>\n",
              "      <td id=\"T_5b555_row11_col0\" class=\"data row11 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_5b555_row11_col1\" class=\"data row11 col1\" >0.7735</td>\n",
              "      <td id=\"T_5b555_row11_col2\" class=\"data row11 col2\" >0.8013</td>\n",
              "      <td id=\"T_5b555_row11_col3\" class=\"data row11 col3\" >0.8943</td>\n",
              "      <td id=\"T_5b555_row11_col4\" class=\"data row11 col4\" >0.3981</td>\n",
              "      <td id=\"T_5b555_row11_col5\" class=\"data row11 col5\" >0.3033</td>\n",
              "      <td id=\"T_5b555_row11_col6\" class=\"data row11 col6\" >0.5719</td>\n",
              "      <td id=\"T_5b555_row11_col7\" class=\"data row11 col7\" >0.8140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row12\" class=\"row_heading level0 row12\" >lasso</th>\n",
              "      <td id=\"T_5b555_row12_col0\" class=\"data row12 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_5b555_row12_col1\" class=\"data row12 col1\" >0.7690</td>\n",
              "      <td id=\"T_5b555_row12_col2\" class=\"data row12 col2\" >0.9550</td>\n",
              "      <td id=\"T_5b555_row12_col3\" class=\"data row12 col3\" >0.9770</td>\n",
              "      <td id=\"T_5b555_row12_col4\" class=\"data row12 col4\" >0.2843</td>\n",
              "      <td id=\"T_5b555_row12_col5\" class=\"data row12 col5\" >0.3078</td>\n",
              "      <td id=\"T_5b555_row12_col6\" class=\"data row12 col6\" >0.5184</td>\n",
              "      <td id=\"T_5b555_row12_col7\" class=\"data row12 col7\" >0.0220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row13\" class=\"row_heading level0 row13\" >llar</th>\n",
              "      <td id=\"T_5b555_row13_col0\" class=\"data row13 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_5b555_row13_col1\" class=\"data row13 col1\" >0.7690</td>\n",
              "      <td id=\"T_5b555_row13_col2\" class=\"data row13 col2\" >0.9550</td>\n",
              "      <td id=\"T_5b555_row13_col3\" class=\"data row13 col3\" >0.9770</td>\n",
              "      <td id=\"T_5b555_row13_col4\" class=\"data row13 col4\" >0.2843</td>\n",
              "      <td id=\"T_5b555_row13_col5\" class=\"data row13 col5\" >0.3078</td>\n",
              "      <td id=\"T_5b555_row13_col6\" class=\"data row13 col6\" >0.5184</td>\n",
              "      <td id=\"T_5b555_row13_col7\" class=\"data row13 col7\" >0.0230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row14\" class=\"row_heading level0 row14\" >knn</th>\n",
              "      <td id=\"T_5b555_row14_col0\" class=\"data row14 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_5b555_row14_col1\" class=\"data row14 col1\" >0.8314</td>\n",
              "      <td id=\"T_5b555_row14_col2\" class=\"data row14 col2\" >1.1722</td>\n",
              "      <td id=\"T_5b555_row14_col3\" class=\"data row14 col3\" >1.0825</td>\n",
              "      <td id=\"T_5b555_row14_col4\" class=\"data row14 col4\" >0.1210</td>\n",
              "      <td id=\"T_5b555_row14_col5\" class=\"data row14 col5\" >0.3379</td>\n",
              "      <td id=\"T_5b555_row14_col6\" class=\"data row14 col6\" >0.5318</td>\n",
              "      <td id=\"T_5b555_row14_col7\" class=\"data row14 col7\" >0.0440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row15\" class=\"row_heading level0 row15\" >omp</th>\n",
              "      <td id=\"T_5b555_row15_col0\" class=\"data row15 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_5b555_row15_col1\" class=\"data row15 col1\" >0.9128</td>\n",
              "      <td id=\"T_5b555_row15_col2\" class=\"data row15 col2\" >1.3352</td>\n",
              "      <td id=\"T_5b555_row15_col3\" class=\"data row15 col3\" >1.1552</td>\n",
              "      <td id=\"T_5b555_row15_col4\" class=\"data row15 col4\" >-0.0005</td>\n",
              "      <td id=\"T_5b555_row15_col5\" class=\"data row15 col5\" >0.3620</td>\n",
              "      <td id=\"T_5b555_row15_col6\" class=\"data row15 col6\" >0.6200</td>\n",
              "      <td id=\"T_5b555_row15_col7\" class=\"data row15 col7\" >0.0220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row16\" class=\"row_heading level0 row16\" >dummy</th>\n",
              "      <td id=\"T_5b555_row16_col0\" class=\"data row16 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_5b555_row16_col1\" class=\"data row16 col1\" >0.9128</td>\n",
              "      <td id=\"T_5b555_row16_col2\" class=\"data row16 col2\" >1.3355</td>\n",
              "      <td id=\"T_5b555_row16_col3\" class=\"data row16 col3\" >1.1553</td>\n",
              "      <td id=\"T_5b555_row16_col4\" class=\"data row16 col4\" >-0.0007</td>\n",
              "      <td id=\"T_5b555_row16_col5\" class=\"data row16 col5\" >0.3618</td>\n",
              "      <td id=\"T_5b555_row16_col6\" class=\"data row16 col6\" >0.6193</td>\n",
              "      <td id=\"T_5b555_row16_col7\" class=\"data row16 col7\" >0.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row17\" class=\"row_heading level0 row17\" >huber</th>\n",
              "      <td id=\"T_5b555_row17_col0\" class=\"data row17 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_5b555_row17_col1\" class=\"data row17 col1\" >0.5879</td>\n",
              "      <td id=\"T_5b555_row17_col2\" class=\"data row17 col2\" >3.2726</td>\n",
              "      <td id=\"T_5b555_row17_col3\" class=\"data row17 col3\" >1.2782</td>\n",
              "      <td id=\"T_5b555_row17_col4\" class=\"data row17 col4\" >-1.5294</td>\n",
              "      <td id=\"T_5b555_row17_col5\" class=\"data row17 col5\" >0.2451</td>\n",
              "      <td id=\"T_5b555_row17_col6\" class=\"data row17 col6\" >0.3322</td>\n",
              "      <td id=\"T_5b555_row17_col7\" class=\"data row17 col7\" >0.2250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_5b555_level0_row18\" class=\"row_heading level0 row18\" >par</th>\n",
              "      <td id=\"T_5b555_row18_col0\" class=\"data row18 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_5b555_row18_col1\" class=\"data row18 col1\" >1.6485</td>\n",
              "      <td id=\"T_5b555_row18_col2\" class=\"data row18 col2\" >6.2284</td>\n",
              "      <td id=\"T_5b555_row18_col3\" class=\"data row18 col3\" >2.2063</td>\n",
              "      <td id=\"T_5b555_row18_col4\" class=\"data row18 col4\" >-3.8036</td>\n",
              "      <td id=\"T_5b555_row18_col5\" class=\"data row18 col5\" >0.5180</td>\n",
              "      <td id=\"T_5b555_row18_col6\" class=\"data row18 col6\" >1.1031</td>\n",
              "      <td id=\"T_5b555_row18_col7\" class=\"data row18 col7\" >0.0860</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/81 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d871a38d36a846799417b39a65e53b4c"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaC_BlJo6Kq-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645,
          "referenced_widgets": [
            "c29526e92bc843d0abf9d761ea4f0e20",
            "2a0b9974e7a6494b9c29c2db19e05d9f",
            "52c5598c42a34139997570849868f64e",
            "a9af3ec259c34ecfbc6f4da2090b6f91",
            "4d67a00fcd2142b18bd52c661ab29dbd",
            "b232e7bf721d46feb84f54cd272d88d0",
            "22a005feec0b472080acc45f6f26ed9f",
            "a5c3d20a39ea461c81ef285398e3d081",
            "b97535e070454ae48a9df9da642e3020",
            "efb18980b95a42f8ae3f599215b87c5c",
            "5a9e637708f645d4828260cbcdec4580"
          ]
        },
        "outputId": "69f124b1-7b35-4b61-a10c-b13e7c037449"
      },
      "source": [
        "best= compare_models(n_select = 2, sort= 'RMSE')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7812da89a650>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_d6afd th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_d6afd_row0_col0, #T_d6afd_row1_col0, #T_d6afd_row1_col1, #T_d6afd_row1_col2, #T_d6afd_row1_col3, #T_d6afd_row1_col4, #T_d6afd_row1_col5, #T_d6afd_row1_col6, #T_d6afd_row2_col0, #T_d6afd_row2_col1, #T_d6afd_row2_col2, #T_d6afd_row2_col3, #T_d6afd_row2_col4, #T_d6afd_row2_col5, #T_d6afd_row2_col6, #T_d6afd_row3_col0, #T_d6afd_row3_col1, #T_d6afd_row3_col2, #T_d6afd_row3_col3, #T_d6afd_row3_col4, #T_d6afd_row3_col5, #T_d6afd_row3_col6, #T_d6afd_row4_col0, #T_d6afd_row4_col1, #T_d6afd_row4_col2, #T_d6afd_row4_col3, #T_d6afd_row4_col4, #T_d6afd_row4_col5, #T_d6afd_row4_col6, #T_d6afd_row5_col0, #T_d6afd_row5_col1, #T_d6afd_row5_col2, #T_d6afd_row5_col3, #T_d6afd_row5_col4, #T_d6afd_row5_col5, #T_d6afd_row5_col6, #T_d6afd_row6_col0, #T_d6afd_row6_col1, #T_d6afd_row6_col2, #T_d6afd_row6_col3, #T_d6afd_row6_col4, #T_d6afd_row6_col5, #T_d6afd_row6_col6, #T_d6afd_row7_col0, #T_d6afd_row7_col1, #T_d6afd_row7_col2, #T_d6afd_row7_col3, #T_d6afd_row7_col4, #T_d6afd_row7_col5, #T_d6afd_row7_col6, #T_d6afd_row8_col0, #T_d6afd_row8_col1, #T_d6afd_row8_col2, #T_d6afd_row8_col3, #T_d6afd_row8_col4, #T_d6afd_row8_col5, #T_d6afd_row8_col6, #T_d6afd_row9_col0, #T_d6afd_row9_col1, #T_d6afd_row9_col2, #T_d6afd_row9_col3, #T_d6afd_row9_col4, #T_d6afd_row9_col5, #T_d6afd_row9_col6, #T_d6afd_row10_col0, #T_d6afd_row10_col1, #T_d6afd_row10_col2, #T_d6afd_row10_col3, #T_d6afd_row10_col4, #T_d6afd_row10_col5, #T_d6afd_row10_col6, #T_d6afd_row11_col0, #T_d6afd_row11_col1, #T_d6afd_row11_col2, #T_d6afd_row11_col3, #T_d6afd_row11_col4, #T_d6afd_row11_col5, #T_d6afd_row11_col6, #T_d6afd_row12_col0, #T_d6afd_row12_col1, #T_d6afd_row12_col2, #T_d6afd_row12_col3, #T_d6afd_row12_col4, #T_d6afd_row12_col5, #T_d6afd_row12_col6, #T_d6afd_row13_col0, #T_d6afd_row13_col1, #T_d6afd_row13_col2, #T_d6afd_row13_col3, #T_d6afd_row13_col4, #T_d6afd_row13_col5, #T_d6afd_row13_col6, #T_d6afd_row14_col0, #T_d6afd_row14_col1, #T_d6afd_row14_col2, #T_d6afd_row14_col3, #T_d6afd_row14_col4, #T_d6afd_row14_col5, #T_d6afd_row14_col6, #T_d6afd_row15_col0, #T_d6afd_row15_col1, #T_d6afd_row15_col2, #T_d6afd_row15_col3, #T_d6afd_row15_col4, #T_d6afd_row15_col5, #T_d6afd_row15_col6, #T_d6afd_row16_col0, #T_d6afd_row16_col1, #T_d6afd_row16_col2, #T_d6afd_row16_col3, #T_d6afd_row16_col4, #T_d6afd_row16_col5, #T_d6afd_row16_col6, #T_d6afd_row17_col0, #T_d6afd_row17_col1, #T_d6afd_row17_col2, #T_d6afd_row17_col3, #T_d6afd_row17_col4, #T_d6afd_row17_col5, #T_d6afd_row17_col6, #T_d6afd_row18_col0, #T_d6afd_row18_col1, #T_d6afd_row18_col2, #T_d6afd_row18_col3, #T_d6afd_row18_col4, #T_d6afd_row18_col5, #T_d6afd_row18_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_d6afd_row0_col1, #T_d6afd_row0_col2, #T_d6afd_row0_col3, #T_d6afd_row0_col4, #T_d6afd_row0_col5, #T_d6afd_row0_col6 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_d6afd_row0_col7, #T_d6afd_row1_col7, #T_d6afd_row2_col7, #T_d6afd_row3_col7, #T_d6afd_row4_col7, #T_d6afd_row5_col7, #T_d6afd_row6_col7, #T_d6afd_row7_col7, #T_d6afd_row8_col7, #T_d6afd_row9_col7, #T_d6afd_row10_col7, #T_d6afd_row11_col7, #T_d6afd_row12_col7, #T_d6afd_row13_col7, #T_d6afd_row14_col7, #T_d6afd_row15_col7, #T_d6afd_row17_col7, #T_d6afd_row18_col7 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_d6afd_row16_col7 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d6afd\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d6afd_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_d6afd_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_d6afd_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_d6afd_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_d6afd_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_d6afd_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_d6afd_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_d6afd_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row0\" class=\"row_heading level0 row0\" >lightgbm</th>\n",
              "      <td id=\"T_d6afd_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_d6afd_row0_col1\" class=\"data row0 col1\" >0.3167</td>\n",
              "      <td id=\"T_d6afd_row0_col2\" class=\"data row0 col2\" >0.2248</td>\n",
              "      <td id=\"T_d6afd_row0_col3\" class=\"data row0 col3\" >0.4740</td>\n",
              "      <td id=\"T_d6afd_row0_col4\" class=\"data row0 col4\" >0.8309</td>\n",
              "      <td id=\"T_d6afd_row0_col5\" class=\"data row0 col5\" >0.1432</td>\n",
              "      <td id=\"T_d6afd_row0_col6\" class=\"data row0 col6\" >0.1785</td>\n",
              "      <td id=\"T_d6afd_row0_col7\" class=\"data row0 col7\" >0.8470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row1\" class=\"row_heading level0 row1\" >xgboost</th>\n",
              "      <td id=\"T_d6afd_row1_col0\" class=\"data row1 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_d6afd_row1_col1\" class=\"data row1 col1\" >0.3178</td>\n",
              "      <td id=\"T_d6afd_row1_col2\" class=\"data row1 col2\" >0.2332</td>\n",
              "      <td id=\"T_d6afd_row1_col3\" class=\"data row1 col3\" >0.4826</td>\n",
              "      <td id=\"T_d6afd_row1_col4\" class=\"data row1 col4\" >0.8244</td>\n",
              "      <td id=\"T_d6afd_row1_col5\" class=\"data row1 col5\" >0.1463</td>\n",
              "      <td id=\"T_d6afd_row1_col6\" class=\"data row1 col6\" >0.1790</td>\n",
              "      <td id=\"T_d6afd_row1_col7\" class=\"data row1 col7\" >0.4370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
              "      <td id=\"T_d6afd_row2_col0\" class=\"data row2 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_d6afd_row2_col1\" class=\"data row2 col1\" >0.3325</td>\n",
              "      <td id=\"T_d6afd_row2_col2\" class=\"data row2 col2\" >0.2578</td>\n",
              "      <td id=\"T_d6afd_row2_col3\" class=\"data row2 col3\" >0.5076</td>\n",
              "      <td id=\"T_d6afd_row2_col4\" class=\"data row2 col4\" >0.8063</td>\n",
              "      <td id=\"T_d6afd_row2_col5\" class=\"data row2 col5\" >0.1495</td>\n",
              "      <td id=\"T_d6afd_row2_col6\" class=\"data row2 col6\" >0.1847</td>\n",
              "      <td id=\"T_d6afd_row2_col7\" class=\"data row2 col7\" >3.9380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
              "      <td id=\"T_d6afd_row3_col0\" class=\"data row3 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_d6afd_row3_col1\" class=\"data row3 col1\" >0.3379</td>\n",
              "      <td id=\"T_d6afd_row3_col2\" class=\"data row3 col2\" >0.2668</td>\n",
              "      <td id=\"T_d6afd_row3_col3\" class=\"data row3 col3\" >0.5163</td>\n",
              "      <td id=\"T_d6afd_row3_col4\" class=\"data row3 col4\" >0.7995</td>\n",
              "      <td id=\"T_d6afd_row3_col5\" class=\"data row3 col5\" >0.1537</td>\n",
              "      <td id=\"T_d6afd_row3_col6\" class=\"data row3 col6\" >0.1910</td>\n",
              "      <td id=\"T_d6afd_row3_col7\" class=\"data row3 col7\" >11.2290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row4\" class=\"row_heading level0 row4\" >gbr</th>\n",
              "      <td id=\"T_d6afd_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_d6afd_row4_col1\" class=\"data row4 col1\" >0.3730</td>\n",
              "      <td id=\"T_d6afd_row4_col2\" class=\"data row4 col2\" >0.2891</td>\n",
              "      <td id=\"T_d6afd_row4_col3\" class=\"data row4 col3\" >0.5375</td>\n",
              "      <td id=\"T_d6afd_row4_col4\" class=\"data row4 col4\" >0.7828</td>\n",
              "      <td id=\"T_d6afd_row4_col5\" class=\"data row4 col5\" >0.1635</td>\n",
              "      <td id=\"T_d6afd_row4_col6\" class=\"data row4 col6\" >0.2123</td>\n",
              "      <td id=\"T_d6afd_row4_col7\" class=\"data row4 col7\" >3.0340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
              "      <td id=\"T_d6afd_row5_col0\" class=\"data row5 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_d6afd_row5_col1\" class=\"data row5 col1\" >0.4640</td>\n",
              "      <td id=\"T_d6afd_row5_col2\" class=\"data row5 col2\" >0.5259</td>\n",
              "      <td id=\"T_d6afd_row5_col3\" class=\"data row5 col3\" >0.7246</td>\n",
              "      <td id=\"T_d6afd_row5_col4\" class=\"data row5 col4\" >0.6045</td>\n",
              "      <td id=\"T_d6afd_row5_col5\" class=\"data row5 col5\" >0.2124</td>\n",
              "      <td id=\"T_d6afd_row5_col6\" class=\"data row5 col6\" >0.2512</td>\n",
              "      <td id=\"T_d6afd_row5_col7\" class=\"data row5 col7\" >0.2630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row6\" class=\"row_heading level0 row6\" >ridge</th>\n",
              "      <td id=\"T_d6afd_row6_col0\" class=\"data row6 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_d6afd_row6_col1\" class=\"data row6 col1\" >0.5337</td>\n",
              "      <td id=\"T_d6afd_row6_col2\" class=\"data row6 col2\" >0.5339</td>\n",
              "      <td id=\"T_d6afd_row6_col3\" class=\"data row6 col3\" >0.7304</td>\n",
              "      <td id=\"T_d6afd_row6_col4\" class=\"data row6 col4\" >0.5998</td>\n",
              "      <td id=\"T_d6afd_row6_col5\" class=\"data row6 col5\" >0.2246</td>\n",
              "      <td id=\"T_d6afd_row6_col6\" class=\"data row6 col6\" >0.3177</td>\n",
              "      <td id=\"T_d6afd_row6_col7\" class=\"data row6 col7\" >0.0250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row7\" class=\"row_heading level0 row7\" >br</th>\n",
              "      <td id=\"T_d6afd_row7_col0\" class=\"data row7 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_d6afd_row7_col1\" class=\"data row7 col1\" >0.5337</td>\n",
              "      <td id=\"T_d6afd_row7_col2\" class=\"data row7 col2\" >0.5339</td>\n",
              "      <td id=\"T_d6afd_row7_col3\" class=\"data row7 col3\" >0.7304</td>\n",
              "      <td id=\"T_d6afd_row7_col4\" class=\"data row7 col4\" >0.5999</td>\n",
              "      <td id=\"T_d6afd_row7_col5\" class=\"data row7 col5\" >0.2246</td>\n",
              "      <td id=\"T_d6afd_row7_col6\" class=\"data row7 col6\" >0.3178</td>\n",
              "      <td id=\"T_d6afd_row7_col7\" class=\"data row7 col7\" >0.0270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row8\" class=\"row_heading level0 row8\" >lr</th>\n",
              "      <td id=\"T_d6afd_row8_col0\" class=\"data row8 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_d6afd_row8_col1\" class=\"data row8 col1\" >0.5337</td>\n",
              "      <td id=\"T_d6afd_row8_col2\" class=\"data row8 col2\" >0.5339</td>\n",
              "      <td id=\"T_d6afd_row8_col3\" class=\"data row8 col3\" >0.7305</td>\n",
              "      <td id=\"T_d6afd_row8_col4\" class=\"data row8 col4\" >0.5998</td>\n",
              "      <td id=\"T_d6afd_row8_col5\" class=\"data row8 col5\" >0.2246</td>\n",
              "      <td id=\"T_d6afd_row8_col6\" class=\"data row8 col6\" >0.3177</td>\n",
              "      <td id=\"T_d6afd_row8_col7\" class=\"data row8 col7\" >0.7780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row9\" class=\"row_heading level0 row9\" >lar</th>\n",
              "      <td id=\"T_d6afd_row9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_d6afd_row9_col1\" class=\"data row9 col1\" >0.5419</td>\n",
              "      <td id=\"T_d6afd_row9_col2\" class=\"data row9 col2\" >0.6005</td>\n",
              "      <td id=\"T_d6afd_row9_col3\" class=\"data row9 col3\" >0.7738</td>\n",
              "      <td id=\"T_d6afd_row9_col4\" class=\"data row9 col4\" >0.5507</td>\n",
              "      <td id=\"T_d6afd_row9_col5\" class=\"data row9 col5\" >0.2305</td>\n",
              "      <td id=\"T_d6afd_row9_col6\" class=\"data row9 col6\" >0.3127</td>\n",
              "      <td id=\"T_d6afd_row9_col7\" class=\"data row9 col7\" >0.0260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row10\" class=\"row_heading level0 row10\" >en</th>\n",
              "      <td id=\"T_d6afd_row10_col0\" class=\"data row10 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_d6afd_row10_col1\" class=\"data row10 col1\" >0.6809</td>\n",
              "      <td id=\"T_d6afd_row10_col2\" class=\"data row10 col2\" >0.7712</td>\n",
              "      <td id=\"T_d6afd_row10_col3\" class=\"data row10 col3\" >0.8780</td>\n",
              "      <td id=\"T_d6afd_row10_col4\" class=\"data row10 col4\" >0.4218</td>\n",
              "      <td id=\"T_d6afd_row10_col5\" class=\"data row10 col5\" >0.2776</td>\n",
              "      <td id=\"T_d6afd_row10_col6\" class=\"data row10 col6\" >0.4521</td>\n",
              "      <td id=\"T_d6afd_row10_col7\" class=\"data row10 col7\" >0.0270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row11\" class=\"row_heading level0 row11\" >ada</th>\n",
              "      <td id=\"T_d6afd_row11_col0\" class=\"data row11 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_d6afd_row11_col1\" class=\"data row11 col1\" >0.7735</td>\n",
              "      <td id=\"T_d6afd_row11_col2\" class=\"data row11 col2\" >0.8013</td>\n",
              "      <td id=\"T_d6afd_row11_col3\" class=\"data row11 col3\" >0.8943</td>\n",
              "      <td id=\"T_d6afd_row11_col4\" class=\"data row11 col4\" >0.3981</td>\n",
              "      <td id=\"T_d6afd_row11_col5\" class=\"data row11 col5\" >0.3033</td>\n",
              "      <td id=\"T_d6afd_row11_col6\" class=\"data row11 col6\" >0.5719</td>\n",
              "      <td id=\"T_d6afd_row11_col7\" class=\"data row11 col7\" >0.9350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row12\" class=\"row_heading level0 row12\" >lasso</th>\n",
              "      <td id=\"T_d6afd_row12_col0\" class=\"data row12 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_d6afd_row12_col1\" class=\"data row12 col1\" >0.7690</td>\n",
              "      <td id=\"T_d6afd_row12_col2\" class=\"data row12 col2\" >0.9550</td>\n",
              "      <td id=\"T_d6afd_row12_col3\" class=\"data row12 col3\" >0.9770</td>\n",
              "      <td id=\"T_d6afd_row12_col4\" class=\"data row12 col4\" >0.2843</td>\n",
              "      <td id=\"T_d6afd_row12_col5\" class=\"data row12 col5\" >0.3078</td>\n",
              "      <td id=\"T_d6afd_row12_col6\" class=\"data row12 col6\" >0.5184</td>\n",
              "      <td id=\"T_d6afd_row12_col7\" class=\"data row12 col7\" >0.0260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row13\" class=\"row_heading level0 row13\" >llar</th>\n",
              "      <td id=\"T_d6afd_row13_col0\" class=\"data row13 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_d6afd_row13_col1\" class=\"data row13 col1\" >0.7690</td>\n",
              "      <td id=\"T_d6afd_row13_col2\" class=\"data row13 col2\" >0.9550</td>\n",
              "      <td id=\"T_d6afd_row13_col3\" class=\"data row13 col3\" >0.9770</td>\n",
              "      <td id=\"T_d6afd_row13_col4\" class=\"data row13 col4\" >0.2843</td>\n",
              "      <td id=\"T_d6afd_row13_col5\" class=\"data row13 col5\" >0.3078</td>\n",
              "      <td id=\"T_d6afd_row13_col6\" class=\"data row13 col6\" >0.5184</td>\n",
              "      <td id=\"T_d6afd_row13_col7\" class=\"data row13 col7\" >0.0260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row14\" class=\"row_heading level0 row14\" >knn</th>\n",
              "      <td id=\"T_d6afd_row14_col0\" class=\"data row14 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_d6afd_row14_col1\" class=\"data row14 col1\" >0.8314</td>\n",
              "      <td id=\"T_d6afd_row14_col2\" class=\"data row14 col2\" >1.1722</td>\n",
              "      <td id=\"T_d6afd_row14_col3\" class=\"data row14 col3\" >1.0825</td>\n",
              "      <td id=\"T_d6afd_row14_col4\" class=\"data row14 col4\" >0.1210</td>\n",
              "      <td id=\"T_d6afd_row14_col5\" class=\"data row14 col5\" >0.3379</td>\n",
              "      <td id=\"T_d6afd_row14_col6\" class=\"data row14 col6\" >0.5318</td>\n",
              "      <td id=\"T_d6afd_row14_col7\" class=\"data row14 col7\" >0.0490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row15\" class=\"row_heading level0 row15\" >omp</th>\n",
              "      <td id=\"T_d6afd_row15_col0\" class=\"data row15 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_d6afd_row15_col1\" class=\"data row15 col1\" >0.9128</td>\n",
              "      <td id=\"T_d6afd_row15_col2\" class=\"data row15 col2\" >1.3352</td>\n",
              "      <td id=\"T_d6afd_row15_col3\" class=\"data row15 col3\" >1.1552</td>\n",
              "      <td id=\"T_d6afd_row15_col4\" class=\"data row15 col4\" >-0.0005</td>\n",
              "      <td id=\"T_d6afd_row15_col5\" class=\"data row15 col5\" >0.3620</td>\n",
              "      <td id=\"T_d6afd_row15_col6\" class=\"data row15 col6\" >0.6200</td>\n",
              "      <td id=\"T_d6afd_row15_col7\" class=\"data row15 col7\" >0.0270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row16\" class=\"row_heading level0 row16\" >dummy</th>\n",
              "      <td id=\"T_d6afd_row16_col0\" class=\"data row16 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_d6afd_row16_col1\" class=\"data row16 col1\" >0.9128</td>\n",
              "      <td id=\"T_d6afd_row16_col2\" class=\"data row16 col2\" >1.3355</td>\n",
              "      <td id=\"T_d6afd_row16_col3\" class=\"data row16 col3\" >1.1553</td>\n",
              "      <td id=\"T_d6afd_row16_col4\" class=\"data row16 col4\" >-0.0007</td>\n",
              "      <td id=\"T_d6afd_row16_col5\" class=\"data row16 col5\" >0.3618</td>\n",
              "      <td id=\"T_d6afd_row16_col6\" class=\"data row16 col6\" >0.6193</td>\n",
              "      <td id=\"T_d6afd_row16_col7\" class=\"data row16 col7\" >0.0220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row17\" class=\"row_heading level0 row17\" >huber</th>\n",
              "      <td id=\"T_d6afd_row17_col0\" class=\"data row17 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_d6afd_row17_col1\" class=\"data row17 col1\" >0.5879</td>\n",
              "      <td id=\"T_d6afd_row17_col2\" class=\"data row17 col2\" >3.2726</td>\n",
              "      <td id=\"T_d6afd_row17_col3\" class=\"data row17 col3\" >1.2782</td>\n",
              "      <td id=\"T_d6afd_row17_col4\" class=\"data row17 col4\" >-1.5294</td>\n",
              "      <td id=\"T_d6afd_row17_col5\" class=\"data row17 col5\" >0.2451</td>\n",
              "      <td id=\"T_d6afd_row17_col6\" class=\"data row17 col6\" >0.3322</td>\n",
              "      <td id=\"T_d6afd_row17_col7\" class=\"data row17 col7\" >0.1890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d6afd_level0_row18\" class=\"row_heading level0 row18\" >par</th>\n",
              "      <td id=\"T_d6afd_row18_col0\" class=\"data row18 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_d6afd_row18_col1\" class=\"data row18 col1\" >1.6485</td>\n",
              "      <td id=\"T_d6afd_row18_col2\" class=\"data row18 col2\" >6.2284</td>\n",
              "      <td id=\"T_d6afd_row18_col3\" class=\"data row18 col3\" >2.2063</td>\n",
              "      <td id=\"T_d6afd_row18_col4\" class=\"data row18 col4\" >-3.8036</td>\n",
              "      <td id=\"T_d6afd_row18_col5\" class=\"data row18 col5\" >0.5180</td>\n",
              "      <td id=\"T_d6afd_row18_col6\" class=\"data row18 col6\" >1.1031</td>\n",
              "      <td id=\"T_d6afd_row18_col7\" class=\"data row18 col7\" >0.0530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/82 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c29526e92bc843d0abf9d761ea4f0e20"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Creating a Regression Model**</p>"
      ],
      "metadata": {
        "id": "YycCFN4lW8ir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = create_model('rf')  # Random Forest Regressor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "5b4430f8da1d4ea9979b69c1d747de2e",
            "fd298e1616b946ae8b6846b41ecddbdd",
            "c11e3d4431ca437c9e8c2aa43aa84d1b",
            "d334813a435b4df2b6e2bdafbe0461a5",
            "0084e1bc95224e8394d7b6ce3f436dca",
            "a45814b9475345c4939d9571752fb1c7",
            "d292529637834042ae9d6053856599de",
            "b615bd35beba45efa8525ebd249aeaa8",
            "1faae79f3620496eac1b120016dc7c85",
            "8b5dacea268b43f6928f15a99a361494",
            "b3f553c6c25e41d68d6718bd2042511a"
          ]
        },
        "id": "rOopU0GPXFpE",
        "outputId": "510b6fbd-94fc-4104-e95a-af593d0597a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7db34379f350>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_33753_row10_col0, #T_33753_row10_col1, #T_33753_row10_col2, #T_33753_row10_col3, #T_33753_row10_col4, #T_33753_row10_col5 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_33753\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_33753_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_33753_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_33753_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_33753_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_33753_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_33753_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_33753_row0_col0\" class=\"data row0 col0\" >0.3301</td>\n",
              "      <td id=\"T_33753_row0_col1\" class=\"data row0 col1\" >0.2537</td>\n",
              "      <td id=\"T_33753_row0_col2\" class=\"data row0 col2\" >0.5037</td>\n",
              "      <td id=\"T_33753_row0_col3\" class=\"data row0 col3\" >0.8241</td>\n",
              "      <td id=\"T_33753_row0_col4\" class=\"data row0 col4\" >0.1493</td>\n",
              "      <td id=\"T_33753_row0_col5\" class=\"data row0 col5\" >0.1901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_33753_row1_col0\" class=\"data row1 col0\" >0.3307</td>\n",
              "      <td id=\"T_33753_row1_col1\" class=\"data row1 col1\" >0.2515</td>\n",
              "      <td id=\"T_33753_row1_col2\" class=\"data row1 col2\" >0.5015</td>\n",
              "      <td id=\"T_33753_row1_col3\" class=\"data row1 col3\" >0.8131</td>\n",
              "      <td id=\"T_33753_row1_col4\" class=\"data row1 col4\" >0.1481</td>\n",
              "      <td id=\"T_33753_row1_col5\" class=\"data row1 col5\" >0.1794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_33753_row2_col0\" class=\"data row2 col0\" >0.3357</td>\n",
              "      <td id=\"T_33753_row2_col1\" class=\"data row2 col1\" >0.2635</td>\n",
              "      <td id=\"T_33753_row2_col2\" class=\"data row2 col2\" >0.5133</td>\n",
              "      <td id=\"T_33753_row2_col3\" class=\"data row2 col3\" >0.8023</td>\n",
              "      <td id=\"T_33753_row2_col4\" class=\"data row2 col4\" >0.1522</td>\n",
              "      <td id=\"T_33753_row2_col5\" class=\"data row2 col5\" >0.1876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_33753_row3_col0\" class=\"data row3 col0\" >0.3422</td>\n",
              "      <td id=\"T_33753_row3_col1\" class=\"data row3 col1\" >0.2937</td>\n",
              "      <td id=\"T_33753_row3_col2\" class=\"data row3 col2\" >0.5419</td>\n",
              "      <td id=\"T_33753_row3_col3\" class=\"data row3 col3\" >0.7923</td>\n",
              "      <td id=\"T_33753_row3_col4\" class=\"data row3 col4\" >0.1551</td>\n",
              "      <td id=\"T_33753_row3_col5\" class=\"data row3 col5\" >0.1861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_33753_row4_col0\" class=\"data row4 col0\" >0.3266</td>\n",
              "      <td id=\"T_33753_row4_col1\" class=\"data row4 col1\" >0.2447</td>\n",
              "      <td id=\"T_33753_row4_col2\" class=\"data row4 col2\" >0.4947</td>\n",
              "      <td id=\"T_33753_row4_col3\" class=\"data row4 col3\" >0.8237</td>\n",
              "      <td id=\"T_33753_row4_col4\" class=\"data row4 col4\" >0.1480</td>\n",
              "      <td id=\"T_33753_row4_col5\" class=\"data row4 col5\" >0.1833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_33753_row5_col0\" class=\"data row5 col0\" >0.3478</td>\n",
              "      <td id=\"T_33753_row5_col1\" class=\"data row5 col1\" >0.2750</td>\n",
              "      <td id=\"T_33753_row5_col2\" class=\"data row5 col2\" >0.5244</td>\n",
              "      <td id=\"T_33753_row5_col3\" class=\"data row5 col3\" >0.7728</td>\n",
              "      <td id=\"T_33753_row5_col4\" class=\"data row5 col4\" >0.1599</td>\n",
              "      <td id=\"T_33753_row5_col5\" class=\"data row5 col5\" >0.1993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_33753_row6_col0\" class=\"data row6 col0\" >0.3373</td>\n",
              "      <td id=\"T_33753_row6_col1\" class=\"data row6 col1\" >0.2649</td>\n",
              "      <td id=\"T_33753_row6_col2\" class=\"data row6 col2\" >0.5147</td>\n",
              "      <td id=\"T_33753_row6_col3\" class=\"data row6 col3\" >0.7986</td>\n",
              "      <td id=\"T_33753_row6_col4\" class=\"data row6 col4\" >0.1554</td>\n",
              "      <td id=\"T_33753_row6_col5\" class=\"data row6 col5\" >0.1936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_33753_row7_col0\" class=\"data row7 col0\" >0.3470</td>\n",
              "      <td id=\"T_33753_row7_col1\" class=\"data row7 col1\" >0.2814</td>\n",
              "      <td id=\"T_33753_row7_col2\" class=\"data row7 col2\" >0.5305</td>\n",
              "      <td id=\"T_33753_row7_col3\" class=\"data row7 col3\" >0.7874</td>\n",
              "      <td id=\"T_33753_row7_col4\" class=\"data row7 col4\" >0.1619</td>\n",
              "      <td id=\"T_33753_row7_col5\" class=\"data row7 col5\" >0.2048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_33753_row8_col0\" class=\"data row8 col0\" >0.3463</td>\n",
              "      <td id=\"T_33753_row8_col1\" class=\"data row8 col1\" >0.2787</td>\n",
              "      <td id=\"T_33753_row8_col2\" class=\"data row8 col2\" >0.5279</td>\n",
              "      <td id=\"T_33753_row8_col3\" class=\"data row8 col3\" >0.7840</td>\n",
              "      <td id=\"T_33753_row8_col4\" class=\"data row8 col4\" >0.1562</td>\n",
              "      <td id=\"T_33753_row8_col5\" class=\"data row8 col5\" >0.1988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_33753_row9_col0\" class=\"data row9 col0\" >0.3354</td>\n",
              "      <td id=\"T_33753_row9_col1\" class=\"data row9 col1\" >0.2608</td>\n",
              "      <td id=\"T_33753_row9_col2\" class=\"data row9 col2\" >0.5107</td>\n",
              "      <td id=\"T_33753_row9_col3\" class=\"data row9 col3\" >0.7968</td>\n",
              "      <td id=\"T_33753_row9_col4\" class=\"data row9 col4\" >0.1512</td>\n",
              "      <td id=\"T_33753_row9_col5\" class=\"data row9 col5\" >0.1870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_33753_row10_col0\" class=\"data row10 col0\" >0.3379</td>\n",
              "      <td id=\"T_33753_row10_col1\" class=\"data row10 col1\" >0.2668</td>\n",
              "      <td id=\"T_33753_row10_col2\" class=\"data row10 col2\" >0.5163</td>\n",
              "      <td id=\"T_33753_row10_col3\" class=\"data row10 col3\" >0.7995</td>\n",
              "      <td id=\"T_33753_row10_col4\" class=\"data row10 col4\" >0.1537</td>\n",
              "      <td id=\"T_33753_row10_col5\" class=\"data row10 col5\" >0.1910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_33753_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_33753_row11_col0\" class=\"data row11 col0\" >0.0072</td>\n",
              "      <td id=\"T_33753_row11_col1\" class=\"data row11 col1\" >0.0145</td>\n",
              "      <td id=\"T_33753_row11_col2\" class=\"data row11 col2\" >0.0140</td>\n",
              "      <td id=\"T_33753_row11_col3\" class=\"data row11 col3\" >0.0160</td>\n",
              "      <td id=\"T_33753_row11_col4\" class=\"data row11 col4\" >0.0046</td>\n",
              "      <td id=\"T_33753_row11_col5\" class=\"data row11 col5\" >0.0076</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b4430f8da1d4ea9979b69c1d747de2e"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Hyperparameter Tuning**</p>"
      ],
      "metadata": {
        "id": "RwCcUrY_XSgx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_model = tune_model(model)"
      ],
      "metadata": {
        "id": "hVbnVDuDXgLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Analyze Model**</p>"
      ],
      "metadata": {
        "id": "lqOd877cXzoH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plot_model function is used to analyze the performance of a trained model on the test set. It may require re-training the model in certain cases."
      ],
      "metadata": {
        "id": "UVvFbRCCYRjM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, plot = 'residuals')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "8fUqv1Q4YAd9",
        "outputId": "624310ed-69b4-4897-be13-1386b6c2fe46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot error\n",
        "plot_model(model, plot = 'error')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "GjelRB5FYWhT",
        "outputId": "4ed9e8b0-078b-4766-e0bc-1f568bc6f2b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot feature importance\n",
        "plot_model(model, plot = 'feature')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "EOStZOV-YinU",
        "outputId": "012237be-bdaa-49a9-daa7-1aa4b22b7066"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Model Evaluation**</p>"
      ],
      "metadata": {
        "id": "oEye_5SgXjJL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model)"
      ],
      "metadata": {
        "id": "EhP9KufeXx2S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "8cecba0f52e345dd9cc715446f547011",
            "296d985ade9243e29ddbf445af2f0455",
            "9c13c62849a44fdb9b550e84a090739f",
            "b12a8ad95640488783f58029be2dcef2",
            "5e3c341c2bd540cea2beaeb40055d25e",
            "8bd76591ec8f4ac4972c4bd829e20a48",
            "3a4445ea3338451f83317715c4317f0c"
          ]
        },
        "outputId": "ac63be0e-6fec-4aed-f31b-22bdf0ecc80f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8cecba0f52e345dd9cc715446f547011"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Predictions on New Data**</p>"
      ],
      "metadata": {
        "id": "VlWm2s-qX1yG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = predict_model(model, data=df.iloc[:10])\n",
        "predictions"
      ],
      "metadata": {
        "id": "BVoQ5kuNYB-N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "123cfb1b-5106-46d7-b5de-94a71cb87f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7db343b421d0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_8e8b0\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_8e8b0_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_8e8b0_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_8e8b0_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_8e8b0_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_8e8b0_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_8e8b0_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_8e8b0_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_8e8b0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_8e8b0_row0_col0\" class=\"data row0 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_8e8b0_row0_col1\" class=\"data row0 col1\" >0.2175</td>\n",
              "      <td id=\"T_8e8b0_row0_col2\" class=\"data row0 col2\" >0.0802</td>\n",
              "      <td id=\"T_8e8b0_row0_col3\" class=\"data row0 col3\" >0.2831</td>\n",
              "      <td id=\"T_8e8b0_row0_col4\" class=\"data row0 col4\" >0.8094</td>\n",
              "      <td id=\"T_8e8b0_row0_col5\" class=\"data row0 col5\" >0.0679</td>\n",
              "      <td id=\"T_8e8b0_row0_col6\" class=\"data row0 col6\" >0.0690</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup   Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556  37.880001   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842  37.860001   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260  37.849998   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945  37.849998   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467  37.849998   \n",
              "5  4.0368      52.0  4.761658   1.103627       413.0  2.139896  37.849998   \n",
              "6  3.6591      52.0  4.931907   0.951362      1094.0  2.128405  37.840000   \n",
              "7  3.1200      52.0  4.797527   1.061824      1157.0  1.788253  37.840000   \n",
              "8  2.0804      42.0  4.294117   1.117647      1206.0  2.026891  37.840000   \n",
              "9  3.6912      52.0  4.970588   0.990196      1551.0  2.172269  37.840000   \n",
              "\n",
              "    Longitude  MedHouseVal  prediction_label  \n",
              "0 -122.230003        4.526          4.461291  \n",
              "1 -122.220001        3.585          3.810450  \n",
              "2 -122.239998        3.521          4.034891  \n",
              "3 -122.250000        3.413          3.329280  \n",
              "4 -122.250000        3.422          2.901410  \n",
              "5 -122.250000        2.697          2.709890  \n",
              "6 -122.250000        2.992          2.628970  \n",
              "7 -122.250000        2.414          2.439960  \n",
              "8 -122.260002        2.267          2.033170  \n",
              "9 -122.250000        2.611          2.480030  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0b8ffa2e-f249-47c4-b1e1-c732c53caa3d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>MedHouseVal</th>\n",
              "      <th>prediction_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.880001</td>\n",
              "      <td>-122.230003</td>\n",
              "      <td>4.526</td>\n",
              "      <td>4.461291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.860001</td>\n",
              "      <td>-122.220001</td>\n",
              "      <td>3.585</td>\n",
              "      <td>3.810450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.849998</td>\n",
              "      <td>-122.239998</td>\n",
              "      <td>3.521</td>\n",
              "      <td>4.034891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.849998</td>\n",
              "      <td>-122.250000</td>\n",
              "      <td>3.413</td>\n",
              "      <td>3.329280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.849998</td>\n",
              "      <td>-122.250000</td>\n",
              "      <td>3.422</td>\n",
              "      <td>2.901410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.0368</td>\n",
              "      <td>52.0</td>\n",
              "      <td>4.761658</td>\n",
              "      <td>1.103627</td>\n",
              "      <td>413.0</td>\n",
              "      <td>2.139896</td>\n",
              "      <td>37.849998</td>\n",
              "      <td>-122.250000</td>\n",
              "      <td>2.697</td>\n",
              "      <td>2.709890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.6591</td>\n",
              "      <td>52.0</td>\n",
              "      <td>4.931907</td>\n",
              "      <td>0.951362</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>2.128405</td>\n",
              "      <td>37.840000</td>\n",
              "      <td>-122.250000</td>\n",
              "      <td>2.992</td>\n",
              "      <td>2.628970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3.1200</td>\n",
              "      <td>52.0</td>\n",
              "      <td>4.797527</td>\n",
              "      <td>1.061824</td>\n",
              "      <td>1157.0</td>\n",
              "      <td>1.788253</td>\n",
              "      <td>37.840000</td>\n",
              "      <td>-122.250000</td>\n",
              "      <td>2.414</td>\n",
              "      <td>2.439960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2.0804</td>\n",
              "      <td>42.0</td>\n",
              "      <td>4.294117</td>\n",
              "      <td>1.117647</td>\n",
              "      <td>1206.0</td>\n",
              "      <td>2.026891</td>\n",
              "      <td>37.840000</td>\n",
              "      <td>-122.260002</td>\n",
              "      <td>2.267</td>\n",
              "      <td>2.033170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3.6912</td>\n",
              "      <td>52.0</td>\n",
              "      <td>4.970588</td>\n",
              "      <td>0.990196</td>\n",
              "      <td>1551.0</td>\n",
              "      <td>2.172269</td>\n",
              "      <td>37.840000</td>\n",
              "      <td>-122.250000</td>\n",
              "      <td>2.611</td>\n",
              "      <td>2.480030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b8ffa2e-f249-47c4-b1e1-c732c53caa3d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0b8ffa2e-f249-47c4-b1e1-c732c53caa3d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0b8ffa2e-f249-47c4-b1e1-c732c53caa3d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-539158cc-15bb-4eb2-ae25-68fdf15a0791\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-539158cc-15bb-4eb2-ae25-68fdf15a0791')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-539158cc-15bb-4eb2-ae25-68fdf15a0791 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_baf263a9-6cec-4079-93c3-4bec05aa1a7e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('predictions')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_baf263a9-6cec-4079-93c3-4bec05aa1a7e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('predictions');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "predictions",
              "summary": "{\n  \"name\": \"predictions\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.080399990081787,\n          8.301400184631348,\n          4.036799907684326\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          21.0,\n          42.0,\n          41.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          4.294117450714111,\n          6.238137245178223,\n          4.761658191680908\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.1176470518112183,\n          0.9718804955482483,\n          1.1036269664764404\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1206.0,\n          2401.0,\n          413.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.026890754699707,\n          2.109841823577881,\n          2.1398963928222656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          37.86000061035156,\n          37.84000015258789,\n          37.880001068115234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -122.22000122070312,\n          -122.26000213623047,\n          -122.23999786376953\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MedHouseVal\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.2669999599456787,\n          3.5850000381469727,\n          2.697000026702881\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7925462505734853,\n        \"min\": 2.033169981837273,\n        \"max\": 4.461290693283081,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2.033169981837273,\n          3.8104504132270813,\n          2.7098900079727173\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7hvjz0o5YGG-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Deploying the Model**</p>"
      ],
      "metadata": {
        "id": "O9H2CDrQYHOy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "save_model(model, 'regression_model')"
      ],
      "metadata": {
        "id": "DtxRefNgYR3U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d0e95ad-ea32-453d-ae49-c87f5cb05332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=Memory(location=None),\n",
              "          steps=[('numerical_imputer',\n",
              "                  TransformerWrapper(include=['MedInc', 'HouseAge', 'AveRooms',\n",
              "                                              'AveBedrms', 'Population',\n",
              "                                              'AveOccup', 'Latitude',\n",
              "                                              'Longitude'],\n",
              "                                     transformer=SimpleImputer())),\n",
              "                 ('categorical_imputer',\n",
              "                  TransformerWrapper(include=[],\n",
              "                                     transformer=SimpleImputer(strategy='most_frequent'))),\n",
              "                 ('trained_model',\n",
              "                  RandomForestRegressor(n_jobs=-1, random_state=123))]),\n",
              " 'regression_model.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = load_model('regression_model')"
      ],
      "metadata": {
        "id": "_wVvajanYVFP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**3- Clustering**</p>"
      ],
      "metadata": {
        "id": "suGj_FjqRxQy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Installing PyCaret**"
      ],
      "metadata": {
        "id": "KJjCOsUiR_L7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pycaret"
      ],
      "metadata": {
        "id": "D78gL33GSDVi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Simple Clustering Example with PyCaret**"
      ],
      "metadata": {
        "id": "enp8Ck92SIT_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 1: Load Data**"
      ],
      "metadata": {
        "id": "ud5K6fdxSRwg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.clustering import *\n",
        "from sklearn.datasets import load_iris\n",
        "import pandas as pd\n",
        "# Load the dataset\n",
        "iris = load_iris()\n",
        "data = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
        "data.head()"
      ],
      "metadata": {
        "id": "EyNclCCPSNtH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 2: Set Up PyCaret Environment**"
      ],
      "metadata": {
        "id": "7iHF7jxgS1_u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize PyCaret setup\n",
        "clu_setup = setup(data, normalize=True, session_id=123)"
      ],
      "metadata": {
        "id": "pd9Rc1KoS6t9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 3: Train Clustering Model**"
      ],
      "metadata": {
        "id": "55fCusvjTCMS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train K-Means clustering model\n",
        "kmeans_model = create_model('kmeans', num_clusters=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "0a4aeedf8c194dfaab444bb3c8a8ed45",
            "d1c299bc977848c5b75cdac264dff908",
            "71278a650ef549f486bb6108bafff2db",
            "232ce3dd8cd04bfaa8afcd5bc4336d27",
            "f4e6067f11b14721986f42ff615667e9",
            "9f04cee40a3d410bbce674101f677882",
            "9463426efd98485fb03f14cfe8cffdcd",
            "081e4069d34f4ce692a58ba24efc5afa",
            "2542d5e997d04091b6080723e1a769c6",
            "74c6dfc3936849b3b2c5cf0c40c14f8e",
            "28741abca4bd406e9dceb790c3d908d5"
          ]
        },
        "id": "w8PxPn2kTGXR",
        "outputId": "c1fb227c-23c0-4a1e-bdf4-b9609686c901"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7ea3bf30a650>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "</style>\n",
              "<table id=\"T_ef987\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_ef987_level0_col0\" class=\"col_heading level0 col0\" >Silhouette</th>\n",
              "      <th id=\"T_ef987_level0_col1\" class=\"col_heading level0 col1\" >Calinski-Harabasz</th>\n",
              "      <th id=\"T_ef987_level0_col2\" class=\"col_heading level0 col2\" >Davies-Bouldin</th>\n",
              "      <th id=\"T_ef987_level0_col3\" class=\"col_heading level0 col3\" >Homogeneity</th>\n",
              "      <th id=\"T_ef987_level0_col4\" class=\"col_heading level0 col4\" >Rand Index</th>\n",
              "      <th id=\"T_ef987_level0_col5\" class=\"col_heading level0 col5\" >Completeness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_ef987_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_ef987_row0_col0\" class=\"data row0 col0\" >0.4599</td>\n",
              "      <td id=\"T_ef987_row0_col1\" class=\"data row0 col1\" >241.9044</td>\n",
              "      <td id=\"T_ef987_row0_col2\" class=\"data row0 col2\" >0.8336</td>\n",
              "      <td id=\"T_ef987_row0_col3\" class=\"data row0 col3\" >0</td>\n",
              "      <td id=\"T_ef987_row0_col4\" class=\"data row0 col4\" >0</td>\n",
              "      <td id=\"T_ef987_row0_col5\" class=\"data row0 col5\" >0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a4aeedf8c194dfaab444bb3c8a8ed45"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 4: Analyze Results**"
      ],
      "metadata": {
        "id": "TNgqPvBZTQ7s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot cluster assignments\n",
        "plot_model(kmeans_model, plot='cluster')"
      ],
      "metadata": {
        "id": "TcAFr3-PTU7E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To view the assigned cluster labels:"
      ],
      "metadata": {
        "id": "KwUxxlSjTgtH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assign clusters to dataset\n",
        "df_clusters = assign_model(kmeans_model)\n",
        "df_clusters.head()"
      ],
      "metadata": {
        "id": "bWxKckS0TkYw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot elbow\n",
        "plot_model(kmeans_model, plot = 'elbow')"
      ],
      "metadata": {
        "id": "DSMEQx0AUF_M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot silhouette\n",
        "plot_model(kmeans_model, plot = 'silhouette')"
      ],
      "metadata": {
        "id": "IKhhrl8rUXng"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(kmeans_model)"
      ],
      "metadata": {
        "id": "CUIPIdIQUq2_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 5: Save and Load Model**"
      ],
      "metadata": {
        "id": "vtKoJRt8Tq0n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "save_model(kmeans_model, 'kmeans_clustering')"
      ],
      "metadata": {
        "id": "G6DRuDFXTv76"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To load the saved model:"
      ],
      "metadata": {
        "id": "atufhc0-T0bR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = load_model('kmeans_clustering')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vrjentJ7T4ZC",
        "outputId": "0ab9285e-e8d3-492c-d35d-f7630ccfd00c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**4- Anomaly Detection**</p>"
      ],
      "metadata": {
        "id": "CVfeHLSXEKzQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 1: Install PyCaret**"
      ],
      "metadata": {
        "id": "okZYS-kLEd25"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pycaret[full]\n"
      ],
      "metadata": {
        "id": "Sd98W4wBEhI2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check installed version\n",
        "import pycaret\n",
        "pycaret.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_Hj-DoutLBW7",
        "outputId": "f1b535eb-664e-437f-ab9d-b97b761635a5"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'3.3.2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 2: Load and Prepare the  Dataset**"
      ],
      "metadata": {
        "id": "2m-qU5vKElBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading sample dataset from pycaret dataset module\n",
        "from pycaret.datasets import get_data\n",
        "data = get_data('anomaly')\n"
      ],
      "metadata": {
        "id": "WxWFso4TFQlX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 3: Set Up the PyCaret Environment**"
      ],
      "metadata": {
        "id": "U03JMfTcFnX0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import pycaret anomaly and init setup\n",
        "from pycaret.anomaly import *\n",
        "s = setup(data, session_id = 123)\n"
      ],
      "metadata": {
        "id": "oviCBpc0Fq2n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import AnomalyExperiment and init the class\n",
        "from pycaret.anomaly import AnomalyExperiment\n",
        "exp = AnomalyExperiment()"
      ],
      "metadata": {
        "id": "5pRJKxlCLXT5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check the type of exp\n",
        "type(exp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "id": "uO4jqNwuLby5",
        "outputId": "04654962-5fee-4dc1-9af5-911813e6c42f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pycaret.anomaly.oop.AnomalyExperiment"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pycaret.anomaly.oop.AnomalyExperiment</b><br/>def __init__()</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.11/dist-packages/pycaret/anomaly/oop.py</a>Class for all standard transformation steps.</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 16);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# init setup on exp\n",
        "exp.setup(data, session_id = 123)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "UZcGAbt3LgJC",
        "outputId": "6ad10ea3-906d-42c2-f340-8ba55eda1393"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7d3037527f90>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_764e9_row4_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_764e9\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_764e9_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_764e9_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_764e9_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_764e9_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_764e9_row1_col0\" class=\"data row1 col0\" >Original data shape</td>\n",
              "      <td id=\"T_764e9_row1_col1\" class=\"data row1 col1\" >(1000, 10)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_764e9_row2_col0\" class=\"data row2 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_764e9_row2_col1\" class=\"data row2 col1\" >(1000, 10)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_764e9_row3_col0\" class=\"data row3 col0\" >Numeric features</td>\n",
              "      <td id=\"T_764e9_row3_col1\" class=\"data row3 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_764e9_row4_col0\" class=\"data row4 col0\" >Preprocess</td>\n",
              "      <td id=\"T_764e9_row4_col1\" class=\"data row4 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_764e9_row5_col0\" class=\"data row5 col0\" >Imputation type</td>\n",
              "      <td id=\"T_764e9_row5_col1\" class=\"data row5 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_764e9_row6_col0\" class=\"data row6 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_764e9_row6_col1\" class=\"data row6 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_764e9_row7_col0\" class=\"data row7 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_764e9_row7_col1\" class=\"data row7 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_764e9_row8_col0\" class=\"data row8 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_764e9_row8_col1\" class=\"data row8 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_764e9_row9_col0\" class=\"data row9 col0\" >Use GPU</td>\n",
              "      <td id=\"T_764e9_row9_col1\" class=\"data row9 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_764e9_row10_col0\" class=\"data row10 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_764e9_row10_col1\" class=\"data row10 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_764e9_row11_col0\" class=\"data row11 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_764e9_row11_col1\" class=\"data row11 col1\" >anomaly-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_764e9_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_764e9_row12_col0\" class=\"data row12 col0\" >USI</td>\n",
              "      <td id=\"T_764e9_row12_col1\" class=\"data row12 col1\" >e464</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pycaret.anomaly.oop.AnomalyExperiment at 0x7d31bf793150>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 4: Run Anomaly Detection Models**"
      ],
      "metadata": {
        "id": "t-ElXbIgFyAb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train iforest model\n",
        "iforest = create_model('iforest')\n",
        "iforest\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69,
          "referenced_widgets": [
            "0554f901739e49e4978c853963c03fa1",
            "d7a02cfa6b8f4b65a8adcfd280de6481",
            "c3c8e069ffa642208110f6517ee4c730",
            "242f764f764c4ded9d4971b81a74517f",
            "196ce086dc3f4a159ae50c6327cde0a9",
            "32ededba96df4c7cadd8ceafbe249a6b",
            "0353a22efa0343e4b8ba0300f9c45b8b",
            "9dbad6929b5d4b5186e43d59bb8dd540",
            "3ba0bffce33946058dff732fb07cc244",
            "711f7bc3de024479ac1deecc2c582c4b",
            "050a1dc86ed74d6dbd476501c590e51a"
          ]
        },
        "id": "QDjB5l7IF1hk",
        "outputId": "05a1775a-9a0a-47d1-9810-6b23c94783d9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0554f901739e49e4978c853963c03fa1"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=-1,\n",
              "    random_state=123, verbose=0)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to check all the available models\n",
        "models()"
      ],
      "metadata": {
        "id": "5_rOb8s5Lr9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iforest_anomalies = assign_model(iforest)\n",
        "iforest_anomalies"
      ],
      "metadata": {
        "id": "hxqqzIB9L6_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analyze Model**"
      ],
      "metadata": {
        "id": "Ytg9RVojMGiI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tsne plot anomalies\n",
        "plot_model(iforest, plot = 'tsne')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "l0MG_26VMKjv",
        "outputId": "ea77d0f7-1e27-475d-daf5-a953c314b6b1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js\"></script>                <div id=\"7119918d-22dd-4412-89df-4f1be0dd80be\" class=\"plotly-graph-div\" style=\"height:800px; width:900px;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"7119918d-22dd-4412-89df-4f1be0dd80be\")) {                    Plotly.newPlot(                        \"7119918d-22dd-4412-89df-4f1be0dd80be\",                        [{\"customdata\":[[0.26399535],[0.54609233],[0.33671412],[0.09210783],[0.32526118],[0.21246485],[0.25856572],[0.86923677],[0.19707796],[0.29298452],[0.8217832],[0.79662293],[0.045577012],[0.61366034],[0.95074457],[0.33634633],[0.034440666],[0.74154425],[0.18443266],[0.66451496],[0.16282485],[0.72983843],[0.62837386],[0.05788366],[0.44553483],[0.80183697],[0.27709612],[0.22863401],[0.8348607],[0.6697665],[0.5325273],[0.17045908],[0.5102885],[0.977562],[0.24977599],[0.9755007],[0.6137017],[0.16742036],[0.5374725],[0.37989914],[0.6338973],[0.6339604],[0.5499747],[0.17817299],[0.85563815],[0.3217967],[0.8174905],[0.25487942],[0.3441405],[0.8061457],[0.27708167],[0.5443285],[0.07708393],[0.27308145],[0.39087734],[0.3496214],[0.21454975],[0.20607522],[0.86462],[0.9817088],[0.33482966],[0.6483871],[0.09364356],[0.8378027],[0.76034397],[0.2914042],[0.3413454],[0.7611125],[0.5418998],[0.6841766],[0.9835356],[0.52081245],[0.32822773],[0.09715794],[0.8760886],[0.8702145],[0.656087],[0.3067915],[0.48126733],[0.25955918],[0.32612365],[0.93056804],[0.6583894],[0.6899923],[0.31578112],[0.5669051],[0.63752663],[0.6875407],[0.3243006],[0.5927049],[0.74025315],[0.5940329],[0.59645295],[0.2776808],[0.25832966],[0.14246087],[0.43553346],[0.70249903],[0.58897537],[0.6533044],[0.3369685],[0.6312447],[0.7353008],[0.050390366],[0.06426492],[0.6653595],[0.91783524],[0.4465101],[0.6619382],[0.43935984],[0.6875724],[0.36346224],[0.6542622],[0.57855356],[0.065927945],[0.55014855],[0.13918282],[0.08000463],[0.27822837],[0.5601243],[0.53639865],[0.4265319],[0.78910977],[0.7429005],[0.90169257],[0.6503374],[0.11405731],[0.26081258],[0.43991232],[0.9644093],[0.18440141],[0.34514543],[0.17589459],[0.31251445],[0.6741319],[0.32825044],[0.5982311],[0.09259882],[0.49791652],[0.41977653],[0.3121767],[0.9378571],[0.049030963],[0.47610453],[0.44739753],[0.29580605],[0.737364],[0.6258963],[0.40881342],[0.43751115],[0.69135135],[0.9355553],[0.43784666],[0.07221772],[0.67636067],[0.468884],[0.5438206],[0.65552264],[0.69352895],[0.108878314],[0.28546375],[0.0],[0.7064955],[0.27353063],[0.29232028],[0.6739843],[0.12612183],[0.9467075],[0.9363389],[0.85375535],[0.7060714],[0.6936822],[0.5085279],[0.34717038],[0.70415425],[0.6587301],[0.15584156],[0.31108543],[0.28113535],[0.0945878],[0.683261],[0.90772927],[0.29151267],[0.7527345],[0.93482965],[0.07141396],[0.5948963],[0.40467203],[0.1383762],[0.7473385],[0.5308825],[0.2826496],[0.69933575],[0.05611125],[0.855235],[0.07789513],[0.8916496],[0.3867423],[0.27113158],[0.95888895],[0.08512087],[0.52690226],[0.066577785],[0.8415067],[0.2553421],[0.060545772],[0.07338587],[0.18749373],[0.8583657],[0.09064035],[0.7097252],[0.6313006],[0.24750447],[0.8228436],[0.37347677],[0.6255386],[0.2929517],[0.29973033],[0.25121406],[0.34448054],[0.29306477],[0.7628032],[0.7384708],[0.67488325],[0.7191516],[0.13098022],[0.51132804],[0.04492903],[0.56091446],[0.74404067],[0.26696163],[0.64423186],[0.038113352],[0.0571553],[0.34139246],[0.72046363],[0.40287822],[0.63514894],[0.32072946],[0.091209695],[0.23414609],[0.8902211],[0.6021709],[0.63365203],[0.80624086],[0.7397312],[0.46523127],[0.7321109],[0.3738743],[0.5015879],[0.115383156],[0.7319466],[0.41829872],[0.062259592],[0.3914964],[0.3598493],[0.24523562],[0.6100283],[0.43540362],[0.43430966],[0.52376336],[0.17118786],[0.1877343],[0.94501466],[0.027851097],[0.68445706],[0.17801453],[0.6434279],[0.59588355],[0.3723617],[0.58610356],[0.27165884],[0.5995111],[0.46836308],[0.31451556],[0.21893726],[0.25471658],[0.25612816],[0.60225976],[0.13438107],[0.5138874],[0.62424165],[0.16025186],[0.5807081],[0.27054048],[0.83291996],[0.93032134],[0.7191579],[0.88672006],[0.09879228],[0.7871743],[0.3655924],[0.7195762],[0.8058956],[0.75557834],[0.5260059],[0.68880427],[0.2211153],[0.73936945],[0.40844023],[0.34892654],[0.43371376],[0.5964966],[0.6942953],[0.709031],[0.867653],[0.89332545],[0.7460209],[0.5382844],[0.8922193],[0.9351758],[0.2605277],[0.95678043],[0.20493248],[0.68773335],[0.32906362],[0.6321498],[0.4637445],[0.69885963],[0.38544658],[0.47997537],[0.7322791],[0.3813543],[0.459326],[0.25740144],[0.6650148],[0.103399545],[0.42528442],[0.94352907],[0.5947502],[0.5437768],[0.18203989],[0.23584208],[0.58598447],[0.7620132],[0.6849829],[0.6769369],[0.29018497],[0.48106822],[0.49276602],[0.22185624],[0.053875998],[0.08275115],[0.38536623],[0.70251393],[0.9155316],[0.082087435],[0.25128037],[0.41672182],[0.27703267],[0.5795398],[0.5693635],[0.13339971],[0.26504546],[0.9365719],[0.25261855],[0.054033082],[0.8024814],[0.5281977],[0.9515026],[0.57212114],[0.7529382],[0.14385754],[0.5134846],[0.3133224],[0.68779737],[0.37705985],[0.34822613],[0.6776328],[0.24138752],[0.8506502],[0.2203481],[0.47644547],[0.690324],[0.36469594],[0.6492492],[0.9437019],[0.529646],[0.299097],[0.96696246],[0.27200523],[0.31416145],[0.752021],[0.22188245],[0.7203327],[0.37920478],[0.60168654],[0.58293265],[0.95080984],[0.5883014],[0.8808872],[0.4678577],[0.8799022],[0.619844],[0.35900256],[0.29151127],[0.6941571],[0.72796035],[0.6177039],[0.39581257],[0.5831728],[0.7103324],[0.2537537],[0.04941223],[0.7283787],[0.4324329],[0.58612347],[0.052342374],[0.053483903],[0.5187294],[0.9330402],[0.7035543],[0.2689899],[0.73300695],[0.44435248],[0.58339465],[0.87117267],[0.42876709],[0.30046085],[0.95332944],[0.60644394],[0.9098681],[0.71489644],[0.4973495],[0.8530975],[0.72180414],[0.30346733],[0.6644851],[0.27978063],[0.5694817],[0.6286289],[0.088368125],[0.7335673],[0.5374368],[0.6385937],[0.95472693],[0.6322023],[0.64213467],[0.7446531],[0.115714006],[0.48625007],[0.44035614],[0.08272289],[0.511591],[0.39462754],[0.224394],[0.62692755],[0.29025406],[0.77900803],[0.8661058],[0.87332606],[0.49548915],[0.5942977],[0.91820157],[0.57704276],[0.01946767],[0.6702983],[0.54659534],[0.59166515],[0.4215582],[0.68560857],[0.34030303],[0.67562675],[0.31906736],[0.38849968],[0.7069496],[0.5594095],[0.6876635],[0.70880455],[0.6818881],[0.42253512],[0.3200046],[0.6582814],[0.63415486],[0.777332],[0.8126772],[0.0742808],[0.39600217],[0.054933913],[0.82505894],[0.382642],[0.8687298],[0.58804333],[0.89155155],[0.10920046],[0.6476828],[0.7243425],[0.07016304],[0.28088206],[0.03911326],[0.5796416],[0.6390009],[0.2817459],[0.6478577],[0.6479058],[0.87522626],[0.5676033],[0.2629406],[0.26802605],[0.40519133],[0.47996545],[0.33552402],[0.77716535],[0.42548797],[0.23798612],[0.14252298],[0.72417647],[0.23469894],[0.07818228],[0.8559902],[0.27025175],[0.43551785],[0.7626975],[0.46448725],[0.39611268],[0.37638092],[0.29459468],[0.6944921],[0.6227679],[0.84755975],[0.30146307],[0.9129378],[0.50001717],[0.90258795],[0.30451795],[0.29916567],[0.09201409],[0.4885489],[0.66466445],[0.26534954],[0.723657],[0.14926238],[0.04577339],[0.6966106],[0.42181486],[0.7338458],[0.41879538],[0.6774435],[0.2612836],[0.26852778],[0.54432225],[0.43163204],[0.9810181],[0.21498072],[0.39473],[0.12222494],[0.29749689],[0.37490442],[0.6869877],[0.3885492],[0.039296072],[0.73553425],[0.3792949],[0.7321234],[0.33922145],[0.6594506],[0.054838642],[0.6401121],[0.3071417],[0.3966119],[0.42043915],[0.24171497],[0.06589315],[0.9105219],[0.6971631],[0.072559856],[0.81825095],[0.3118888],[0.9616446],[0.81632555],[0.14647302],[0.5487539],[0.9461581],[0.29399273],[0.33147964],[0.90622],[0.5255404],[0.83301413],[0.5148498],[0.57460904],[0.46871185],[0.71002775],[0.64597327],[0.22979441],[0.5517834],[0.05582299],[0.33175093],[0.7155202],[0.58392435],[0.3069577],[0.7914782],[0.16423663],[0.06571122],[0.94899315],[0.08042383],[0.052495427],[0.5663461],[0.3630266],[0.60544217],[0.6308506],[0.065791234],[0.28681707],[0.68546283],[0.2608537],[0.6591224],[0.44317392],[0.602064],[0.06036691],[0.78190315],[0.51457983],[0.4838862],[0.6002138],[0.49736404],[0.41114524],[0.11699917],[0.34189013],[0.74695086],[0.39629275],[0.62214386],[0.33527115],[0.14025714],[0.08162068],[0.06983757],[0.46894002],[0.9908574],[0.8966168],[0.25531057],[0.22464527],[0.01991825],[0.29648042],[0.155459],[0.57791674],[0.95630306],[0.8134644],[0.5296758],[0.28767136],[0.48039135],[0.2810448],[0.63319653],[0.09332538],[0.284474],[0.052223776],[0.051129386],[0.9674056],[0.040045727],[0.9085829],[0.6621174],[0.4247032],[0.7220781],[0.6661933],[0.5096812],[0.7513536],[0.27907312],[0.074927],[0.91635954],[0.31040326],[0.71660405],[0.4855871],[0.3060752],[0.4775582],[0.71192396],[0.066348635],[0.27453935],[0.31082252],[0.47607312],[0.18679556],[0.26654208],[0.07023114],[0.26951927],[0.8000047],[0.27363366],[0.14647919],[0.37831897],[0.37384906],[0.24217084],[0.2497965],[0.06659119],[0.3729568],[0.5018123],[0.31309775],[0.8802022],[0.5198189],[0.36413875],[0.4926829],[0.28573266],[0.89011496],[0.31641957],[0.26866353],[0.77197593],[0.968889],[0.08033438],[0.7368282],[0.30055806],[0.9179504],[0.46581504],[0.05403668],[0.96315014],[0.0896535],[0.7076932],[0.636416],[0.5395211],[0.11475716],[0.3573124],[0.67639834],[0.4348284],[0.032326385],[0.7113656],[0.18748651],[0.052789655],[0.5058636],[0.9274319],[0.68098426],[0.8828222],[0.49570546],[0.5962404],[0.3206107],[0.3413685],[0.1596818],[0.31813368],[0.83930105],[0.06435925],[0.47021663],[0.77625227],[0.6358434],[0.62510544],[0.52959067],[0.98690015],[0.078585684],[0.23328426],[0.36333096],[0.716717],[0.37588978],[0.2233169],[0.29934397],[0.5385958],[0.73029715],[0.6603308],[0.435296],[0.3264943],[0.8544027],[0.57349384],[0.75778615],[0.7047432],[0.2815756],[0.6719682],[0.313726],[0.36433828],[0.3682164],[0.3862952],[0.6559719],[0.56602526],[0.4249991],[0.09011037],[0.77235055],[0.33104727],[0.27371618],[0.23877569],[0.37307978],[0.33118588],[0.25613454],[0.6783732],[0.43463835],[0.65466833],[0.98668104],[0.3715205],[0.40835962],[0.40932366],[0.28341207],[0.39524654],[0.010984428],[0.30601388],[0.26619482],[0.29931852],[0.31829396],[0.7428259],[0.09766433],[0.07966694],[0.6132588],[0.9944307],[0.5688351],[0.7080345],[0.3705927],[0.66986734],[0.6512956],[0.46253157],[0.7196256],[0.4537725],[0.39486393],[0.32397482],[0.14645472],[0.77906704],[0.4018289],[0.6014796],[0.856423],[0.56524295],[0.3012251],[0.28210816],[0.594378],[0.7053124],[0.54086965],[0.31924957],[0.36176613],[0.6250007],[0.3162874],[0.04723902],[0.8011822],[0.5084744],[0.8178934],[0.3992356],[0.974052],[0.7224153],[0.039564762],[0.90868413],[0.47301012],[0.71804965],[0.76347905],[0.64759654],[0.89499736],[0.2228259],[0.35901225],[0.9449102],[0.29787782],[0.172518],[0.55658627],[0.8764817],[0.48667786],[0.45949674],[0.47412637],[0.7010114],[0.3375974],[0.5375789],[0.96699244],[0.056849547],[0.23324907],[0.3800547],[0.6029294],[0.303848],[0.17019455],[0.97216856],[0.33216894],[0.6887287],[0.1107734],[0.27335206],[0.5729397],[0.49145707],[0.28139],[0.82169724],[0.67497766],[0.58513457],[0.71059614],[0.04048879],[0.5680543],[0.32175294],[0.61185026],[0.9260969],[0.6183474],[0.6676298],[0.39773348],[0.2908755],[0.5943751],[0.50825524],[0.33857137],[0.43230236],[0.30222198],[0.8761982],[0.080204494],[0.09233353],[0.09365645],[0.6084524],[0.7326546],[0.94415885],[0.2674231],[0.7311242],[0.6613085],[0.38776627],[0.9625853],[0.32206705],[0.63408107],[0.47534502],[0.353559],[0.8859363],[0.8767981],[0.22158106],[0.39436245],[0.80503565],[0.45289847],[0.59252053],[0.7610264],[0.24504903],[0.37213936],[0.68105483],[0.74714315],[0.92073953],[0.09720795],[0.699303],[0.721647],[0.5353561],[0.7382938],[0.70646757],[0.6739506],[0.7112123],[0.30255872],[0.080097854],[0.70701087],[0.51224864],[0.6596275],[0.6575597],[0.10064561],[0.9266536],[0.89287937],[0.8391575],[0.89095974],[0.55675447],[0.96036196],[0.6310379],[0.029838065],[0.27884334],[0.9261577],[0.55150425],[0.48594236],[0.4372347],[0.25231802],[0.515467],[0.43725228],[0.4937242],[0.83324724],[0.31752148],[0.28623456],[0.72683394],[0.49566704],[0.028645255],[0.31967193],[0.6743446],[0.76603717],[0.6545699],[0.7565144],[0.52825373],[0.3897195],[0.35508746],[0.870152],[0.7390833],[0.30576184],[0.4115877],[0.06363309],[0.7354094],[0.6059167],[0.5393956],[0.5661835],[0.6747952],[0.070993364],[0.1252641],[0.27960148],[0.9521744],[0.23380676],[0.43724743],[0.9254231],[0.43349862],[0.525179],[0.08843553],[0.44767764],[0.6470006],[0.9036033],[0.26579073],[0.66419697],[0.49812067],[0.49018896],[0.7470842],[0.90678364],[0.6787709],[0.34859985],[0.29483968],[0.7056918],[0.21191794],[0.06320112],[0.27669954],[0.6724594],[0.80820596],[0.29653838],[0.16012686],[0.43938503],[0.5559514],[0.38624012],[0.15868962],[0.21449986],[0.29957944],[0.56916165],[0.4886148],[0.878295],[0.6493809],[0.46571335],[0.16813774],[0.87529546],[0.84113234],[0.33292082],[0.5130479],[0.30505508],[0.812627],[0.25096706],[0.50243556],[0.45799074]],\"hovertemplate\":\"0=%{x}\\u003cbr\\u003e1=%{y}\\u003cbr\\u003e2=%{z}\\u003cbr\\u003eFeature=%{customdata[0]}\\u003cbr\\u003eAnomaly=%{marker.color}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":[0,0,1,1,0,0,0,1,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,1,1,0,0,1,1,0,0,1,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,1,1,0,0,0,0,1,0,0,1,1,1,0,1,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\"coloraxis\":\"coloraxis\",\"opacity\":0.7,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"\",\"scene\":\"scene\",\"showlegend\":false,\"x\":[-1.737983,-0.56389624,5.2782435,12.273477,11.132393,7.6850505,-4.7405176,18.44633,1.716437,7.1279945,18.067942,12.643631,12.798827,5.1311812,7.9348474,16.233128,12.814795,-5.76657,3.6876743,11.27069,6.8628993,-4.366797,6.429719,2.1327446,9.965768,-2.6159065,11.342843,-2.6198008,11.934642,15.285625,-0.1705194,1.9816501,1.9624304,8.0972,-4.6020064,-3.640126,2.7485197,-2.9966447,-3.2596672,8.422665,-12.977925,11.73673,-11.605028,7.649919,10.453462,16.554693,10.054888,1.8061491,12.2981615,-8.219272,10.484217,1.8491875,-6.3578973,16.393347,9.287821,8.880681,8.262048,-4.2311363,8.710642,6.122189,-2.2754169,-5.307068,17.196293,12.040012,-8.725432,9.988405,-7.9282327,-6.1007953,11.7309475,11.251498,19.810322,15.697275,-5.0959077,14.412947,-13.800056,-7.4711432,11.961561,15.384733,-15.485368,-11.183323,-10.432369,-8.763977,10.591903,-4.4121766,-2.7964108,1.7456939,-6.3938856,-6.5304523,-2.7351449,-6.5475006,-5.8789277,16.246008,-11.583079,-6.2166452,-1.1680328,17.878881,-8.375207,1.082125,-4.779395,11.876268,-3.7573621,-9.177572,0.7358402,-7.956451,18.579906,-9.891747,-6.4185266,0.10720745,-13.5069065,14.956939,-8.152233,-3.878424,15.326974,-7.7462306,9.890404,-10.355189,-4.6277666,-6.4921885,12.796358,-6.257303,-10.932226,-2.5128756,1.7312765,-10.352694,-1.9609972,-0.6757928,-0.18432535,13.471558,-2.4442885,-1.2289584,-1.8886503,-8.252399,18.064522,-1.3207952,-9.085401,-5.2728815,9.124436,16.46576,0.03442762,-1.8830297,15.381509,-7.291052,-7.542291,11.147267,-7.2933717,-10.650522,15.263568,-8.931958,-0.085980274,-4.317918,-7.8107843,-10.103816,0.8127385,-8.591312,-4.1580896,-8.717055,-8.767648,-11.349047,-15.332379,11.812551,-3.596913,-4.616986,-11.875228,-15.210232,12.057695,-9.991959,15.381409,17.315145,8.653462,-3.640708,-7.2844996,1.4168338,10.422014,8.146755,0.8513674,-7.5370965,9.758016,7.32032,0.52978414,15.566721,-0.88363135,11.417604,2.4376178,17.245249,17.135984,-5.491249,12.294798,-2.8156066,-9.238291,-9.613616,-9.347134,11.393273,17.103312,8.49192,-6.103002,-3.3199637,-1.2276926,1.4750192,-3.245653,-1.2321416,-0.6590332,-3.0319498,0.2243392,11.422113,7.059029,2.0937073,-11.818739,-0.89274055,11.384082,-0.31724414,0.52165323,13.505302,14.974555,-9.509809,-2.3101408,-2.3611007,-0.4254035,15.925925,17.751085,-0.33259627,7.048556,-1.8835946,11.336143,-14.249871,-6.6920676,-11.905579,-10.475941,-8.408305,-9.778904,12.982452,-3.7536967,-11.7245035,15.450798,16.144186,-6.7933607,-1.8949596,-0.5467367,-9.201788,13.640678,6.5114994,-1.1416451,1.4670594,7.4887886,-4.9022546,16.325706,-7.17782,-8.410269,-14.292722,-10.420564,7.5345955,-6.3548,-0.8769113,15.15527,-4.691358,-10.244861,2.1978374,-2.5144987,-7.494725,12.808618,-7.111974,6.590737,-4.611802,9.9662895,-0.9941179,-6.7924995,-4.5883713,17.806238,-4.768434,-13.278307,2.230844,-0.30542353,0.89094543,-5.919127,6.521619,11.291383,-8.206331,16.077621,11.502537,-0.8151866,-9.185268,-10.893076,6.564474,-9.062153,-5.421171,9.822195,-2.8006735,-7.671646,-1.6556027,-6.398037,-2.4341714,-7.600784,-0.9644402,-6.358332,-3.5068254,-4.3043766,15.443972,14.221272,-14.034521,-3.8615794,-2.9233155,2.371413,-7.1096163,17.00142,16.56603,12.883171,-12.644008,0.108319975,-11.646011,-12.072031,16.990618,16.446188,-9.85581,11.440359,6.6895294,-2.7989397,-2.2895117,-3.5855823,-3.768659,-13.569718,-0.22539501,1.6177839,-8.844174,15.832265,1.1848009,-12.944434,-4.7607713,17.176992,18.36753,9.340351,13.034309,-4.916671,15.594735,-6.857747,-9.157631,-6.4483924,11.108889,-0.80639684,-5.1570134,12.582127,14.324039,-4.7613897,-8.909875,12.818275,-8.804897,18.008898,9.897159,-0.94709104,-11.20892,13.383292,16.125345,-0.53424305,-6.169317,-7.1357074,0.47708297,-7.734319,-8.229157,-5.0525794,-1.5798615,18.89018,-4.2653728,-0.28804067,-0.09260666,-2.2961953,-10.721289,15.049126,-11.969953,16.899746,-6.748084,-7.743881,0.027574586,-10.691226,-1.3742286,12.041547,10.021315,15.727267,-9.560464,-12.704967,13.479129,-0.27960527,-6.511355,13.644494,-2.9772828,-11.845942,-5.968977,-0.8966519,-7.6673884,-6.3663006,-5.2549596,-0.6324376,-4.932266,-7.6427884,-8.841663,-6.700981,-9.88723,-6.4345884,0.7092141,-9.597337,16.282637,-3.8511288,-8.710844,-5.7359176,-5.6752996,-9.503553,2.2697942,18.802416,-4.481226,6.9300046,-3.7407851,11.608345,2.9476779,-3.6679149,-7.5054893,19.019852,-12.533771,-1.2859944,-11.562114,-6.43025,-2.2946746,18.183477,10.295763,-4.910864,-2.740629,-2.0370994,0.9868154,9.526124,-8.143104,-4.1551948,13.112269,-4.001335,12.077142,-9.900873,-4.864151,-4.828514,14.059818,19.584541,-1.5162129,-6.9622126,11.501493,-13.768153,7.0843678,-9.884761,-14.800977,8.371448,14.454339,-3.6305087,10.377366,17.908716,-8.921796,7.7770624,11.646867,-6.5699735,9.723216,-10.405377,-12.709844,15.600087,-0.4772378,1.8434172,8.538566,-15.229444,-14.837606,-1.1700399,9.479573,-11.717875,-7.694515,0.96835107,-6.0751934,-13.384091,19.126997,-9.724072,8.821714,-11.299732,-8.593092,-6.8369136,-7.727414,-9.263855,-6.539549,-2.8532765,11.948993,-3.6996782,-3.5256925,-11.328403,-5.2590685,13.511899,13.213827,-4.752295,13.350668,1.267004,-4.3331795,-4.2995286,7.3534203,-12.7572155,15.470638,10.424927,17.291296,7.8341923,15.08931,2.6418607,12.917739,-7.5827165,-6.1982164,-0.39226243,-1.8919225,-5.1604676,-2.3366334,-3.9022224,7.33705,-15.291764,-10.570976,-4.8468323,10.89316,11.45935,-11.423181,-0.22980508,14.07518,-14.852243,-10.695792,-7.6092086,-15.54683,-0.3716025,0.61190873,-4.8464317,11.247193,11.455343,9.619219,17.937561,-4.642528,-5.8399534,-7.1947074,0.798313,-1.6659462,0.8214037,15.780318,13.276014,-11.004777,13.874756,15.646647,2.7495542,12.346712,-3.4380393,2.0868368,-0.30571085,12.793432,-3.599569,-0.16663957,-10.817951,16.325687,-10.750177,14.059378,14.813178,-9.772149,-9.960376,-6.352264,10.819269,-15.366397,-8.077212,-3.7813268,12.305421,-13.157353,18.045362,2.4075887,14.4513855,18.189405,-8.416591,15.27569,-7.2926497,-12.169428,-9.056482,-1.7889794,16.97008,14.605125,-3.066732,14.528128,17.83735,16.388735,1.3707427,18.16899,-2.8964736,-6.5591965,-4.7324123,-11.651985,-4.2805676,-12.213265,9.728926,-2.5908911,-12.110699,-11.620209,-1.6700453,-8.058204,-6.861201,16.679531,-5.047306,-10.80989,1.698508,18.229856,-0.008361917,-7.1830807,16.548723,15.318984,12.019266,13.698216,14.602774,-1.4152282,10.268117,-4.8731956,12.216099,-0.11189142,18.666315,16.278082,-6.4747863,2.2256482,11.456816,-7.016488,-5.7872076,-4.6407166,-7.266025,-9.033852,-0.81403714,-2.3342648,-3.1021006,-1.5736076,-1.1231625,-6.777772,-6.821862,-0.519443,9.434815,19.56343,-4.2649436,10.724124,-3.5486412,-7.6502213,-4.5724063,1.5637951,-15.197988,10.89742,-6.228627,-3.5348232,-4.2719016,-1.0915495,15.425895,-0.7480604,6.8250184,-0.23279428,-3.375528,-7.200509,14.652854,-12.94756,7.6038847,-6.8663516,12.676554,-6.696432,-10.442602,-8.509803,-2.5221174,16.04747,-7.513599,1.4500455,11.407561,-5.2160745,-4.883698,15.285795,18.123598,-5.8876934,0.86756593,-2.7205606,13.42322,-0.4466874,15.360537,-1.0189053,1.3516836,-8.211473,-1.2102963,-8.694868,-7.7351294,-1.6716529,11.041138,-10.107868,0.5467363,14.500642,14.412165,-1.4516724,8.592023,-11.343245,-5.637538,10.314613,-8.93851,-8.207463,-11.615764,-12.036476,16.71747,17.449734,13.878802,15.376624,18.682112,1.298865,16.863321,12.229144,-5.1424394,-11.685663,-15.497101,11.467415,-0.8420636,-9.745038,5.144565,11.923824,-8.661382,8.138502,17.272305,14.091607,-1.5114613,8.430747,8.619561,-11.088855,-4.3537455,-2.481362,-5.524066,10.452941,7.445976,16.26633,-5.5053053,-9.313917,3.8055046,-4.604673,2.254483,1.7508515,2.9283512,2.021413,11.371925,11.892653,15.00982,8.301706,0.23605399,12.715683,-1.5542787,-4.29236,1.7085632,15.887761,-9.649158,-12.714649,-14.263309,11.801221,-3.4484541,-6.30785,-5.8404384,11.4321,-3.8465507,-14.06223,-11.892571,15.039949,8.26268,18.649076,-0.39314088,11.2887335,-1.6619645,-6.093583,-11.159125,7.6366987,-11.097828,-4.592973,14.230589,-10.740817,17.367788,-7.702793,-8.955269,-6.6826344,-2.5582802,-6.0948825,-4.532633,11.923319,-3.6137805,12.068974,-10.825145,-4.515631,-0.012432742,-3.651267,-4.7593546,-10.898457,-3.5082533,-0.774369,-15.166326,-3.5058293,-10.46004,-5.0396647,1.4435925,-7.9387517,-14.34387,18.376867,-6.0439076,11.621176,-13.5188675,-11.42451,19.367292,-9.159548,12.750337,-13.086285,-5.8425326,2.6869848,-10.414923,7.790819,-11.566169,-8.93195,-3.8808455,-3.4100428,0.21412827,-0.15124695,7.1232915,-9.157558,-10.445331,-3.4726431,15.95215,-10.208573,10.917757,10.664206,-13.509747,-4.093331,1.6525246,7.8138,0.5111723,18.918695,9.208989,14.664971,15.364177,6.677793,-14.635718,2.294599,-0.8450296,-7.889225,15.913856,-12.270022,-6.2809763,16.05196,-3.7212799,-5.1955004,16.77772,10.420501,8.382372,11.543485,13.536919,10.988484,14.209766,-9.1405735,-11.033071,-7.665732,14.728103,-1.2230103,-4.800576,12.894243,2.4699504,-11.545977,-8.431539,-2.213868,12.329539,-6.038516,-8.736073,-10.777356,0.0831594,1.1025819,-1.7733862,16.265226,-12.623974,12.576456,-4.1620197,-11.531728,2.6994927,-11.693629,0.2685494,16.397295,-6.7471285,-11.535749,12.427486,-6.880307,17.132479,-9.927568,-6.405764,-9.190097,-1.3875241,-8.401962,-6.4884644,-2.786163,-4.738859,6.345674,-7.908374,-13.271226,-8.603852,15.942841,16.644316,15.596878,-3.854235,-5.191604,-2.7686908,9.206643,11.114009,16.25299,9.2964735,-8.35829,16.639286,-8.580698,-6.263317,-7.8858767,0.95877767,0.05868011,-1.5834548,-4.9750843,-10.790832,19.182484,-13.060092,11.14868,14.801229,2.0305843,9.255596,-14.440844,-4.5491686,-7.7400355,-11.777042,-14.269421,12.246377,11.168163,-3.986051,-1.2567728,15.981354,16.345734,-5.1459756,18.219961,-5.4064927,-9.103122,1.9977679,-8.064162,0.43154943,-3.956475,-3.5422256,1.1843635,12.302385,-9.27422,-6.164813,18.364073,16.39551,11.300315,-8.400098,6.205543,0.31086394,15.709318,6.774104,-2.4524314,-14.798717,13.180904,-13.123012,12.267851,-12.807533,-9.852931,-0.92440826,-0.31479573,-3.6670768,-3.4206657,18.796612,15.918226,-4.946288,-6.3842,16.602482,13.333479,10.197893,-2.1306608,-7.144305,-2.9617069,6.8909016,-14.89194,-4.875041,-11.416447,-3.3818538,-15.797239,11.314812,-7.105333,16.74787,-12.189671,-5.018966,10.925445,-9.668147,-5.3095946,-4.128563,15.924417,-1.4941634,11.431459,11.387332,-3.719286,-13.918899,-5.6198297,16.152822,-5.896513,-2.8523886,13.621427,14.537505,13.523686,12.12699,-13.397278,-13.186752,6.196604,13.291977,-4.338626,-6.8971243,-12.908175,13.599182,13.1856165],\"y\":[7.3503246,-9.068841,13.611011,-1.2038969,14.777275,-3.5775435,-13.741536,-2.9038363,2.911957,-6.3360248,-5.5012555,9.984658,-6.997801,11.101316,5.3547072,3.8293204,-6.9906282,-6.134225,14.024526,8.302412,-3.8470738,12.563406,11.198108,2.4585063,-0.018365948,14.772335,9.262517,1.773762,3.5883834,2.5510883,2.3851247,-12.408027,10.682073,3.5766356,-0.97054404,13.097239,2.3336523,1.856,11.435457,-4.09338,-8.069373,2.8796585,-4.333543,2.446805,4.0222425,3.1748013,3.781741,2.38979,9.7588825,-8.114144,-8.3608885,14.309881,-0.102627665,3.2732792,-2.052004,-8.438498,-5.4417377,-4.8145375,3.12758,8.162964,0.7200085,-4.6826286,7.242269,2.3619487,-5.392415,3.9523463,-6.573127,-5.258447,9.840132,-2.4127336,3.7181232,-0.22844341,8.669978,-4.657983,9.29246,9.172611,9.455548,3.7906392,6.34049,5.8451138,5.834007,10.757703,11.149357,-4.4064713,-12.287731,-7.542048,-10.337503,-9.844439,-3.8195143,11.232217,11.32162,-4.941204,-2.7967086,-13.5934515,-13.609914,6.5311704,-0.6902108,-7.7695293,-4.182943,9.57684,9.954024,-8.714913,-7.5685897,-1.2747235,6.044655,1.8128835,-2.8427887,-10.533961,9.977293,2.7741115,9.885487,11.933389,-7.7942824,8.267604,13.009832,9.820314,-16.123999,-13.732193,-2.8777223,-3.1985483,9.236718,-1.9728832,-9.751413,-6.422193,-2.3840573,-8.697825,-12.409007,-1.2210723,-11.493702,-7.905455,-2.3874238,-0.4480723,-4.000785,-13.517286,-10.135383,6.7375045,11.859825,-2.541694,-9.96667,-0.71351874,-4.6291184,12.512018,-1.0707587,12.187424,-2.9117453,4.234517,-3.0833294,-7.705205,-10.005351,-0.7534686,10.971168,8.919641,-4.4051027,-0.9442094,11.362086,-1.7707336,-6.616491,-7.8367686,6.2374687,-4.071869,-15.536493,-3.9897516,5.039588,5.657686,-2.028607,-9.431987,-4.3500915,2.2648108,8.370625,-1.8807948,-2.805757,-6.609487,-0.9136808,11.947477,-2.5002143,-9.214906,-1.859772,11.544764,-10.738127,-1.2624007,-3.6874151,0.93448746,-13.106055,4.997532,3.193011,-15.729815,3.6031227,-2.3253167,5.8131714,8.930079,-8.003309,12.793226,-5.3508406,13.232068,-13.478196,-15.148519,-4.97364,-10.672334,9.666002,-2.4936583,-1.7016743,-0.59629697,-14.093289,11.194044,13.011892,-12.499962,6.30524,-2.2446744,0.055347145,-11.84298,-3.5055065,4.8948536,4.0898046,10.970873,-2.044987,-3.136825,-11.216206,4.6931605,5.033958,-0.12079405,13.607374,-8.744049,10.726186,9.482709,-12.122264,7.3187394,-4.491942,6.2598705,-10.322115,3.4235535,-13.887342,-2.992169,-2.2687957,-2.1663694,-3.165462,-8.725505,1.9826812,8.663207,-0.97732866,11.35319,-2.6468768,-6.453712,9.428317,-10.53799,-0.7437655,-11.3761835,5.9800076,5.0642223,-4.2999983,11.613116,-0.6019459,-1.1451348,-0.050726257,-3.63602,2.3740532,0.3005529,7.9788594,-11.662381,4.1550717,-2.5323827,10.250231,-2.266606,13.424202,-3.1227746,0.3996883,-10.32567,-2.0362043,-13.5098715,9.89588,-9.193816,-9.055855,-14.767239,8.109285,9.68212,-2.6559358,-4.479571,6.431419,12.827651,-9.668552,5.5900164,-4.98494,9.506066,4.1995673,8.252285,12.304496,-7.658525,-5.767424,-9.13812,-11.403002,9.560339,12.040561,-11.102678,-2.453473,-7.5981073,-4.4171767,-6.287447,-2.53335,5.548641,-2.6161458,8.725344,-8.241518,-13.0175295,3.272194,-1.0231535,-0.75018847,6.6923122,-7.3585877,6.444054,-7.569094,3.6431482,2.83846,-7.5731273,1.5221585,10.706358,-5.068062,8.956449,-2.1046686,-12.009073,6.859235,-13.933569,-7.675777,6.8342085,-0.8658989,-9.647385,9.188793,11.097336,-0.58453184,-1.9129218,7.3823466,-1.7035236,-13.892728,6.1554894,-3.681914,0.37899035,-9.373138,9.154706,-8.940564,6.1985183,3.3284197,6.3257847,6.7781544,4.266419,-3.1286635,-1.6134909,-2.3669677,9.112108,-13.850419,-9.649919,-1.9109983,6.0465813,-0.18789516,7.428876,-1.6938255,-14.40672,-5.7760653,4.4493375,-1.0605356,-9.2525015,5.625466,13.380536,-8.331528,-8.125361,3.0072758,-6.9171686,-1.7277105,4.7902656,3.0868146,-14.594169,-11.6541,-11.293675,-5.676032,-2.7119982,10.791431,6.321851,-0.80884445,-5.4419,8.1972,-1.0741264,-14.171726,12.498458,-3.668946,0.07304915,8.945295,7.1756535,-8.130686,-0.6423114,-2.0678587,-1.5139959,-2.6711998,11.690709,-6.6446695,-0.317961,9.681593,8.264463,-3.4900303,-14.482789,1.9586533,-0.4032812,10.827801,5.1333437,-3.0426135,-6.7857113,-7.431926,-13.192645,-0.6388936,-14.04823,9.202507,1.2911117,-5.431817,-11.968302,-8.611983,11.206387,4.562663,6.183087,-11.62971,-3.6572444,-8.434492,-10.732339,-2.3198364,8.885634,11.920484,-5.0557017,-8.637379,-8.812985,8.313205,-10.856697,-13.507125,-1.2550582,8.547629,9.115481,10.066092,-15.54177,-4.6525803,-0.54227924,4.063593,-3.5197299,-4.187198,10.415268,9.466349,13.399053,2.6345134,5.93513,13.097672,9.608677,-2.676861,-2.3336062,2.887233,-3.5061839,10.382928,0.59052974,-6.5904813,11.844733,-5.909668,6.560263,-6.624381,-12.830626,-2.2579303,11.409721,6.6873164,7.7047725,-7.746283,11.8100605,-4.22621,5.5860257,0.8824952,11.097903,10.364055,1.9636195,-9.543989,9.175132,8.576924,-2.7087135,-10.789829,6.9078894,-5.4568853,-13.360783,-14.550051,-0.5337391,6.2103953,-9.758501,-7.1712785,-5.710936,-0.50732297,9.3240595,-15.951422,10.109241,-5.278084,8.015476,7.039047,11.552113,5.2794523,-6.2948194,2.3215318,3.135659,9.510049,-2.4129567,-11.332177,-5.2688236,-2.5085728,10.309325,-10.020721,1.5174419,11.963923,-3.532067,8.030002,11.138977,7.6189427,-8.920532,8.193428,1.4792742,13.2706,-8.354356,-6.744622,5.555059,5.3166027,-3.178796,-0.74211174,7.1843686,-0.36997455,-7.5455613,10.491522,9.808113,4.3784084,7.052835,5.8764234,-15.953038,6.696904,-12.990189,-2.0868802,9.690266,-7.559522,3.2805295,-5.047646,-5.2617464,-6.0859365,2.6839683,-12.704924,11.047966,4.7530975,-9.50939,-1.1305758,-3.205362,-8.354422,-10.781449,-10.042792,-5.656839,3.2240117,-2.5262346,-1.7397258,3.9153962,-1.6946747,8.1815815,10.850587,6.366186,-3.971288,-11.36619,-5.327935,8.554379,-1.4400064,0.19485956,10.167267,4.8327203,-4.6907644,-4.1773095,-4.891649,5.0734677,-7.9500074,1.1990733,-2.284473,-5.599812,7.9728975,-5.93604,-1.4325197,6.4140487,-11.92476,3.0889482,-10.614704,-10.702347,8.138734,9.41697,11.279081,7.3210206,6.808256,-11.478351,-7.581123,6.16421,9.376607,-4.4227366,-13.426878,3.1737974,-7.11355,-5.0856004,0.5629979,-0.701809,1.1859341,-0.510221,-1.4598249,3.795385,-0.862256,9.747425,-3.4102266,-1.2602693,1.7913157,-14.546144,-2.9758458,-10.201252,-1.563623,6.8891683,-4.017954,-8.896996,3.6744428,11.051617,10.206597,7.7907586,5.5881367,-5.15407,-4.115521,-4.417342,-2.3666997,9.657839,2.883214,-1.6427966,-0.1068354,0.44327554,7.051018,0.07967488,-15.390452,13.263065,0.14681776,5.8351173,-1.2892033,-10.0800085,8.058205,10.420487,11.334761,5.011456,-0.34713376,2.4892545,-6.333946,1.6242279,12.947251,-11.954536,-14.898018,12.267387,6.1549377,4.8738475,9.118087,6.7131686,0.7840432,8.499913,-4.4374027,-7.6246023,-0.36410418,-4.0714297,-9.241926,-10.410087,10.248871,7.974712,-0.8416308,3.0242646,2.9106636,-0.97266406,0.94054186,8.157679,-2.8109086,1.9367728,-2.8130255,-13.694406,-10.826017,-8.548301,-0.77504694,4.096284,-0.94261277,2.264297,-3.364128,-2.644735,-12.14809,-7.511814,-1.7487788,-13.474607,7.9113493,-5.0522265,6.8109984,-1.1685822,-5.1252284,-9.224799,3.7337193,-8.970836,-5.316027,2.6311886,5.5683117,2.5533757,4.6352057,-4.1882243,-7.3203735,-5.260431,12.519477,7.569318,6.39737,3.924397,-4.2654853,0.48509073,11.101582,3.5872464,4.284741,-8.341253,-4.027201,7.0908923,4.067151,2.596026,3.6173434,-7.1545815,-1.3411309,14.941294,4.975902,-5.1008725,-3.7364087,2.5129225,4.966668,-2.593396,14.002737,-15.841866,-4.894471,14.328721,-1.467004,10.682879,0.9246738,-5.812731,-0.33684376,-3.6031058,-3.7689893,10.048658,2.2751322,-0.69066155,-2.4217935,-5.872634,10.317011,4.617106,5.8733926,3.4231572,-0.91567343,9.41979,8.703109,3.0692055,11.082586,5.07903,9.253452,-7.1838384,11.435725,3.1676679,-9.107942,10.899604,-14.041358,-13.557897,-5.4754286,12.08499,5.966861,-2.2869768,5.625661,-9.24217,4.3470583,-0.8758657,-3.7931774,-10.038559,8.640873,-14.508809,-14.137363,12.103524,-12.175391,13.306134,6.0065737,6.2634754,2.2672682,-13.622875,10.1752205,-5.802953,0.10286258,-3.9564195,8.007688,9.724995,9.720823,-14.442212,-6.8234267,10.7454405,4.882531,3.4994323,-2.4528084,-3.57056,5.2709584,8.032671,-1.0437422,7.465545,3.3603563,7.088379,-14.195421,-13.742387,-3.6111436,10.587504,8.5768795,-7.1477866,-2.4647036,0.86030436,-1.2766892,-10.023662,11.777177,-9.497127,-3.3000748,-10.725585,-4.8980684,-5.4126678,10.370577,2.3090658,7.5723143,12.001133,-6.7629595,10.207145,-8.455044,0.2663258,-1.6430811,-2.4226155,-4.286519,10.192497,6.166672,-9.03126,-6.105839,-2.935211,-4.916811,6.146902,10.522508,4.2033534,-11.465581,12.4884615,6.8674307,2.5210555,11.9301195,-2.3995035,10.63332,13.26002,-4.5974035,-3.2294357,9.147257,-1.178399,-2.237594,-10.123903,-12.367661,-5.599504,-6.776693,8.6288185,10.302485,-2.3282819,-5.100841,10.294816,-2.067451,9.120508,-2.5291717,-8.208534,-2.5228732,-6.349119,6.4866333,3.4854481,10.97361,6.8264656,-0.8082978,9.696213,-7.1348014,-2.5567634,-14.175821,7.7412744,-3.110763,11.998438,-2.6205502,-2.1054854,-10.489448,10.3761015,-14.756945,-3.800275,-3.1518517,-4.4655666,7.33252,11.85741,12.294492,4.0298915,4.114172,0.6690134,-0.5857863,-7.4634953,9.884334,-2.4679651,8.292135,2.756208,10.940673,3.2257965,7.74434,4.9422336,4.7608333,10.4923,7.2151556,-10.145239,-2.2935045,0.17103909,-8.981935,-2.4021852,-4.67841,0.40779915,9.31917,11.630136,-2.374819,-11.564001,7.3222036,9.121425,-7.564545,-4.207744,-2.5244792,9.127404,2.855976,13.504749,6.2488246,-7.204876,-6.3273582,-6.5872545,10.336704,-4.0146213,-3.4968345,6.2787576,-8.682339,11.806347,-14.316682,6.799376,-2.8141809,-8.646298,13.530621,-3.015475,-10.092862,2.0338147,-1.0095133,8.8886595,-1.1201943,12.65467,-10.113942,-4.700723,8.504375,-2.3559697,5.9475846,-5.8874984,6.2910357,4.7828727,5.1829963,6.49341,-9.072343,-14.804311,5.778079,0.93529946,0.11971335,-1.5158168,-1.0224881,-8.374928,-7.1659145,2.782398,2.6356947,-3.8002565,-13.584721,-4.8486147,11.345751,6.6668997,8.029356,-8.780581,-7.8696866,6.7944264,3.6767492,-3.517159,5.161891,7.7868404,6.419752,13.499836,6.180994,-11.636087,-10.097742,5.8931427,2.1686566,-3.8290198,3.8678308,10.17948,6.5291815,-15.13674,5.325022,-7.107078,-4.124804,-2.8282514,-5.5751824,10.578734,-3.0837958,7.3596144,4.4691668,12.522114,4.3987684,7.006245,-4.2646084,5.525408,-1.1523664,10.291828],\"z\":[0.15468317,4.521765,-2.5847285,-3.0774443,-0.5739706,-0.20432456,4.550959,0.96930116,0.39669052,2.684771,2.611489,-4.4281964,-3.845944,3.744063,-0.7992734,-5.1668115,-3.7894561,-0.06540547,-2.459131,-4.364927,-0.41538876,-0.67197996,2.884463,1.0349984,3.9583318,-1.0156744,-3.9694474,-2.6382492,-5.8966937,1.4166971,-2.0340252,1.5946434,3.4989214,-1.130594,3.7856402,-2.2202537,-2.9193697,-2.8592348,0.7770305,1.2116777,0.6747712,-1.648394,-2.5280132,3.6708994,-0.89351976,-5.5921636,-1.7303857,0.70046544,-4.2891717,-1.3611356,-0.77482885,-2.251711,2.1758325,-5.4067593,-1.0169278,-0.5908629,1.7197156,5.5409236,-1.0820935,0.8152344,4.3929477,-3.226409,-2.494087,-1.4787636,-1.5077807,-2.59498,3.1563675,-0.20144105,-3.1244752,3.2063704,0.90629166,-0.6317951,2.560608,-2.068786,-3.375548,-2.2329814,-1.0832626,-0.5465393,0.28110534,2.1748877,1.9595098,-2.2453785,2.275494,-4.325464,1.9040766,1.9937142,-2.5532293,1.3749152,2.2697976,0.75753844,-3.236641,1.7329113,-2.8354855,3.6669638,-0.363958,-1.2747349,-0.15222836,-0.2857636,-6.480031,0.13604252,2.1976807,-0.75397617,-0.10554479,2.5556047,-2.7722602,-1.0150005,-2.8990083,-2.6485672,-2.67234,-1.0107794,0.717089,3.0460892,2.1432252,-0.5428987,-2.2260098,0.14279148,0.61425674,3.7267141,-2.552088,-6.348048,0.40641358,2.344987,-2.1300664,-1.8872354,-5.342481,1.9985569,-1.1226715,-1.6825782,1.634951,1.1298633,4.9599447,0.5977495,-2.1113544,3.6102035,-0.45656723,-2.3831527,0.96568,-3.4377248,1.6034575,-0.07954119,-1.4351536,-0.47602925,3.2957773,0.84226024,-6.824799,0.25759163,2.7245958,1.19556,2.8856552,-1.7545595,1.5677531,-2.802143,0.6565415,4.0303226,0.0779611,-0.22367139,-1.055284,-1.2059305,-3.3886507,-0.9252069,1.4384049,5.221098,-2.6679327,2.002583,-0.9145315,-1.6659874,-2.3367,2.5133855,-0.49517995,-5.3857584,-2.6520343,0.34435984,2.4673202,-1.5103117,-3.488977,0.48929244,0.10045556,-2.0093968,2.986684,-2.9842741,-0.9912323,1.7078433,0.12244527,2.430693,2.7260168,1.4424449,0.64860827,4.9085803,0.005551888,-2.159792,0.6558306,-0.21325274,1.3236432,-1.7934085,-2.1992233,3.3173158,-1.3896323,-3.2711723,3.3657384,-4.731132,3.122313,-1.2493571,-1.9186577,2.0822802,-2.1915827,1.1100045,4.0113297,3.4666731,1.2572774,0.758049,-3.9829695,1.8936077,-2.1271403,0.42689195,2.3989968,0.19460575,1.0986359,0.40470496,-1.721296,0.35580736,-2.668935,-1.5032037,1.0193316,-1.4550947,-1.4662098,3.3287787,-1.2102807,1.0478238,-0.1576754,4.937485,3.1268508,-2.8540485,-2.3788595,-3.2927694,1.4436013,-2.079682,3.7421584,-1.8524091,-1.4814503,-3.2908585,3.2602937,-2.210289,-1.9403447,1.4440553,1.6525346,-1.2912143,-2.167938,-1.1850377,-0.6082211,2.2501564,5.874713,-1.9068251,-0.6018,5.0684247,-0.286537,0.17409034,-2.2101967,1.2852592,4.800613,-6.9817815,-1.4435287,5.2026615,-0.33303317,-2.2477396,4.526941,1.3597165,-2.9250305,-0.8783784,-2.9190164,-4.275683,-3.4479318,-0.1742535,-3.9130049,-2.3486674,-1.074838,4.802878,-1.3250309,-0.3806141,-2.6713626,-0.25567362,-1.3732938,-2.0373251,0.25522327,-0.68137944,-2.1494322,-0.39602903,-5.1549115,-1.1341975,0.687075,2.951451,1.1560712,-2.616149,-2.6701975,-1.082447,-4.182899,-0.16787899,2.3339107,1.8793236,-2.650937,-1.3304452,2.7027037,1.9101601,0.023209708,0.91896176,2.7373655,-3.7543283,-2.0080476,-3.0087297,0.77994066,2.889614,3.0100424,2.4107404,2.3585086,-3.0046396,0.40313172,0.20153295,-5.5202785,2.9414222,-3.112419,-1.5617223,2.2814202,-3.6477828,-1.5706869,0.120268196,2.0562408,-2.033804,-3.0913074,-1.8010865,1.3044662,1.9067278,-0.9925734,0.3508105,5.513943,-0.7208672,1.0102401,-0.30312675,1.0687723,-2.6358454,0.363356,1.4416586,-3.4479425,2.2108092,-2.710562,4.562489,0.4154878,1.839871,3.0034125,1.252079,-1.5776895,0.63907444,-2.351003,-4.006654,-7.114829,0.024003595,-5.2246866,2.1597843,2.3596203,-0.060906116,-0.6206059,-1.2513916,3.0152674,-1.2697836,0.91232544,0.42706305,-1.4941657,-2.266749,-1.8654363,1.0719482,0.76094025,-2.6057706,-4.0821853,3.1250074,-1.0741098,0.078724995,-1.6379112,-2.5684273,-2.5483103,1.7772284,-1.0312924,-1.3937699,-0.39393649,0.30037135,-3.6750436,2.7784915,2.106074,-0.4274802,-2.5708435,-2.255632,-3.9630487,2.6482327,-3.5780656,-0.4108928,-4.516508,-2.163349,2.3649683,-0.15932249,-1.3076295,1.3068331,-0.9603627,-1.7506149,-6.1825647,-3.4111784,2.531799,1.1986732,1.6755806,-0.05442085,0.24585705,0.74094963,-0.9008229,-1.587945,0.55325955,-1.377312,-2.5832772,-2.632882,-2.9068582,-2.7238178,0.19266367,2.9678347,-2.0316532,1.7247448,2.7968779,-2.343157,-1.8247718,3.1943674,-0.5332443,-0.22632815,3.317342,2.3588555,1.7157032,-0.71431124,-0.2769064,2.5541408,-4.730803,0.75258136,0.1626161,-2.3335717,-0.05382876,-0.21433593,-2.0788138,-1.5416611,-0.87267107,0.2818495,-2.3413093,-0.23932038,1.3103139,0.46838278,0.46692124,3.9739108,1.7681881,0.12703475,-3.7429388,0.49653485,-2.0191472,-3.3299065,2.081669,-0.7810211,-1.5109595,0.44748074,-0.41805053,0.93184066,2.3978488,-2.5334864,-3.4316049,3.20155,-0.5017851,-3.267004,-2.7964284,0.4380226,-1.2493149,-0.59358424,1.0929681,-0.17722635,-3.0741816,-2.975396,-3.252573,-1.5800968,3.1910942,3.5910914,-0.8118485,1.3493135,0.06811539,-4.0859766,3.5391746,1.3367873,1.1960293,0.31213796,-0.019644992,2.414357,-2.5039904,-4.0846148,-1.2397256,0.64898527,4.6691303,-0.25066575,-1.2747353,3.0366347,-2.2490654,-0.17811947,3.659519,1.3521656,1.5253632,-2.2512703,-2.0449874,1.9964532,1.5780783,-3.5859752,-1.8968189,2.203043,3.0567122,2.021309,-1.356651,-0.8445919,-2.7814832,1.2439454,1.2249173,3.794626,5.583562,-1.1250746,-2.415545,-2.7421787,3.9343305,3.0024755,3.7707846,1.5704172,-1.6838444,-0.6022186,2.4935207,1.8021283,-3.194937,1.1570721,-0.28441077,-1.5291678,-1.7545885,-4.4334273,0.89351827,3.15776,-0.6905912,1.4968783,1.1310959,-4.0207357,0.2133368,1.288409,0.37575665,3.2190928,0.9669576,-2.415975,-0.03771202,0.2799784,2.9476674,-0.083201855,1.7981559,-1.5588886,-1.837985,-3.429173,4.552252,-1.1633233,-1.3663174,-4.7325397,-2.7578537,0.09136985,-0.5545992,-1.9420937,4.3414865,3.4335155,-5.6806064,3.5240746,-2.2045794,-1.9868625,3.3112564,2.9193416,0.6930318,1.7299879,0.9277333,0.5455671,3.20792,2.6270006,2.8911316,1.5753491,-1.1597202,0.48699373,2.5379956,-0.89414823,-0.23104827,3.3531399,0.59165263,4.296491,0.3495564,-3.005852,-0.83490735,-1.7759584,-2.9900029,1.4915308,1.2206714,1.3800831,1.5623363,3.3781567,-0.1362851,-2.128349,2.7813425,-0.08318723,-2.7801325,2.689971,2.0355866,3.1178026,2.240637,1.7631617,-1.014366,-1.3424765,-6.56728,1.7367587,3.9721837,0.36070162,3.406753,-3.454,2.094818,3.3766599,0.17096533,2.7745333,-5.641415,0.8667646,0.45364562,2.7833867,5.370926,-2.1704667,0.8951221,-0.106376015,1.3891621,-2.6999469,1.326695,3.3326507,6.252908,-2.6709933,-2.63155,2.634036,0.93950176,1.0870417,6.9104095,0.84974134,2.3048375,2.5353189,-2.359248,0.5404643,3.1051788,-1.0825058,-0.90031904,-2.693812,-0.4854434,-1.6258996,3.085705,-4.4406395,-1.2591637,-2.1633475,0.6952507,-2.5878491,-0.17205721,0.49279454,0.85966057,-1.3553648,-1.0585358,-0.56353724,0.9274896,2.4425268,-0.10879285,-0.62807053,1.0468268,3.8489668,-3.7646334,3.1899812,-3.4327068,-0.2174009,0.30432162,1.5814567,5.6981397,2.2444546,-0.11659604,1.3084407,2.4093008,0.8329518,0.9400408,3.7518291,-0.62911457,-2.4290662,-4.016976,2.2503428,5.9755516,-0.559737,0.24934034,0.9321574,2.135428,2.0831025,-1.6752414,3.2796574,-2.0497885,-0.97206926,1.5770696,-2.0665758,-1.771187,3.1767359,-1.913226,0.93507224,2.2245429,4.6097856,3.7139375,-5.925132,-2.033064,-0.4667574,1.1931071,3.4569223,0.14179002,3.6830683,-1.4865392,-1.5559754,-4.8445673,-0.99147636,6.3497076,0.9506629,-0.13342561,-5.2137446,6.376711,-3.7638552,-2.470143,-1.4927375,0.16628166,-2.2438989,-4.4314756,3.518645,0.8359999,-2.5567164,-2.661177,1.1222633,-4.016338,-1.5623113,1.8610957,-6.855602,-0.64344865,2.6303244,0.074244104,0.11777013,1.0908465,2.9954674,-2.1768684,-1.9706416,-1.6089774,5.36034,-0.9239506,1.6961384,1.7833346,1.0872192,-0.21646284,0.56997436,3.6721027,-1.4248539,0.016305335,-2.0763087,-0.3697613,-2.0858831,2.0968482,4.88738,0.6993949,2.023248,1.9750929,5.8891025,-2.1970778,-0.9783671,0.12467306,1.0116844,-0.06324377,-0.73073465,3.2984195,-0.26196188,1.7795018,-0.21678619,-0.5010635,2.9969385,-1.9928842,1.866806,1.2197711,0.8061549,-2.8346012,-2.3573055,-1.5369773,0.69273645,0.7491657,1.2425393,-0.49819437,1.476662,5.8990893,0.10822746,3.2294955,3.9770424,1.7711512,-1.462822,4.3548174,-4.1855893,-1.3291972,1.1639546,0.53769654,1.0735036,-1.3944407,1.7338246,1.3028066,-0.25444555,-2.9070907,4.2532096,-3.2273223,-1.2554587,4.2009587,-0.59577006,-1.0525839,-4.930915,0.031049168,5.7020435,-3.5867233,2.875917,1.2828659,1.7271925,-0.50384897,0.71182835,0.6952528,-0.47105253,3.5205948,-3.0022664,2.023308,0.9794758,-2.3181348,-0.1500274,-0.8332487,1.8586468,-3.7747214,-1.039719,-1.5261658,-1.9055519,-1.6147343,4.7131147,0.48489043,2.0391183,-1.2643157,0.2242106,3.6582341,3.9224808,-0.9680696,2.5575857,-1.0778067,-2.6818748,2.2387166,0.017139107,-1.9277889,-0.8903902,1.2396054,-5.4659295,-1.3677089,3.4672441,4.1603093,-0.39767507,-3.8357096,-3.4758565,-0.7544622,-1.3850107,2.729196,4.649255,1.0264417,1.746986,-0.049517184,2.1328561,-2.4544878,-3.13918,3.3182542,3.6115108,1.9810387,-0.09505429,3.71461,1.719572,-2.2910402,1.5853558,-1.1860491,-3.2309804,2.3686311,1.0586749,0.04836181,-1.4707729,0.5164092,-3.076815,1.4906231,-1.337527,1.4415579,1.7377828,-2.2003477,-2.7178698,-1.4619304,3.7904093,0.6622232,2.5345707,-0.007775175,0.98914874,2.7928007,-2.1593044,-2.269635,0.2068188,-2.8472629,-3.9939566,-1.0966798,4.698488,1.353333,0.059882723,1.8291237,2.2917244,2.144163,1.1780409,0.19881734,-1.5780135,-2.3062496,-3.06133,-2.8274972,-3.287008,3.5517619,-2.303609,0.649619,0.3356404,-0.6781571,2.376679,2.9440675,-1.9938498,-6.8827157,-1.1173707,-3.674917,1.6587335,0.6301262,-0.006439655,-2.637674,-3.615438,-1.7294127,2.5305588,-3.1449869,0.8392187,0.889896,-0.73461056,-0.38882557,-1.101119,3.3064315,3.3422241,0.41050765,4.8651342,0.79218256,-0.8019401,-2.474005,2.65862,-0.95268327,-3.2748194,-2.1367579,1.1885713,-0.8402296,0.2097634,0.9420932,-2.151666,-1.7841107,0.18122199,1.6534753,-0.046912998,4.9336047,2.133017,-0.50835687,-2.3604436,-2.2681286,-1.0810164,-1.3535215,-0.7927781,-1.6307396,-3.8101144,2.4319327,5.0105686,-1.3843493,-0.059090342,-3.7781692,-2.0591505,0.19583592,1.3229271,0.9196568,-0.6430105,1.7977804,0.55807126,3.4604976,2.3469777,2.2740371,2.21217,-0.19049405,-3.4029787,1.5193161,2.9658227,2.129035,-0.17833279,-0.89885867,-4.1454487,-2.6080732,-1.0326589,2.052659,-1.0071954,-6.4683466,3.0224552,1.2424858,-1.3638362],\"type\":\"scatter3d\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"scene\":{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"xaxis\":{\"title\":{\"text\":\"0\"}},\"yaxis\":{\"title\":{\"text\":\"1\"}},\"zaxis\":{\"title\":{\"text\":\"2\"}}},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"Anomaly\"}},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"3d TSNE Plot for Outliers\"},\"height\":800,\"width\":900},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('7119918d-22dd-4412-89df-4f1be0dd80be');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 5: Evaluate the Results**"
      ],
      "metadata": {
        "id": "JCIsebqUGiW6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(iforest)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219,
          "referenced_widgets": [
            "e3ec36fc346d44dda3b9bb7a81ec6dcb",
            "4678778a5f4a4975994302b2c7a226a6",
            "9599a19bb03f4fe88d85f36144f85274",
            "d61be4f2df3b408fb7b23093f8abe118",
            "147cb5148b144f52b6e375d124c83c05",
            "909c5e14e95d463eadce7d9c9f588d2c",
            "aa19e82be23a4a36b207c414639f1451"
          ]
        },
        "id": "cmLuX41mGmys",
        "outputId": "309a1795-3c86-4f58-96ad-429f41dbaa45"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e3ec36fc346d44dda3b9bb7a81ec6dcb"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prediction**"
      ],
      "metadata": {
        "id": "eZllxRnSMZn1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# predict on test set\n",
        "iforest_pred = predict_model(iforest, data=data)\n",
        "iforest_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "bLON1s2XL104",
        "outputId": "d5196fb4-3fdf-40fa-e15f-81901540bad3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
              "0    0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
              "1    0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
              "2    0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
              "3    0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
              "4    0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
              "..        ...       ...       ...       ...       ...       ...       ...   \n",
              "995  0.305055  0.656837  0.331665  0.822525  0.907127  0.882276  0.855732   \n",
              "996  0.812627  0.864258  0.616604  0.167966  0.811223  0.938071  0.418462   \n",
              "997  0.250967  0.138627  0.919703  0.461234  0.886555  0.869888  0.800908   \n",
              "998  0.502436  0.936820  0.580062  0.540773  0.151995  0.059452  0.225220   \n",
              "999  0.457991  0.017755  0.714113  0.125992  0.063316  0.154739  0.922974   \n",
              "\n",
              "         Col8      Col9     Col10  Anomaly  Anomaly_Score  \n",
              "0    0.608234  0.723782  0.733591        0      -0.035865  \n",
              "1    0.331679  0.429297  0.367422        0      -0.084927  \n",
              "2    0.861309  0.899017  0.088600        1       0.025356  \n",
              "3    0.158963  0.073715  0.208463        1       0.042415  \n",
              "4    0.558449  0.885169  0.182754        0      -0.023408  \n",
              "..        ...       ...       ...      ...            ...  \n",
              "995  0.584786  0.808640  0.242762        0      -0.083981  \n",
              "996  0.472306  0.348347  0.671129        0      -0.075839  \n",
              "997  0.530324  0.779433  0.234952        0      -0.052903  \n",
              "998  0.242755  0.279385  0.538755        0      -0.075104  \n",
              "999  0.692299  0.816777  0.307592        0      -0.008665  \n",
              "\n",
              "[1000 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08d2a83e-94bc-40b6-a652-04890652816b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Col1</th>\n",
              "      <th>Col2</th>\n",
              "      <th>Col3</th>\n",
              "      <th>Col4</th>\n",
              "      <th>Col5</th>\n",
              "      <th>Col6</th>\n",
              "      <th>Col7</th>\n",
              "      <th>Col8</th>\n",
              "      <th>Col9</th>\n",
              "      <th>Col10</th>\n",
              "      <th>Anomaly</th>\n",
              "      <th>Anomaly_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.263995</td>\n",
              "      <td>0.764929</td>\n",
              "      <td>0.138424</td>\n",
              "      <td>0.935242</td>\n",
              "      <td>0.605867</td>\n",
              "      <td>0.518790</td>\n",
              "      <td>0.912225</td>\n",
              "      <td>0.608234</td>\n",
              "      <td>0.723782</td>\n",
              "      <td>0.733591</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.035865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.546092</td>\n",
              "      <td>0.653975</td>\n",
              "      <td>0.065575</td>\n",
              "      <td>0.227772</td>\n",
              "      <td>0.845269</td>\n",
              "      <td>0.837066</td>\n",
              "      <td>0.272379</td>\n",
              "      <td>0.331679</td>\n",
              "      <td>0.429297</td>\n",
              "      <td>0.367422</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.084927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.336714</td>\n",
              "      <td>0.538842</td>\n",
              "      <td>0.192801</td>\n",
              "      <td>0.553563</td>\n",
              "      <td>0.074515</td>\n",
              "      <td>0.332993</td>\n",
              "      <td>0.365792</td>\n",
              "      <td>0.861309</td>\n",
              "      <td>0.899017</td>\n",
              "      <td>0.088600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.025356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.092108</td>\n",
              "      <td>0.995017</td>\n",
              "      <td>0.014465</td>\n",
              "      <td>0.176371</td>\n",
              "      <td>0.241530</td>\n",
              "      <td>0.514724</td>\n",
              "      <td>0.562208</td>\n",
              "      <td>0.158963</td>\n",
              "      <td>0.073715</td>\n",
              "      <td>0.208463</td>\n",
              "      <td>1</td>\n",
              "      <td>0.042415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.325261</td>\n",
              "      <td>0.805968</td>\n",
              "      <td>0.957033</td>\n",
              "      <td>0.331665</td>\n",
              "      <td>0.307923</td>\n",
              "      <td>0.355315</td>\n",
              "      <td>0.501899</td>\n",
              "      <td>0.558449</td>\n",
              "      <td>0.885169</td>\n",
              "      <td>0.182754</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.023408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>0.305055</td>\n",
              "      <td>0.656837</td>\n",
              "      <td>0.331665</td>\n",
              "      <td>0.822525</td>\n",
              "      <td>0.907127</td>\n",
              "      <td>0.882276</td>\n",
              "      <td>0.855732</td>\n",
              "      <td>0.584786</td>\n",
              "      <td>0.808640</td>\n",
              "      <td>0.242762</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.083981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>0.812627</td>\n",
              "      <td>0.864258</td>\n",
              "      <td>0.616604</td>\n",
              "      <td>0.167966</td>\n",
              "      <td>0.811223</td>\n",
              "      <td>0.938071</td>\n",
              "      <td>0.418462</td>\n",
              "      <td>0.472306</td>\n",
              "      <td>0.348347</td>\n",
              "      <td>0.671129</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.075839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>0.250967</td>\n",
              "      <td>0.138627</td>\n",
              "      <td>0.919703</td>\n",
              "      <td>0.461234</td>\n",
              "      <td>0.886555</td>\n",
              "      <td>0.869888</td>\n",
              "      <td>0.800908</td>\n",
              "      <td>0.530324</td>\n",
              "      <td>0.779433</td>\n",
              "      <td>0.234952</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.052903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0.502436</td>\n",
              "      <td>0.936820</td>\n",
              "      <td>0.580062</td>\n",
              "      <td>0.540773</td>\n",
              "      <td>0.151995</td>\n",
              "      <td>0.059452</td>\n",
              "      <td>0.225220</td>\n",
              "      <td>0.242755</td>\n",
              "      <td>0.279385</td>\n",
              "      <td>0.538755</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.075104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>0.457991</td>\n",
              "      <td>0.017755</td>\n",
              "      <td>0.714113</td>\n",
              "      <td>0.125992</td>\n",
              "      <td>0.063316</td>\n",
              "      <td>0.154739</td>\n",
              "      <td>0.922974</td>\n",
              "      <td>0.692299</td>\n",
              "      <td>0.816777</td>\n",
              "      <td>0.307592</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.008665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 12 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08d2a83e-94bc-40b6-a652-04890652816b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-08d2a83e-94bc-40b6-a652-04890652816b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-08d2a83e-94bc-40b6-a652-04890652816b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fd5236b3-3e33-470e-9dea-8890b5a2e1fc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fd5236b3-3e33-470e-9dea-8890b5a2e1fc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fd5236b3-3e33-470e-9dea-8890b5a2e1fc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_070b71a6-f013-4c2b-94c3-02b609bcbdfd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('iforest_pred')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_070b71a6-f013-4c2b-94c3-02b609bcbdfd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('iforest_pred');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "iforest_pred",
              "summary": "{\n  \"name\": \"iforest_pred\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Col1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2591380084506426,\n        \"min\": 0.0,\n        \"max\": 0.99443072,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.912937812,\n          0.435296009,\n          0.573493847\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25193138881205795,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.6034935,\n          0.290440743,\n          0.71194044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25660639164786075,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.686645381,\n          0.823521345,\n          0.448518031\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2635617786543451,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.129411327,\n          0.169415852,\n          0.841721968\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3346578662269241,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.158402475,\n          0.885059958,\n          0.792152985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31746975110866293,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.063175913,\n          0.524440536,\n          0.478297845\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col7\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27848329985006426,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.752866818,\n          0.768405388,\n          0.643619434\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22012860569216827,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.448788845,\n          0.647698063,\n          0.344078758\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col9\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24143244105932168,\n        \"min\": 0.0,\n        \"max\": 0.988731809,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.516369185,\n          0.660341101,\n          0.492050258\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Col10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21167715932454373,\n        \"min\": 0.014494632,\n        \"max\": 1.0,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.497103964,\n          0.433412044,\n          0.492949724\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anomaly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Anomaly_Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.032838878078832845,\n        \"min\": -0.1329840497444854,\n        \"max\": 0.06403665110003964,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          -0.050782637813318154,\n          -0.09429293902637131\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 6: Final Model and Conclusion**"
      ],
      "metadata": {
        "id": "ns1WrpZpG_b1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # save pipeline\n",
        "save_model(iforest, 'iforest_pipeline')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVeyxcI2Jqr2",
        "outputId": "34c00997-b9d7-4d20-cee0-16ecf4644042"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=Memory(location=None),\n",
              "          steps=[('numerical_imputer',\n",
              "                  TransformerWrapper(include=['Col1', 'Col2', 'Col3', 'Col4',\n",
              "                                              'Col5', 'Col6', 'Col7', 'Col8',\n",
              "                                              'Col9', 'Col10'],\n",
              "                                     transformer=SimpleImputer())),\n",
              "                 ('categorical_imputer',\n",
              "                  TransformerWrapper(include=[],\n",
              "                                     transformer=SimpleImputer(strategy='most_frequent'))),\n",
              "                 ('trained_model',\n",
              "                  IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
              "     max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=-1,\n",
              "     random_state=123, verbose=0))]),\n",
              " 'iforest_pipeline.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FG82tHJ464jf"
      },
      "source": [
        "### **Building Ensemble Models using PyCaret**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOmJHj0E5wNf"
      },
      "source": [
        "Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance. In PyCaret, we can create bagging, boosting, blending, and stacking ensemble models with just one line of code. Here we will try with blending.\n",
        "Blending models is a method of ensembling which uses consensus among estimators to generate final predictions. The idea behind blending is to combine different machine learning algorithms and use a majority vote or the average predicted probabilities in case of classification to predict the final outcome."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bagged_dt = ensemble_model(dt, method = 'Bagging')"
      ],
      "metadata": {
        "id": "ykv0j3HS5Ju5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXVYs8LA9tnG"
      },
      "source": [
        "# train a voting regressor dynamically\n",
        "blender_specific = blend_models(estimator_list = compare_models(n_select = 5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q67_AV2v6G_k"
      },
      "source": [
        "The calibrated blending model is as follows:-\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cj8GY31cd_ie"
      },
      "source": [
        "blender_specific"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtPrJpWDeJJD"
      },
      "source": [
        "**Analyzing model performance in PyCaret**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HNLexbneNx2"
      },
      "source": [
        "plot_model(blender_specific)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jI2a_yYOgk2s"
      },
      "source": [
        "**Evaluate the model performance**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kcPnISignRz"
      },
      "source": [
        "evaluate_model(blender_specific)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93Vb0BLfg1Wc"
      },
      "source": [
        "**Make predictions on the validation set**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnlApCWHg30U"
      },
      "source": [
        "val_pred = predict_model(blender_specific)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I644BH0ghBGE"
      },
      "source": [
        "**Make Predictions on unseen data**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sl5vGADphDja"
      },
      "source": [
        "# predictions on new dataset\n",
        "pred_new = predict_model(blender_specific, data = X_train) #new_data is pd dataframe\n",
        "pred_new = pred_new.rename(columns={‘RESULT_VALUE’:'prediction'})\n",
        "pred_new"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bx_ALQazhrLc"
      },
      "source": [
        "**Save and Load the Model**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4R7TJ9PPhuPD"
      },
      "source": [
        "save_model(blender_specific , ‘/content/drive/MyDrive/blender_specific_saved_06142021’)\n",
        "# Loading the saved model\n",
        "blend_saved = load_model(‘/content/drive/MyDrive/blender_specific_saved_06142021’)\n",
        "pred = blend_saved.predict(test)\n",
        "pred[:5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Clustering**"
      ],
      "metadata": {
        "id": "M_3HqZgVywDT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Dataset**"
      ],
      "metadata": {
        "id": "ljJJ_o0ek8r7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.datasets import get_data\n",
        "data = get_data('mice')"
      ],
      "metadata": {
        "id": "LhrR3H_wlJn8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset.sample(frac=0.95, random_state=786)\n",
        "data_unseen = dataset.drop(data.index)"
      ],
      "metadata": {
        "id": "hUakoch1vzS9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.reset_index(drop=True, inplace=True)\n",
        "data_unseen.reset_index(drop=True, inplace=True)"
      ],
      "metadata": {
        "id": "Lilw4l-Dv42X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Setting up Environment in PyCaret**"
      ],
      "metadata": {
        "id": "ieFEDaBLmY8D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize PyCaret regression setup\n",
        "regression_setup = setup(data=df, target='MedHouseVal', session_id=123)"
      ],
      "metadata": {
        "id": "zpFB9y0DmhZF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "17fd8ac2-26ae-4b9c-b35b-1c98e3df2fac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7812db242d50>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_a3302_row8_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_a3302\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_a3302_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_a3302_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_a3302_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_a3302_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_a3302_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_a3302_row1_col1\" class=\"data row1 col1\" >MedHouseVal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_a3302_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_a3302_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_a3302_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_a3302_row3_col1\" class=\"data row3 col1\" >(20640, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_a3302_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_a3302_row4_col1\" class=\"data row4 col1\" >(20640, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_a3302_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_a3302_row5_col1\" class=\"data row5 col1\" >(14447, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_a3302_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_a3302_row6_col1\" class=\"data row6 col1\" >(6193, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_a3302_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_a3302_row7_col1\" class=\"data row7 col1\" >8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_a3302_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_a3302_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_a3302_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_a3302_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_a3302_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_a3302_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_a3302_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_a3302_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_a3302_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_a3302_row12_col1\" class=\"data row12 col1\" >KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_a3302_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_a3302_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_a3302_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_a3302_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_a3302_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_a3302_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_a3302_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_a3302_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_a3302_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_a3302_row17_col1\" class=\"data row17 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a3302_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_a3302_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_a3302_row18_col1\" class=\"data row18 col1\" >5496</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The setup function in PyCaret initializes the environment and creates the transformation pipeline for modeling and deployment. setup must be called before executing any other function in pycaret. It takes only one mandatory parameter: a pandas dataframe. All other parameters are optional can be used to customize the preprocessing pipeline[15]"
      ],
      "metadata": {
        "id": "KpBTJ9mlm6H8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "When setup is executed, PyCaret's inference algorithm will automatically infer the data types for all features based on certain properties. The data type should be inferred correctly but this is not always the case. To handle this, PyCaret displays a prompt, asking for data types confirmation, once you execute the setup. You can press enter if all data types are correct or type quit to exit the setup[14]"
      ],
      "metadata": {
        "id": "GfWAjS-SnDy-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ensuring that the data types are correct is really important in PyCaret as it automatically performs multiple type-specific preprocessing tasks which are imperative for machine learning models[15]"
      ],
      "metadata": {
        "id": "pQuFPPZonJrd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Alternatively, you can also use numeric_features and categorical_features parameters in the setup to pre-define the data types."
      ],
      "metadata": {
        "id": "sfKYOrbznQBR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once the setup has been successfully executed it displays the information grid which contains some important information about the experiment. Most of the information is related to the pre-processing pipeline which is constructed when setup is executed. The majority of these features are out of scope for this tutorial, however, a few important things to note are [15]"
      ],
      "metadata": {
        "id": "2m4xTwCUnZt3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**session_id:** A pseduo-random number distributed as a seed in all functions for later reproducibility. If no session_id is passed, a random number is automatically generated that is distributed to all functions. In this experiment, the session_id is set as 123 for later reproducibility."
      ],
      "metadata": {
        "id": "r57In012pljq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Missing Values:** When there are missing values in original data this will show as True. Notice that Missing Values in the information grid above is Trueas the data contains missing values which are automatically imputed using mean for the numeric features and constant for the categorical features in the dataset. The method of imputation can be changed using the numeric_imputation and categorical_imputation parameters in the setup"
      ],
      "metadata": {
        "id": "LyQmgddAptG5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Original Data:** Displays the original shape of the dataset. In this experiment (1026, 82) means 1026 samples and 82 features."
      ],
      "metadata": {
        "id": "MTYtou8Ep2Hx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Transformed Data:** Displays the shape of the transformed dataset. Notice that the shape of the original dataset (1026, 82) is transformed into (1026, 91). The number of features has increased due to the encoding of categorical features in the dataset."
      ],
      "metadata": {
        "id": "ZckK5PEep4hZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Numeric Features:** The number of features inferred as numeric. In this dataset, 77 out of 82 features are inferred as numeric."
      ],
      "metadata": {
        "id": "yu4BpHTkqEa6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Categorical Features:** The number of features inferred as categorical. In this dataset, 5 out of 82 features are inferred as categorical. Also notice that we have ignored one categorical feature MouseID using the ignore_feature parameter since it's a unique identifier for each sample and we don’t want it to be considered during model training."
      ],
      "metadata": {
        "id": "wdUZ7nj3qLHk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice how a few tasks that are imperative to perform modeling are automatically handled such as missing value imputation, categorical encoding, etc. Most of the parameters in the setup function are optional and used for customizing the pre-processing pipeline. These parameters are out of scope for this tutorial but I will write more about them later."
      ],
      "metadata": {
        "id": "GvUL7h3EqNZE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialize PyCaret regression setup\n",
        "regression_setup = setup(data=df, target='MedHouseVal', session_id=123)"
      ],
      "metadata": {
        "id": "TJM2WGjkTuVg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Create a Model**"
      ],
      "metadata": {
        "id": "xU3lqtcP2q-3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training a clustering model in PyCaret is simple and similar to how you would create a model in the supervised learning modules of PyCaret. A clustering model is created using the create_model function. This function returns a trained model object and a few unsupervised metrics. See an example below:"
      ],
      "metadata": {
        "id": "f5ZLHVx4qZE3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = create_model('kmeans')"
      ],
      "metadata": {
        "id": "Yti4gJHxqet3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(kmeans)\n"
      ],
      "metadata": {
        "id": "TUkJGParqkkf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have trained an unsupervised K-Means model using the create_model. Notice the n_clusters parameter is set to 4 which is the default when you do not pass a value to the num_clusters parameter. In the below example we will create a kmodes model with 6 clusters."
      ],
      "metadata": {
        "id": "wiyPQxggqv_N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmodes = create_model('kmodes', num_clusters = 6)"
      ],
      "metadata": {
        "id": "aFbyd_sLq1UA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To see the complete list of models available in the model library, please check the documentation or use the models function."
      ],
      "metadata": {
        "id": "HnRfXWT7q_pp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models()"
      ],
      "metadata": {
        "id": "GFZy4Rp-rC9A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Assign a Model**"
      ],
      "metadata": {
        "id": "xkmlue6DrMta"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have trained a model, we can assign the cluster labels to our training dataset (1026 samples) by using the assign_model function."
      ],
      "metadata": {
        "id": "kXkWu16brSdy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmean_results = assign_model(kmeans)\n",
        "kmean_results.head()"
      ],
      "metadata": {
        "id": "S8kkCZARrYjq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice that a new column called Cluster has been added to the original dataset.\n",
        "Note that the results also include the MouseID column that we actually dropped during the setup. Don’t worry, it is not used for the model training, rather is only appended to the dataset only when assign_model is called."
      ],
      "metadata": {
        "id": "rsnpWIdhri81"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Plot a Model**"
      ],
      "metadata": {
        "id": "4_9A4ZBarozY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plot_model function is used to analyze clustering models. This function takes a trained model object and returns a plot."
      ],
      "metadata": {
        "id": "NmFvwJTBrw4K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cluster PCA Plot**"
      ],
      "metadata": {
        "id": "Akt4BdDsrz6S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(kmeans)"
      ],
      "metadata": {
        "id": "4SV8fl5DrtzD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The cluster labels are automatically colored and shown in a legend. When you hover over the data points you will see additional features which by default use the first column of the dataset (in this case MouseID). You can change this by passing the feature parameter and you may also set label to True if you want labels to be printed on the plot."
      ],
      "metadata": {
        "id": "Wp-hSDOjsAma"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(kmeans, plot = 'cluster')\n"
      ],
      "metadata": {
        "id": "hNfRlLjhs8It"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Elbow Plot**"
      ],
      "metadata": {
        "id": "KUKg2IQRuBmr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(kmeans, plot = 'elbow')"
      ],
      "metadata": {
        "id": "BIVdUiH_sQVE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(kmeans, plot= 'elbow')\n"
      ],
      "metadata": {
        "id": "CrL9u4UpuZiF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model Evaulation**"
      ],
      "metadata": {
        "id": "OYZqAuk1tOkY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(kmeans)"
      ],
      "metadata": {
        "id": "QxsPXOMvtWIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Predict on unseen data**"
      ],
      "metadata": {
        "id": "f_8DGVPwvaEf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The predict_model function is used to assign cluster labels to a new unseen dataset. We will now use our trained kmeans model to predict the data stored in data_unseen. This variable was created at the beginning of the tutorial and contains 54 samples from the original dataset that were never exposed to PyCaret."
      ],
      "metadata": {
        "id": "en5_DHkNvegP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unseen_predictions = predict_model(kmeans, data=data_unseen)\n",
        "unseen_predictions.head()"
      ],
      "metadata": {
        "id": "_eTuUOfkvinP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Saving the model**"
      ],
      "metadata": {
        "id": "5zok3w4JwGtL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have now finished the experiment by using our kmeans model to predict labels on unseen data.\n",
        "This brings us to the end of our experiment, but one question is still to be asked: What happens when you have more new data to predict? Do you have to go through the entire experiment again? The answer is no, PyCaret’s inbuilt function save_model allows you to save the model along with the entire transformation pipeline for later use."
      ],
      "metadata": {
        "id": "IuwgcAiDwKt6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "save_model(kmeans,’Final KMeans Model 25Nov2020')"
      ],
      "metadata": {
        "id": "r-5Eewe3wPTT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "save_model(lr, 'saved_kmeans_model')"
      ],
      "metadata": {
        "id": "s0frrcRxwrZp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Method 2**"
      ],
      "metadata": {
        "id": "zOSmWAx7wX-B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.clustering import *"
      ],
      "metadata": {
        "id": "NSHT2vZty-G6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = get_clusters(Train_data, model = 'kmodes', num_clusters = 3)"
      ],
      "metadata": {
        "id": "-f8QOXrizFVy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "kvNdCcTUzXoy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Anomaly Detection**"
      ],
      "metadata": {
        "id": "7mPw1xdUZ5q5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Anomaly Detection is a machine learning technique used for identifying rare items, events, or observations by checking for rows in the table that differ significantly from the majority of the rows. Typically, the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problem or error. Some common business use cases for anomaly detection are:\n",
        "✔ Fraud detection (credit cards, insurance, etc.) using financial data.\n",
        "✔ Intrusion detection (system security, malware) or monitoring for network traffic surges and drops.\n",
        "✔ Identifying multivariate outliers in the dataset.\n",
        "[13]"
      ],
      "metadata": {
        "id": "onFY8NGpaH8g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/Datasets/Anomaly Detection/anomaly.csv\")"
      ],
      "metadata": {
        "id": "pPXT8o46alkH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Model Creation**"
      ],
      "metadata": {
        "id": "k_fwgDyZstcQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Choose the optimal model from the model library and create models for anomaly detection. We can display the list of models by using the model() function.[14]"
      ],
      "metadata": {
        "id": "VfySs8gRsx3Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.anomaly import *\n",
        "setup = setup(df, session_id = 123)"
      ],
      "metadata": {
        "id": "nxbaHfv-sTDT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Specify the session id, this results in processing after execution. It interprets numerous types of variables automatically and allows us to confirm by pressing ENTER to continue.\n",
        "\n",
        "Observe that our dataset consists of 10 features, 1000 rows each. We can perform various imputations- numeric and categorical or normalize the data. But we don’t require such transformations in our dataset so let us continue!\n",
        "\n",
        "Performing all these computations with a few lines of code presents the beauty of the PyCaret library[14]"
      ],
      "metadata": {
        "id": "ZOIsZIRcsfvq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models()"
      ],
      "metadata": {
        "id": "JpcF70ljs5iR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install scikit-learn==0.23.2"
      ],
      "metadata": {
        "id": "CH6ui8UebHMH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.anomaly import *\n",
        "dataset = get_outliers(data = data)"
      ],
      "metadata": {
        "id": "175xmGqSa4V3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "tIYDC1pMbrsE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Two new columns are attached to the original table. Label (1 = outlier, 0 = inlier) and Score (data points with high scores are categorized as outlier)."
      ],
      "metadata": {
        "id": "DLMGPIbBb9D8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "By default, PyCaret trains a K-Nearest Neighbors Anomaly Detector with 5% fraction (i.e. 5% of the total number of rows in the table will be flagged as outlier). Default values can be changed easily:\n",
        "\n",
        "To change the fraction value you can use fraction parameter within get_outliers( ) function.\n",
        "To change model type use model parameter within get_outliers( ).\n",
        "See the following code for training an Isolation Forest model with 0.1 fraction:[13]"
      ],
      "metadata": {
        "id": "cWHGemcKcLli"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.anomaly import *\n",
        "dataset = get_outliers(dataset, model = 'iforest', fraction = 0.1)"
      ],
      "metadata": {
        "id": "tmdQ539fcVLz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "E9iaXbxqcYhs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are over 10 ready-to-use anomaly detection algorithms in PyCaret:"
      ],
      "metadata": {
        "id": "FyWrwFjZcpbE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=11lZ2xTuK4X7u5NlMxQ23hZgTo53pDZ1D)"
      ],
      "metadata": {
        "id": "5LGdIYxDn5Lk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "All the preprocessing tasks necessary to train an anomaly detection model such as missing value imputation (if table has any missing or null values), or normalization, or one-hot-encoding, they all are automatically performed before training an anomaly detection model. Click here to learn more about PyCaret’s preprocessing capabilities.\n",
        "\n",
        "💡 In this example we have used the **get_outliers( )** function to assign outlier label and score for analysis. Every time the query is refreshed, outliers are recalculated. An alternate way to implement this would be to use the predict_model( ) function to predict outliers using a pre-trained model in Python [13]"
      ],
      "metadata": {
        "id": "LJdK2lQdc52L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Isolation Forest**"
      ],
      "metadata": {
        "id": "nputVV5uuDc1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create the isolation Forest model by using the create_model() function. The isolation Forest algorithm differentiates observations by randomly selecting a feature and then randomly selecting split values between the maximum and minimum values.[14]"
      ],
      "metadata": {
        "id": "-euFWPe0uIFm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iforest = create_model('iforest')\n",
        "print(iforest)"
      ],
      "metadata": {
        "id": "2kUAATYQuSKe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thus an anomaly score is determined as the number of conditions required to separate given observations.\n",
        "\n"
      ],
      "metadata": {
        "id": "Dim2QHE8ueFn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Local Outlier Factor**\n"
      ],
      "metadata": {
        "id": "Jq_OmS0Ruktc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is an algorithm of the unsupervised anomaly detection method and computes the local density deviation of a data point with respect to its neighbor"
      ],
      "metadata": {
        "id": "Ezb8n0l1upt9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lof = create_model('lof')\n",
        "print(lof)"
      ],
      "metadata": {
        "id": "3mSZZ1BiuuhV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **K Nearest Neighbors**\n"
      ],
      "metadata": {
        "id": "b2jJtdaSvKF1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "KNN is a non-parametric lazy learning algorithm used to classify data based on similarities and various distance metrics. It provides a simple yet firm approach to detecting anomalies"
      ],
      "metadata": {
        "id": "WdILErhevO19"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = create_model('knn')\n"
      ],
      "metadata": {
        "id": "pzFpCCthvU4w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Comparing anomalies in models**"
      ],
      "metadata": {
        "id": "P0zLF7Plvb3j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Moving on with our task we can now observe the anomalies determined by the models. Traditionally, we have to manually set up different parameters.\n",
        "\n",
        "But with the use of PyCaret, we can just assign results via the assigned model function. We’ll start with the isolation forest model.[14]"
      ],
      "metadata": {
        "id": "Imwlvluhvgz8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iforest_results = assign_model(iforest)\n",
        "iforest_results.head()"
      ],
      "metadata": {
        "id": "3k-gQQOnvtrF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "assign_model() function returns a data frame with detection of anomalies, the presence of outliers are marked as 1 and non-outliers as 0, along with anomaly scores."
      ],
      "metadata": {
        "id": "WVM1-1Sjv56N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Filter the anomalies by each model, this shows that there are 50 rows out of 1000 considered as anomalies by iforest model."
      ],
      "metadata": {
        "id": "2OCdhiY0wJ0k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iforest_anomaly=iforest_results[iforest_results['Anomaly']==1]\n",
        "iforest_anomaly.shape"
      ],
      "metadata": {
        "id": "zoSDoyVswNlT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lof_anomaly=lof_results[lof_results['Anomaly']==1]\n",
        "lof_anomaly.shape"
      ],
      "metadata": {
        "id": "Kr-gF8zkxoyD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_anomaly=knn_results[knn_results['Anomaly']==1]\n",
        "knn_anomaly.shape"
      ],
      "metadata": {
        "id": "t3TwBSW9xt4j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Interpretation and visualization**\n"
      ],
      "metadata": {
        "id": "ghn94YjKx3Ph"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualization is the most convenient way to interpret the information at hand in a creative and independent manner.\n",
        "\n",
        "Let us start by creating visuals from outside the PyCaret library which will highlight the benefits of the PyCaret library and enable us to understand how the plot_model function is much more interactive.[14]"
      ],
      "metadata": {
        "id": "BmEaF8rtx-YW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from yellowbrick.features import Manifold\n",
        "dfr = iforest_results['Anomaly']\n",
        "viz = Manifold(manifold=\"tsne\")\n",
        "viz.fit_transform(df, dfr)\n",
        "viz.show()"
      ],
      "metadata": {
        "id": "exkK6bTCyD56"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see most of the anomalies determined by the isolation forest in multiple dimensions are usually out of different clusters.\n",
        "\n",
        "Now use the plot_model() function for KNN within PyCaret that will create a 3D plot for outliers, in which we can see why certain features are considered as an anomaly."
      ],
      "metadata": {
        "id": "_7ICl522yKQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(knn)"
      ],
      "metadata": {
        "id": "RkPucs0CyOV6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can move it in any dimension to see and point out the anomalies. This 3D plot helps us to view it better. The KNN plot shows that most of the outliers are those which were not part of any clusters. So that’s good to go!\n",
        "\n",
        "The same can be done for the other two models."
      ],
      "metadata": {
        "id": "VmKNwUTxya6k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(iforest)"
      ],
      "metadata": {
        "id": "b53dVh2uymKv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is clearly visible that the data set is divided into four different clusters, So anything out of these groups will surely be an anomaly.\n",
        "\n",
        "Anomalies are not always a bad sign! Sometimes they can be very useful in interpreting results or data analysis. These can be used to solve distinct data science usecases.\n",
        "\n",
        "Moving on to the third model ie. linear outlier factor we can experiment with a different plot that creates a 2D plot."
      ],
      "metadata": {
        "id": "063hjjNmysOI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can zoom this 2D plot to view which points were considered as outliers.\n",
        "\n",
        "Another visual can be created for pair plot again, now with the anomalies\n",
        "to see which points will count as anomalies."
      ],
      "metadata": {
        "id": "7h0PGRc5ywA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(lof_results, hue = \"Anomaly\")"
      ],
      "metadata": {
        "id": "ORFwrVlry1P5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "save_model(iforest,'IForest_Model')"
      ],
      "metadata": {
        "id": "raH2iht-y6Q6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Natural Language Processing**"
      ],
      "metadata": {
        "id": "aIZ9Fd06kn-n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Several techniques are used to analyze text data among which Topic Modeling is a popular one. A topic model is a type of statistical model for discovering the abstract topics in a collection of documents. Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text data[13]"
      ],
      "metadata": {
        "id": "bPcGou14lBzQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "dataset = pd.read_csv(\"/content/drive/MyDrive/Datasets/NLP/kiva.csv\")"
      ],
      "metadata": {
        "id": "cci_zG_RlKO1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.nlp import *\n",
        "dataset = get_topics(data = dataset, text = 'en')"
      ],
      "metadata": {
        "id": "C1FIrGvvlZ5b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "JI2oANfmmKrU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "‘en’ is the name of the column containing text in the table ‘kiva’."
      ],
      "metadata": {
        "id": "bWNPFF6blk6r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once the code is executed, new columns with weight of topics and dominant topic are attached to the original table. There are many ways to visualize the output of Topic Mod"
      ],
      "metadata": {
        "id": "9oNhK9SzlvFg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "By default, PyCaret trains a Latent Dirichlet Allocation model with 4 topics. Default values can be changed easily:\n",
        "\n",
        "To change the number of topics you can use the num_topics parameter within get_topics( ) function.\n",
        "To change model type use the model parameter within the get_topics( ).\n",
        "See the example code for training a Non-Negative Matrix Factorization Model with 10 topics:[13]"
      ],
      "metadata": {
        "id": "gxhTa3N6mB19"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.nlp import *\n",
        "dataset = get_topics(dataset, 'en', model = 'nmf', num_topics = 10)"
      ],
      "metadata": {
        "id": "52kZtJwTmBBt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "96kDmtBom2wU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "PyCaret has following ready-to-use algorithms for topic modeling:\n",
        "\n"
      ],
      "metadata": {
        "id": "Eo_TFKLhma1q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1zKKxIIy1O8ZGDeg01ZuEitf8KRXNTag4)"
      ],
      "metadata": {
        "id": "MDV9kcX4nZOQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Association Rule Mining**"
      ],
      "metadata": {
        "id": "psATXe5xoLNa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Association Rule Mining is a rule-based machine learning technique for discovering interesting relations between variables in a database. It is intended to identify strong rules using measures of interestingness. Some common business use cases for association rule mining are:\n",
        "\n",
        "✔ Market Basket Analysis to understand items frequently bought together.\n",
        "\n",
        "✔ Medical Diagnosis to assist physicians in determining occurrence probability of illness given factors and symptoms.\n",
        "[13]"
      ],
      "metadata": {
        "id": "07e_74u1oSyK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "dataset = pd.read_csv(\"/content/drive/MyDrive/Datasets/Association Rule Mining/france.csv\")"
      ],
      "metadata": {
        "id": "JDmMr3kmotbe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Apriori Algorithm**"
      ],
      "metadata": {
        "id": "KxY5UmxQpSwl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.arules import *\n",
        "dataset = get_rules(dataset, transaction_id = 'InvoiceNo', item_id = 'Description')"
      ],
      "metadata": {
        "id": "h5cPWG5tpgVU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "yyJH4AI7pjQD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Time Series**"
      ],
      "metadata": {
        "id": "8-LZFroEyc2l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Dataset**"
      ],
      "metadata": {
        "id": "EAXf6jvMymAJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "!pip install pycaret-ts-alpha"
      ],
      "metadata": {
        "id": "e9yDbquo0niD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pprint import pprint\n",
        "from pycaret.internal.pycaret_experiment import TimeSeriesExperiment\n",
        "from sktime.utils.plotting import plot_series"
      ],
      "metadata": {
        "id": "4mI7D_Mu1FZO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.datasets import get_data"
      ],
      "metadata": {
        "id": "QR45Af9Iyq-Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_datasets = get_data()"
      ],
      "metadata": {
        "id": "XdHYKmnty_jg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.datasets import get_data\n",
        "data=get_data('airline')\n"
      ],
      "metadata": {
        "id": "Qi-D8uj4yxuy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMe-AMlnitQ8"
      },
      "source": [
        "# **AutoML-Gs**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wlijuc-di5QR"
      },
      "source": [
        "**Installing required libraries**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwpdRorQi8WK"
      },
      "source": [
        "!pip install automl-gs\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJJzKmNFjGTX"
      },
      "source": [
        "**Importing required libraries**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZCsXFHljIkj"
      },
      "source": [
        "import automl_gs\n",
        "from automl_gs import automl_grid_search\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MvMRJnLQjSTg"
      },
      "source": [
        "**Creating the Model**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FfzVTAMRjeOn"
      },
      "source": [
        "automl_grid_search(MiceImputed, 'RESULT_TEXT')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oz8BPvW3P8Hx"
      },
      "source": [
        "# **MLflow**[8]\n",
        "\n",
        "MLflow provides a convenient way to build end-to-end Machine Learning pipelines in production and in this guide\n",
        "\n",
        "MLflow is an open-source platform for managing the end-to-end machine learning lifecycle or pipeline. It supports multiple Machine Learning libraries, algorithms, deployment tools, and programming languages."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8cWDBKtQ-ep"
      },
      "source": [
        "The platform was created by Databricks has over 10,000 stars on GitHub with over 300+ contributors updating the platform on a daily basis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QC66c-SRDqi"
      },
      "source": [
        "The MLflow platform provides four major components:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q15NbkkNRFYi"
      },
      "source": [
        "- **MLflow Tracking** — This component is perfect for users looking to select the right hyper-parameters for their model as well as for recording model performance over time. You can record and query model experiments which include your code, data, config, and results.\n",
        "- **MLflow Projects** — This component is mostly used when trying to reproduce your Machine Learning project on a different machine (similar to Docker images and containers). You can package your data science code in a given format to reproduce model runs on any platform.\n",
        "- **MLflow Models** — This component is for you if you are looking to deploy Machine Learning models in the real world. With this component, you can deploy machine learning models in diverse serving environments.\n",
        "- **MLflow Model Registry**— This component is great for you if you are looking to manage multiple Machine Learning models. Using this component, you can store, annotate, discover, and manage Machine Learning models in a central repository."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXa4xmVSSGsv"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1BoZqaCplUEEJ8fDpk3k167WGi_GmJI_f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJYWowyzSSXf"
      },
      "source": [
        "- Individual Data Scientists can use MLflow to track experiments locally, organize code in projects for future reuse, and deploy models using MLflow’s deployment tools.\n",
        "- Large Organizations can share projects, models, and results with any teams using MLflow.\n",
        "- Data Science Teams can log metrics and compare results across multiple users working on the same problem\n",
        "- Production Engineers can deploy models from diverse ML libraries, store the models as files in a management system, and track which run a model came from."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEVSTGhmSg8r"
      },
      "source": [
        "Based on these components, MLflow is designed to be useful for an individual to a large range of people working as a team. Some of its applications are as follows:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzmmT8ZKStGt"
      },
      "source": [
        "If you’re still undecided about learning how to use the platform, you can go over the MLflow components again and figure out if the platform is for you or not."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NllAK6hmUAUH"
      },
      "source": [
        "**Installing MLflow to use with Python**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKriiariUFCc"
      },
      "source": [
        "**Using with Python [4]**\n",
        "\n",
        "Remember we passed log_experiment = True in the setup function along with experiment_name = 'diamond' . Let’s see the magic PyCaret has done with the help of MLflow behind the scene. To see the magic let’s initiate the MLflow server:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jiwt9i9LUXCH"
      },
      "source": [
        "# within notebook (notice ! sign infront)\n",
        "!mlflow ui\n",
        "# on command line in the same folder\n",
        "#mlflow ui"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvzazsunYIof"
      },
      "source": [
        "!mlflow ui --host 0.0.0.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSyTRCPjUahe"
      },
      "source": [
        "Now open your browser and type “localhost:5000”. It will open a UI like this:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rofMGBlUuWl"
      },
      "source": [
        "Remember that MLflow supports multiple programming languages and tools such as R-programming language or Python. It also comes with a graphical user interface that you can access from your browser once you successfully install MLflow.[8]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaMK4Ca5U06l"
      },
      "source": [
        "To keep this guide concise and easy to digest, we’ll show you how you can install MLflow to use with Python. To install MLflow, open up your command line/terminal and write the following command:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMQfIK-RU2hl"
      },
      "source": [
        "!pip install mlflow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5ScuZwPVIAO"
      },
      "source": [
        "Note: You must have Python installed in your system to use pip which is Python’s package manager."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWq_TXcoVMrF"
      },
      "source": [
        "Once you execute the command, MLflow will get installed in your system. You can check if the installation is successful or not by importing MLflow in Python using the following line of code:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcgo8jZiVQ_l"
      },
      "source": [
        "import mlflow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ml198OF5Vbju"
      },
      "source": [
        "If this line of Python code doesn’t give you an error, then, you’ve successfully installed MLflow to use with Python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "utD3ulx-VfVc"
      },
      "source": [
        "**Component 1: MLflow Tracking**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdrXTWuAZbtV"
      },
      "source": [
        "# Importing the os library to work with operating system functionalities\n",
        "import os\n",
        "\n",
        "# Importing tracking functions from MLflow\n",
        "from mlflow import log_metric, log_param, log_artifacts\n",
        "\n",
        "# Logging a parameter (key-value pair)\n",
        "log_param(\"param1\", 0)\n",
        "\n",
        "# Logging a metric; metrics can be updated throughout the run\n",
        "log_metric(\"foo\", 100)\n",
        "log_metric(\"foo\", 200)\n",
        "log_metric(\"foo\", 300)\n",
        "\n",
        "# Create a file called test.text in outputs directory\n",
        "if not os.path.exists(\"outputs\"):\n",
        "    os.makedirs(\"outputs\")\n",
        "with open(\"outputs/test.txt\", \"w\") as f:\n",
        "    f.write(\"hello world!\")\n",
        "\n",
        "# Logging an artifact (output file)\n",
        "log_artifacts(\"outputs\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZBaq1dGZgFZ"
      },
      "source": [
        "!mlflow ui"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **AutoML**[9]"
      ],
      "metadata": {
        "id": "oLv0-wNq1s7Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An AutoML pipeline includes data preprocessing, feature engineering, feature selection, model training, hyperparameter tuning, and algorithm selection"
      ],
      "metadata": {
        "id": "M47_UJN44vVW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "What if there was an open-source Python package that could do all of the above and automatically determine the best model? A package that would explain to you exactly how each ML model is built & also provide you with a detailed report!\n",
        "The answer to the question is the Python package ‘mjlar supervised’. It was developed by MJLAR with the goal of helping software developers and data analysts that do not have classical ML training."
      ],
      "metadata": {
        "id": "IH1AGUwd43_f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The package ‘mjlar supervised’allows you to select machine learning algorithms, preprocess dataset, train & tune the model, and finally explain and evaluate the model."
      ],
      "metadata": {
        "id": "tC1OBZWT5Azz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The idea behind AutoML is to continuously build and test machine learning models using a neural network. As a new set of data is added, the neural network will automatically determine whether it needs to create a new specialized model by training and executing its own algorithm or simply improve an existing one. Essentially, AutoML creates sophisticated machine learning programs that can be accessed via APIs where the output is returned in the form of API calls.\n",
        "AutoML is clearly a very powerful tool for automating much of this process using machine learning algorithms. But you’re still going to need programmers to define heuristics for machines or identify how they should make decisions and also learn about various data science concepts.[10]"
      ],
      "metadata": {
        "id": "vBmvXFF2AV5d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Why AutoML?**"
      ],
      "metadata": {
        "id": "RBeDpR9IApqA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "AutoML would continuously build models based on new data sets provided by an enterprise and improve them when better ones are discovered by the AutoML algorithm. The idea of applying neural networks to this process is not new — it’s been around for a long time and several companies have experimented with it in their own ways. AutoML has tremendous implications for non-technical people who want to leverage insights from data, but don’t have the programming chops or the resources to implement ML solutions. All they need now is an API call where they can get data back in the form of “recommendations”, such as which products are ideal for each customer based on his purchasing history, demographics, and other aspects. The other possibility is that they will get an alert if a certain event happens and the solution to prevent it.[10]"
      ],
      "metadata": {
        "id": "Di9HmVU4AsyD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What Does AutoML Do?**[10]\n"
      ],
      "metadata": {
        "id": "t_TDvqdSA0CC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "AutoML can be used for solving three primary problems: Model Building, Hyperparameter Tuning & Selection, and Model Optimization.\n",
        "Model Building — This refers to setting up the model based on parameters in your data set (such as predicting the probability of customers churning) so that machine learning algorithms can compare them with labels (actual instances) and then feedback their “correctness” or explain how inaccurate they are. Once modeled, results can be analyzed using tools like TensorBoard. The process of refining these models is known as Hyperparameter tuning & selection — this refers to setting up the appropriate parameters in a model so that it learns data efficiently. When you’re using AutoML, this is done by providing an algorithm with lots of examples and letting it come up with its own metrics for measuring what makes sense. For example, if you want a model that recognizes images — you’ll need to provide it with hundreds of thousands of images so it can identify patterns between them. Model Optimization refers to improving your existing models after training them on large datasets provided by an enterprise; AutoML helps optimize your models based on computation speed and accuracy.\n",
        "AutoML has two powerful components — Abalone and Ranker (which have been open-sourced).\n",
        "Abalone: This component takes care of creating a simple model of your data set, by using AutoML’s cloud platform that uses Tensorflow. It also helps you to build more advanced models with the help of human experts in machine learning.\n",
        "Ranker: This component recommends which models (created via Abalone) are the best for a particular enterprise based on their usage requirements and results."
      ],
      "metadata": {
        "id": "_7rgwwFsA5OJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Algorithm Selection**[9]"
      ],
      "metadata": {
        "id": "gxnNxyQx-Ai-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This package allows the user to check their model with various algorithms: Baseline, Linear, Random Forest, Extra Trees, LightGBM, Xgboost, CatBoost, Neural Networks, Ensemble, and Nearest Neighbors. Once you run the code, the output will show you how much time each model took to train the dataset and the root mean squared error of the trained model. The package will select the algorithm with the lowest root mean squared error."
      ],
      "metadata": {
        "id": "m8JpFjNa-Cvt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Available Modes**[9]\n",
        "\n",
        "The available modes allow the user to explain and optimize the ML model according to the audience. It has four modes — Explain, Perform, Compete and Optuna."
      ],
      "metadata": {
        "id": "wvIqOBI8-IeV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "automl = AutoML(mode=”Explain”)"
      ],
      "metadata": {
        "id": "hhFo7stm-aJz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Explain mode allows the user to explain the model including making learning curves, feature plots, and SHAP plots."
      ],
      "metadata": {
        "id": "yovifnr4-VuZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "automl = AutoML(mode=”Perform”)\n"
      ],
      "metadata": {
        "id": "bCVROikv-cXX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Perform mode allows the user to train a model that will be used in real-life use cases."
      ],
      "metadata": {
        "id": "2TNinMbH-luU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "automl = AutoML(mode=”Compete”)"
      ],
      "metadata": {
        "id": "LIwp9nIl-syk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Compete mode will allow the user to adapt a validation strategy depending on the size of the dataset and can be used for entering into machine learning competitions."
      ],
      "metadata": {
        "id": "093rG7oN-wmn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "automl = AutoML(mode=”Optuna”)\n"
      ],
      "metadata": {
        "id": "_VtvlDJr--C6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Optuna mode should be used when the priority is the performance of the model instead of time."
      ],
      "metadata": {
        "id": "TAw42Tzz_EgM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mljar-supervised\n"
      ],
      "metadata": {
        "id": "77kJpRoK2mp5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=Train_data\n"
      ],
      "metadata": {
        "id": "gOXDgk4F59Ue"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['gender'] = data['gender'].fillna(data['gender'].mode()[0])\n",
        "data['ssc_b'] = data['ssc_b'].fillna(data['ssc_b'].mode()[0])\n",
        "data['hsc_b'] = data['hsc_b'].fillna(data['hsc_b'].mode()[0])\n",
        "data['hsc_s'] = data['hsc_s'].fillna(data['hsc_s'].mode()[0])\n",
        "data['degree_t'] = data['degree_t'].fillna(data['degree_t'].mode()[0])\n",
        "data['workex'] = data['workex'].fillna(data['workex'].mode()[0])\n",
        "data['specialisation'] = data['specialisation'].fillna(data['specialisation'].mode()[0])\n",
        "data['status'] = data['status'].fillna(data['status'].mode()[0])"
      ],
      "metadata": {
        "id": "tg_SRR6z4aH-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical features to continuous features with Label Encoding\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "lencoders = {}\n",
        "for col in data.select_dtypes(include=['object']).columns:\n",
        "    lencoders[col] = LabelEncoder()\n",
        "    data[col] = lencoders[col].fit_transform(data[col])"
      ],
      "metadata": {
        "id": "s4AiZ8KF6HC0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=data.drop('specialisation',axis=1)\n"
      ],
      "metadata": {
        "id": "nxHrMYRg6Nmy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=data[['specialisation']]\n"
      ],
      "metadata": {
        "id": "sN-xP3Mq6TRT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=100)"
      ],
      "metadata": {
        "id": "SkGM-w5V6dPK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall scikit-learn -y\n",
        "!pip install scikit-learn==0.24"
      ],
      "metadata": {
        "id": "w94F0cMx77VT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from supervised.automl import AutoML\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "metadata": {
        "id": "lmXH4S1k288e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "automl = AutoML()\n",
        "automl.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "Xs0g9q4o8Mv5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = automl.predict_all(X_test)\n",
        "print(predictions.head())\n",
        "print(\"Test accuracy:\", accuracy_score(y_test, predictions[\"label\"].astype(int)))"
      ],
      "metadata": {
        "id": "NhhRUuYY80Da"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "automl = AutoML(mode=\"Perform\")\n",
        "automl.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "fB-AVcvW9KcW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compute the accuracy on test data\n",
        "predictions = automl.predict_all(X_test)\n",
        "print(predictions.head())\n",
        "print(\"Test accuracy:\", accuracy_score(y_test, predictions[\"label\"].astype(int)))"
      ],
      "metadata": {
        "id": "l9Ypog-w9kap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install autokeras\n"
      ],
      "metadata": {
        "id": "KKokCjLTDINa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from autokeras import StructuredDataClassifier"
      ],
      "metadata": {
        "id": "CbsbsjnEFRvc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "search = StructuredDataClassifier(max_trials = 20)\n"
      ],
      "metadata": {
        "id": "Tgx_0ex2FzPO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "search.fit(x=X_train, y=y_train)\n"
      ],
      "metadata": {
        "id": "iJg2GSchGAeM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = search.evaluate(X_train, y_train, verbose=0)\n",
        "print('Accuracy is {}'.format(acc))"
      ],
      "metadata": {
        "id": "cEFLDUE6GNxD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = search.evaluate(X_test, y_test, verbose=0)\n",
        "print('Accuracy is {}'.format(acc))"
      ],
      "metadata": {
        "id": "otLKcCn-GSsD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MLBox**"
      ],
      "metadata": {
        "id": "EKVHEHumRF45"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a Machine Learning model is not a difficult task because Python provides ample libraries which can help in creating models related to problems like Regression, Classification, etc. Python packages like Sklearn Statsmodel can be used for creating these models but the difficult part is optimizing and generalizing these models so that they work on unseen data also **[16]**.In other words, creating a Machine Learning model alone does not solve the problem, we should also be able to tune the hyperparameter of these models to make it generalized and achieve higher performance and accuracy. There are a large number of Machine Learning models from which we can select a model which works best for our dataset, but it is a time-consuming process because we need to write the code and train the model for every algorithm. Similarly, if we are able to select a model then we need to select the best hyperparameter of the model which again is a time-consuming process **[16]**."
      ],
      "metadata": {
        "id": "c0MfiFTaRLJx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, in order to bring down the efforts and time taken in these processes, we can automate it using **MLBox**. It is an open-source python library that is used for automating the Machine Learning process which includes feature selection, Hyperparameter Optimization, Creating the model, and generating predictions using that model **[16]**"
      ],
      "metadata": {
        "id": "ggpqtGv6Rrn3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Installing required libraries**\n"
      ],
      "metadata": {
        "id": "iRYpzbGBRzgI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mlbox"
      ],
      "metadata": {
        "id": "fVNvxwbuR3BQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing required libraries**"
      ],
      "metadata": {
        "id": "9LBjYBw4SEkp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mlbox.preprocessing import *\n",
        "from mlbox.optimisation import *\n",
        "from mlbox.prediction import*"
      ],
      "metadata": {
        "id": "JmirXZ3ESOrI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading dataset**"
      ],
      "metadata": {
        "id": "YNk_ozY4TBMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "Train_data = pd.read_csv(\"/content/drive/MyDrive/Datasets/Student field Recommendation /Placement_Data_Full_Class.csv\")"
      ],
      "metadata": {
        "id": "-_CUwS-UUPPu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "paths = [\"/content/drive/MyDrive/Datasets/Student field Recommendation /Train.csv\",\"/content/drive/MyDrive/Datasets/Student field Recommendation /Test.csv\"]\n",
        "target_name = \"specialisation\""
      ],
      "metadata": {
        "id": "90ZZFwwyTIMz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rd = Reader(sep = \",\")\n",
        "df = rd.train_test_split(paths, target_name)"
      ],
      "metadata": {
        "id": "SUJdmgUoTML1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating the Models**"
      ],
      "metadata": {
        "id": "SFmiEq4rdGNJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Defining Optimiser\n",
        "opt = Optimiser(scoring = \"accuracy\", n_folds = 5)\n",
        "#Defining the model\n",
        "space = {\n",
        "'est__strategy':{\"search\":\"choice\",\n",
        "\"space\":[\"LightGBM\"]},\n",
        "'est__n_estimators':{\"search\":\"choice\",\n",
        "\"space\":[150]},\n",
        "'est__colsample_bytree':{\"search\":\"uniform\",\n",
        "\"space\":[0.8,0.95]},\n",
        "'est__subsample':{\"search\":\"uniform\",\n",
        "\"space\":[0.8,0.95]},\n",
        "'est__max_depth':{\"search\":\"choice\",\n",
        "\"space\":[5,6,7,8,9]},\n",
        "'est__learning_rate':{\"search\":\"choice\",\n",
        "\"space\":[0.07]}\n",
        "}\n",
        "params = opt.optimise(space, df,15)"
      ],
      "metadata": {
        "id": "yrCeMPNocERL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Importance**\n"
      ],
      "metadata": {
        "id": "82u9Ugi3dNt_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prd = Predictor()\n",
        "prd.fit_predict(params, df)"
      ],
      "metadata": {
        "id": "nS4Fh-YGcMfx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CF7dADqbc8c4"
      },
      "source": [
        "# **References**\n",
        "\n",
        "[1-Build a machine learning model with PyCaret and corresponding user interface with Gradio](https://medium.com/nerd-for-tech/build-a-machine-learning-model-with-pycaret-and-corresponding-user-interface-with-gradio-57ff09b7d262)\n",
        "\n",
        "[2-PyCaret in Machine Learning](https://thecleverprogrammer.com/2021/03/07/pycaret-in-machine-learning/)\n",
        "\n",
        "[3-PyCaret 101: An introduction for beginners](https://www.kdnuggets.com/2021/06/pycaret-101-introduction-beginners.html?fbclid=IwAR29t4C5QplspexoIyAd4eYfUjlZSOFL5wVmxc9sU5qXr_mMqIPfaJVmP6Y)\n",
        "\n",
        "[4-Easy MLOps with PyCaret + MLflow](https://towardsdatascience.com/easy-mlops-with-pycaret-mlflow-7fbcbf1e38c6)\n",
        "\n",
        "[5-Binary Classification Tutorial Level Beginner](https://colab.research.google.com/drive/1GqQ3XAIzg4krBbnOpKyeRqT0qBQhdwYL#scrollTo=GM-nQ7LqEQma)\n",
        "\n",
        "[6-Create Clusters](https://pycaret.org/create-clusters/)\n",
        "\n",
        "[7-Plot Model](https://pycaret.org/plot-model/)\n",
        "\n",
        "[8-MLflow For Machine Learning Pipelines [Ultimate Guide]](https://medium.com/@theclickreader/mlflow-for-machine-learning-pipelines-ultimate-guide-821e55370034)\n",
        "\n",
        "[9-Gentle Introduction to New AutoML Package by MJLAR!](https://medium.com/mlearning-ai/gentle-introduction-to-new-automl-package-by-mjlar-bc51a99ba53c)\n",
        "\n",
        "[10-AutoML: The future of Data Science and Machine Learning](https://protonautoml.medium.com/automl-the-future-of-data-science-and-machine-learning-45abb8f5ebcf)\n",
        "\n",
        "[11- Predicting Machine Maintenance using Machine Learning](https://medium.com/analytics-vidhya/predicting-machine-maintenance-using-machine-learning-ee694c7fa0f0)\n",
        "\n",
        "\n",
        "[12- 7 Top AutoML Tools](https://victorzhou.com/posts/tools-for-auto-ml/)\n",
        "\n",
        "[13-Machine Learning in Power BI using PyCaret](https://www.kdnuggets.com/2020/05/machine-learning-power-bi-pycaret.html)\n",
        "\n",
        "[14- Getting familiar with PyCaret for anomaly detection](https://www.analyticsvidhya.com/blog/2021/05/getting-familiar-with-pycaret-for-anomaly-detection/)\n",
        "\n",
        "[15- Introduction to Clustering in Python with PyCaret](https://towardsdatascience.com/introduction-to-clustering-in-python-with-pycaret-5d869b9714a3)\n",
        "\n",
        "[16-Automating Machine Learning Modelling](https://towardsdatascience.com/automating-machine-learning-modelling-62aac2081e3f)\n",
        "\n",
        "\n",
        "[Introduction to Anomaly Detection in Python with PyCaret](https://towardsdatascience.com/introduction-to-anomaly-detection-in-python-with-pycaret-2fecd7144f87)\n",
        "\n",
        "[How to Use pyGAM to Fit More Flexible Functions to Your Data](https://tech-at-kraftheinz.medium.com/how-to-use-pygam-to-fit-more-flexible-functions-to-your-data-5de16e7d913a)\n",
        "\n",
        "[Automating Machine Learning Modelling](https://towardsdatascience.com/automating-machine-learning-modelling-62aac2081e3f)\n",
        "\n",
        "[What Are Baseline Models and Benchmarking For Machine Learning, Why We Need Them? Part 1 Classification](https://pub.towardsai.net/what-are-baseline-models-and-benchmarking-for-machine-learning-why-we-need-them-affe0714cd07)\n",
        "\n",
        "\n",
        "[Time Series](https://pycaret.readthedocs.io/en/time_series/api/time_series.html)\n"
      ]
    }
  ]
}