Aurora_RoundHacks.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Aurora RoundHacks.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNNLQb3KS3XjawPeqcWnxzs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "dc7a5f4d5d944b079566adf27d05cc2e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_91c3bfc034e64767840fb865f34f1ec1",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 3,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 3,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_11fb4bf94538457a8034b9a494b8dd88"
          }
        },
        "91c3bfc034e64767840fb865f34f1ec1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "11fb4bf94538457a8034b9a494b8dd88": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "8c0cb088fc91473d9a698bc547517ebd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "TextModel",
          "state": {
            "_view_name": "TextView",
            "style": "IPY_MODEL_a09888c55f5240fd9deab188fda56900",
            "_dom_classes": [],
            "description": "",
            "_model_name": "TextModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": "Following data types have been inferred automatically, if they are correct press enter to continue or type 'quit' otherwise.",
            "_view_count": null,
            "disabled": false,
            "_view_module_version": "1.5.0",
            "continuous_update": true,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_cedfab03514a4e92b4517ad5b421be3b"
          }
        },
        "a09888c55f5240fd9deab188fda56900": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "cedfab03514a4e92b4517ad5b421be3b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": "100%",
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "f93eb2f73c314726a2b21a6fa892ab5e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_bf4f7a8eb19f4205a52d34184eb90871",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 4,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 4,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_ac80a82af6734672b9e975b9502dfff4"
          }
        },
        "bf4f7a8eb19f4205a52d34184eb90871": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "ac80a82af6734672b9e975b9502dfff4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "fee804a92d954e43b0b16158c3ea439f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_16509114a92c4dea8160eb875d3d6ee1",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 7,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 7,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_c78de6be33094defa6626e6eb8c52ba7"
          }
        },
        "16509114a92c4dea8160eb875d3d6ee1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "c78de6be33094defa6626e6eb8c52ba7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "aa0d6f72b63d4962a706732741b3c0ab": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_9acc9d820e1a4b929fd731ae9213c2e1",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 4,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 4,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_880403c4ecd34c43ba2a3eb1996bae8a"
          }
        },
        "9acc9d820e1a4b929fd731ae9213c2e1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "880403c4ecd34c43ba2a3eb1996bae8a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "4d4314810c104f57ba9ae572d84b5e5d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_a345352930704b5294531f082f1ad378",
            "_dom_classes": [],
            "description": "Processing: ",
            "_model_name": "IntProgressModel",
            "bar_style": "",
            "max": 7,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 7,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_0370e713d28b43dc90339d137ab9d2fd"
          }
        },
        "a345352930704b5294531f082f1ad378": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "0370e713d28b43dc90339d137ab9d2fd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpanSM/Machine_Learning_Hackathons/blob/master/Aurora_RoundHacks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NDHrtVV5mbGx",
        "outputId": "a86cda9f-bc41-4f2e-8f50-b568fafad5f0"
      },
      "source": [
        "!wget https://dphi-courses.s3.ap-south-1.amazonaws.com/data-science-challenges/DTU_RoundTable/new_train.csv\n",
        "!wget https://dphi-courses.s3.ap-south-1.amazonaws.com/data-science-challenges/DTU_RoundTable/new_test.csv\n",
        "!wget https://dphi-courses.s3.ap-south-1.amazonaws.com/data-science-challenges/DTU_RoundTable/sample_submission.csv\n",
        "!pip install pycaret[full]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-07 12:01:11--  https://dphi-courses.s3.ap-south-1.amazonaws.com/data-science-challenges/DTU_RoundTable/new_train.csv\n",
            "Resolving dphi-courses.s3.ap-south-1.amazonaws.com (dphi-courses.s3.ap-south-1.amazonaws.com)... 52.219.66.127\n",
            "Connecting to dphi-courses.s3.ap-south-1.amazonaws.com (dphi-courses.s3.ap-south-1.amazonaws.com)|52.219.66.127|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 199060 (194K) [text/csv]\n",
            "Saving to: ‘new_train.csv.1’\n",
            "\n",
            "new_train.csv.1     100%[===================>] 194.39K   272KB/s    in 0.7s    \n",
            "\n",
            "2021-03-07 12:01:13 (272 KB/s) - ‘new_train.csv.1’ saved [199060/199060]\n",
            "\n",
            "--2021-03-07 12:01:13--  https://dphi-courses.s3.ap-south-1.amazonaws.com/data-science-challenges/DTU_RoundTable/new_test.csv\n",
            "Resolving dphi-courses.s3.ap-south-1.amazonaws.com (dphi-courses.s3.ap-south-1.amazonaws.com)... 52.219.66.127\n",
            "Connecting to dphi-courses.s3.ap-south-1.amazonaws.com (dphi-courses.s3.ap-south-1.amazonaws.com)|52.219.66.127|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 42738 (42K) [text/csv]\n",
            "Saving to: ‘new_test.csv.1’\n",
            "\n",
            "new_test.csv.1      100%[===================>]  41.74K   182KB/s    in 0.2s    \n",
            "\n",
            "2021-03-07 12:01:14 (182 KB/s) - ‘new_test.csv.1’ saved [42738/42738]\n",
            "\n",
            "--2021-03-07 12:01:14--  https://dphi-courses.s3.ap-south-1.amazonaws.com/data-science-challenges/DTU_RoundTable/sample_submission.csv\n",
            "Resolving dphi-courses.s3.ap-south-1.amazonaws.com (dphi-courses.s3.ap-south-1.amazonaws.com)... 52.219.66.127\n",
            "Connecting to dphi-courses.s3.ap-south-1.amazonaws.com (dphi-courses.s3.ap-south-1.amazonaws.com)|52.219.66.127|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 21378 (21K) [text/csv]\n",
            "Saving to: ‘sample_submission.csv.1’\n",
            "\n",
            "sample_submission.c 100%[===================>]  20.88K  88.7KB/s    in 0.2s    \n",
            "\n",
            "2021-03-07 12:01:16 (88.7 KB/s) - ‘sample_submission.csv.1’ saved [21378/21378]\n",
            "\n",
            "Requirement already satisfied: pycaret[full] in /usr/local/lib/python3.7/dist-packages (2.3.0)\n",
            "Requirement already satisfied: scikit-plot in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.3.7)\n",
            "Requirement already satisfied: spacy<2.4.0 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (2.2.4)\n",
            "Requirement already satisfied: ipywidgets in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (7.6.3)\n",
            "Requirement already satisfied: mlflow in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.14.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.0.1)\n",
            "Requirement already satisfied: numpy==1.19.5 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.19.5)\n",
            "Requirement already satisfied: scipy<=1.5.4 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (3.2.2)\n",
            "Requirement already satisfied: scikit-learn==0.23.2 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.23.2)\n",
            "Requirement already satisfied: imbalanced-learn>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.8.0)\n",
            "Requirement already satisfied: kmodes>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.11.0)\n",
            "Requirement already satisfied: IPython in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (5.5.0)\n",
            "Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (3.6.0)\n",
            "Requirement already satisfied: plotly>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (4.4.1)\n",
            "Requirement already satisfied: pyod in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.8.7)\n",
            "Requirement already satisfied: lightgbm>=2.3.1 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (3.1.1)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.11.1)\n",
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.14.0)\n",
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.5.0)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (3.2.5)\n",
            "Requirement already satisfied: textblob in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.15.3)\n",
            "Requirement already satisfied: pandas-profiling>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (2.12.0)\n",
            "Requirement already satisfied: umap-learn in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.5.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.1.5)\n",
            "Requirement already satisfied: yellowbrick>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.3.post1)\n",
            "Requirement already satisfied: cufflinks>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.17.3)\n",
            "Requirement already satisfied: pyLDAvis in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (3.2.2)\n",
            "Requirement already satisfied: Boruta in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.3)\n",
            "Requirement already satisfied: tune-sklearn>=0.2.1; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.2.1)\n",
            "Requirement already satisfied: azure-storage-blob; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (12.8.0)\n",
            "Requirement already satisfied: google-cloud-storage; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.18.1)\n",
            "Requirement already satisfied: awscli; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.19.22)\n",
            "Requirement already satisfied: hyperopt; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.1.2)\n",
            "Requirement already satisfied: xgboost>=1.1.0; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.3.3)\n",
            "Requirement already satisfied: shap; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.39.0)\n",
            "Requirement already satisfied: catboost>=0.23.2; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.24.4)\n",
            "Requirement already satisfied: ray[tune]>=1.0.0; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (1.2.0)\n",
            "Requirement already satisfied: optuna; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (2.5.0)\n",
            "Requirement already satisfied: scikit-optimize>=0.8.1; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (0.8.1)\n",
            "Requirement already satisfied: psutil; extra == \"full\" in /usr/local/lib/python3.7/dist-packages (from pycaret[full]) (5.4.8)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (1.0.5)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (1.0.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (0.8.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (4.59.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (0.4.1)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (1.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (7.4.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (2.25.1)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (1.1.3)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (2.0.5)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (3.0.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<2.4.0->pycaret[full]) (54.0.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->pycaret[full]) (3.5.1)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->pycaret[full]) (5.0.5)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->pycaret[full]) (4.10.1)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from ipywidgets->pycaret[full]) (1.0.0)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets->pycaret[full]) (5.1.2)\n",
            "Requirement already satisfied: Flask in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (1.1.2)\n",
            "Requirement already satisfied: docker>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (4.4.4)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (1.3.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (3.13)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (7.1.2)\n",
            "Requirement already satisfied: prometheus-flask-exporter in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (0.18.1)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (2018.9)\n",
            "Requirement already satisfied: alembic<=1.4.1 in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (1.4.1)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (3.12.4)\n",
            "Requirement already satisfied: querystring-parser in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (1.2.4)\n",
            "Requirement already satisfied: gunicorn; platform_system != \"Windows\" in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (20.0.4)\n",
            "Requirement already satisfied: databricks-cli>=0.8.7 in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (0.14.2)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (0.3)\n",
            "Requirement already satisfied: sqlparse>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (0.4.1)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (1.3.23)\n",
            "Requirement already satisfied: gitpython>=2.1.0 in /usr/local/lib/python3.7/dist-packages (from mlflow->pycaret[full]) (3.1.14)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pycaret[full]) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pycaret[full]) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pycaret[full]) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pycaret[full]) (2.4.7)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.2->pycaret[full]) (2.1.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from IPython->pycaret[full]) (4.4.2)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from IPython->pycaret[full]) (4.8.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from IPython->pycaret[full]) (2.6.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from IPython->pycaret[full]) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from IPython->pycaret[full]) (1.0.18)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from IPython->pycaret[full]) (0.8.1)\n",
            "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim->pycaret[full]) (4.2.0)\n",
            "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from gensim->pycaret[full]) (1.15.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly>=4.4.1->pycaret[full]) (1.3.3)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (from pyod->pycaret[full]) (0.10.2)\n",
            "Requirement already satisfied: numba>=0.35 in /usr/local/lib/python3.7/dist-packages (from pyod->pycaret[full]) (0.51.2)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.7/dist-packages (from lightgbm>=2.3.1->pycaret[full]) (0.36.2)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from wordcloud->pycaret[full]) (7.0.0)\n",
            "Requirement already satisfied: missingno>=0.4.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling>=2.8.0->pycaret[full]) (0.4.2)\n",
            "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling>=2.8.0->pycaret[full]) (2.11.3)\n",
            "Requirement already satisfied: attrs>=19.3.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling>=2.8.0->pycaret[full]) (20.3.0)\n",
            "Requirement already satisfied: confuse>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling>=2.8.0->pycaret[full]) (1.4.0)\n",
            "Requirement already satisfied: phik>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling>=2.8.0->pycaret[full]) (0.11.1)\n",
            "Requirement already satisfied: tangled-up-in-unicode>=0.0.6 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling>=2.8.0->pycaret[full]) (0.0.6)\n",
            "Requirement already satisfied: htmlmin>=0.1.12 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling>=2.8.0->pycaret[full]) (0.1.12)\n",
            "Requirement already satisfied: visions[type_image_path]==0.6.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling>=2.8.0->pycaret[full]) (0.6.0)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.7/dist-packages (from umap-learn->pycaret[full]) (0.5.2)\n",
            "Requirement already satisfied: colorlover>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from cufflinks>=0.17.0->pycaret[full]) (0.3.0)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.7/dist-packages (from pyLDAvis->pycaret[full]) (2.7.2)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyLDAvis->pycaret[full]) (0.16.0)\n",
            "Requirement already satisfied: funcy in /usr/local/lib/python3.7/dist-packages (from pyLDAvis->pycaret[full]) (1.15)\n",
            "Requirement already satisfied: msrest>=0.6.18 in /usr/local/lib/python3.7/dist-packages (from azure-storage-blob; extra == \"full\"->pycaret[full]) (0.6.21)\n",
            "Requirement already satisfied: azure-core<2.0.0,>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from azure-storage-blob; extra == \"full\"->pycaret[full]) (1.11.0)\n",
            "Requirement already satisfied: cryptography>=2.1.4 in /usr/local/lib/python3.7/dist-packages (from azure-storage-blob; extra == \"full\"->pycaret[full]) (3.4.6)\n",
            "Requirement already satisfied: google-auth>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from google-cloud-storage; extra == \"full\"->pycaret[full]) (1.27.0)\n",
            "Requirement already satisfied: google-cloud-core<2.0dev,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from google-cloud-storage; extra == \"full\"->pycaret[full]) (1.0.3)\n",
            "Requirement already satisfied: google-resumable-media<0.5.0dev,>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from google-cloud-storage; extra == \"full\"->pycaret[full]) (0.4.1)\n",
            "Requirement already satisfied: rsa<4.8,>=3.1.2; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from awscli; extra == \"full\"->pycaret[full]) (4.7.2)\n",
            "Requirement already satisfied: botocore==1.20.22 in /usr/local/lib/python3.7/dist-packages (from awscli; extra == \"full\"->pycaret[full]) (1.20.22)\n",
            "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.7/dist-packages (from awscli; extra == \"full\"->pycaret[full]) (0.15.2)\n",
            "Collecting colorama<0.4.4,>=0.2.5\n",
            "  Downloading https://files.pythonhosted.org/packages/c9/dc/45cdef1b4d119eb96316b3117e6d5708a08029992b2fee2c143c7a0a5cc5/colorama-0.4.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from awscli; extra == \"full\"->pycaret[full]) (0.3.4)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from hyperopt; extra == \"full\"->pycaret[full]) (2.5)\n",
            "Requirement already satisfied: pymongo in /usr/local/lib/python3.7/dist-packages (from hyperopt; extra == \"full\"->pycaret[full]) (3.11.3)\n",
            "Requirement already satisfied: slicer==0.0.7 in /usr/local/lib/python3.7/dist-packages (from shap; extra == \"full\"->pycaret[full]) (0.0.7)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost>=0.23.2; extra == \"full\"->pycaret[full]) (0.10.1)\n",
            "Requirement already satisfied: prometheus-client>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (0.9.0)\n",
            "Requirement already satisfied: colorful in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (0.5.4)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (1.0.2)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (2.6.0)\n",
            "Requirement already satisfied: redis>=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (3.5.3)\n",
            "Requirement already satisfied: opencensus in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (0.7.12)\n",
            "Requirement already satisfied: gpustat in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (0.6.0)\n",
            "Requirement already satisfied: aiohttp-cors in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (0.7.0)\n",
            "Requirement already satisfied: py-spy>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (0.3.4)\n",
            "Requirement already satisfied: aioredis in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (1.3.1)\n",
            "Requirement already satisfied: grpcio>=1.28.1 in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (1.32.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (3.0.12)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (3.7.4.post0)\n",
            "Requirement already satisfied: tensorboardX; extra == \"tune\" in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (2.1)\n",
            "Requirement already satisfied: tabulate; extra == \"tune\" in /usr/local/lib/python3.7/dist-packages (from ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (0.8.9)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from optuna; extra == \"full\"->pycaret[full]) (20.9)\n",
            "Requirement already satisfied: cliff in /usr/local/lib/python3.7/dist-packages (from optuna; extra == \"full\"->pycaret[full]) (3.7.0)\n",
            "Requirement already satisfied: colorlog in /usr/local/lib/python3.7/dist-packages (from optuna; extra == \"full\"->pycaret[full]) (4.7.2)\n",
            "Requirement already satisfied: cmaes>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from optuna; extra == \"full\"->pycaret[full]) (0.8.2)\n",
            "Requirement already satisfied: pyaml>=16.9 in /usr/local/lib/python3.7/dist-packages (from scikit-optimize>=0.8.1; extra == \"full\"->pycaret[full]) (20.4.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy<2.4.0->pycaret[full]) (3.7.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0->pycaret[full]) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0->pycaret[full]) (2020.12.5)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0->pycaret[full]) (1.24.3)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0->pycaret[full]) (3.0.4)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (5.3.1)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from traitlets>=4.3.1->ipywidgets->pycaret[full]) (0.2.0)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets->pycaret[full]) (5.1.1)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets->pycaret[full]) (5.3.5)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets->pycaret[full]) (4.7.1)\n",
            "Requirement already satisfied: Werkzeug>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow->pycaret[full]) (1.0.1)\n",
            "Requirement already satisfied: itsdangerous>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow->pycaret[full]) (1.1.0)\n",
            "Requirement already satisfied: websocket-client>=0.32.0 in /usr/local/lib/python3.7/dist-packages (from docker>=4.0.0->mlflow->pycaret[full]) (0.58.0)\n",
            "Requirement already satisfied: Mako in /usr/local/lib/python3.7/dist-packages (from alembic<=1.4.1->mlflow->pycaret[full]) (1.1.4)\n",
            "Requirement already satisfied: python-editor>=0.3 in /usr/local/lib/python3.7/dist-packages (from alembic<=1.4.1->mlflow->pycaret[full]) (1.0.4)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from gitpython>=2.1.0->mlflow->pycaret[full]) (4.0.5)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->IPython->pycaret[full]) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->IPython->pycaret[full]) (0.2.5)\n",
            "Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels->pyod->pycaret[full]) (0.5.1)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.35->pyod->pycaret[full]) (0.34.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.11.1->pandas-profiling>=2.8.0->pycaret[full]) (1.1.1)\n",
            "Requirement already satisfied: imagehash; extra == \"type_image_path\" in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.6.0->pandas-profiling>=2.8.0->pycaret[full]) (4.2.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from msrest>=0.6.18->azure-storage-blob; extra == \"full\"->pycaret[full]) (1.3.0)\n",
            "Requirement already satisfied: isodate>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from msrest>=0.6.18->azure-storage-blob; extra == \"full\"->pycaret[full]) (0.6.0)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.7/dist-packages (from cryptography>=2.1.4->azure-storage-blob; extra == \"full\"->pycaret[full]) (1.14.5)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.2.0->google-cloud-storage; extra == \"full\"->pycaret[full]) (4.2.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.2.0->google-cloud-storage; extra == \"full\"->pycaret[full]) (0.2.8)\n",
            "Requirement already satisfied: google-api-core<2.0.0dev,>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from google-cloud-core<2.0dev,>=1.0.0->google-cloud-storage; extra == \"full\"->pycaret[full]) (1.16.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<4.8,>=3.1.2; python_version >= \"3.6\"->awscli; extra == \"full\"->pycaret[full]) (0.4.8)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from botocore==1.20.22->awscli; extra == \"full\"->pycaret[full]) (0.10.0)\n",
            "Requirement already satisfied: opencensus-context==0.1.2 in /usr/local/lib/python3.7/dist-packages (from opencensus->ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (0.1.2)\n",
            "Requirement already satisfied: blessings>=1.6 in /usr/local/lib/python3.7/dist-packages (from gpustat->ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (1.7)\n",
            "Requirement already satisfied: nvidia-ml-py3>=7.352.0 in /usr/local/lib/python3.7/dist-packages (from gpustat->ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (7.352.0)\n",
            "Requirement already satisfied: async-timeout in /usr/local/lib/python3.7/dist-packages (from aioredis->ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (3.0.1)\n",
            "Requirement already satisfied: hiredis in /usr/local/lib/python3.7/dist-packages (from aioredis->ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (1.1.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (1.6.3)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (5.1.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->ray[tune]>=1.0.0; extra == \"full\"->pycaret[full]) (3.7.4.3)\n",
            "Requirement already satisfied: stevedore>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from cliff->optuna; extra == \"full\"->pycaret[full]) (3.3.0)\n",
            "Requirement already satisfied: PrettyTable>=0.7.2 in /usr/local/lib/python3.7/dist-packages (from cliff->optuna; extra == \"full\"->pycaret[full]) (2.0.0)\n",
            "Requirement already satisfied: cmd2>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from cliff->optuna; extra == \"full\"->pycaret[full]) (1.5.0)\n",
            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from cliff->optuna; extra == \"full\"->pycaret[full]) (5.5.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<2.4.0->pycaret[full]) (3.4.0)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (5.6.1)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (0.9.2)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (1.5.0)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets->pycaret[full]) (22.0.3)\n",
            "Requirement already satisfied: smmap<4,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->gitpython>=2.1.0->mlflow->pycaret[full]) (3.0.5)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash; extra == \"type_image_path\"->visions[type_image_path]==0.6.0->pandas-profiling>=2.8.0->pycaret[full]) (1.1.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.5.0->msrest>=0.6.18->azure-storage-blob; extra == \"full\"->pycaret[full]) (3.1.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography>=2.1.4->azure-storage-blob; extra == \"full\"->pycaret[full]) (2.20)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2.0.0dev,>=1.14.0->google-cloud-core<2.0dev,>=1.0.0->google-cloud-storage; extra == \"full\"->pycaret[full]) (1.52.0)\n",
            "Requirement already satisfied: pyperclip>=1.6 in /usr/local/lib/python3.7/dist-packages (from cmd2>=1.0.0->cliff->optuna; extra == \"full\"->pycaret[full]) (1.8.2)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (0.8.4)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (3.3.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (0.4.4)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (1.4.3)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (0.6.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret[full]) (0.5.1)\n",
            "Installing collected packages: colorama\n",
            "  Found existing installation: colorama 0.4.4\n",
            "    Uninstalling colorama-0.4.4:\n",
            "      Successfully uninstalled colorama-0.4.4\n",
            "Successfully installed colorama-0.4.3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "colorama"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EfdeLz7mpMI"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/new_train.csv\")\n",
        "dft = pd.read_csv(\"/content/new_test.csv\")\n",
        "sub = pd.read_csv(\"/content/sample_submission.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICFu-MsQpSzQ"
      },
      "source": [
        "def get_dom(dt):\n",
        "    return dt.day\n",
        "\n",
        "def get_month(dt):\n",
        "    return dt.month\n",
        "  \n",
        "def get_year(dt):\n",
        "    return dt.year\n",
        "\n",
        "def get_weekday(dt):\n",
        "    return dt.weekday()\n",
        "\n",
        "def get_dayofyear(dt):\n",
        "    return dt.dayofyear\n",
        "\n",
        "def get_weekofyear(dt):\n",
        "    return dt.weekofyear"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuclC2NPp8Co"
      },
      "source": [
        "df['date'] = df['date'].map(pd.to_datetime)\n",
        "dft['date'] = dft['date'].map(pd.to_datetime)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "nrC_-F7pqVvE",
        "outputId": "194e0be8-c5b4-48fa-d60f-45e88bd64dee"
      },
      "source": [
        "df['date1'] = df['date'].map(get_dom)\n",
        "df['weekday'] = df['date'].map(get_weekday)\n",
        "df['month'] = df['date'].map(get_month)\n",
        "df['year'] = df['date'].map(get_year)\n",
        "df['dayofyear'] = df['date'].map(get_dayofyear)\n",
        "df['weekofyear'] = df['date'].map(get_weekofyear)\n",
        "df['quarter'] = df['date'].dt.quarter\n",
        "df['semester'] = np.where(df.quarter.isin([1,2]),1,2)\n",
        "df['dayofweek_name'] = df['date'].dt.day_name()\n",
        "df['is_weekend'] = np.where(df['dayofweek_name'].isin(['Sunday','Saturday']),1,0)\n",
        "del df['dayofweek_name']\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>date</th>\n",
              "      <th>temp</th>\n",
              "      <th>date1</th>\n",
              "      <th>weekday</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>weekofyear</th>\n",
              "      <th>quarter</th>\n",
              "      <th>semester</th>\n",
              "      <th>is_weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980-01-01</td>\n",
              "      <td>4.16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1980</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1980-02-01</td>\n",
              "      <td>4.06</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1980</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1980-03-01</td>\n",
              "      <td>7.12</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1980</td>\n",
              "      <td>61</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1980-04-01</td>\n",
              "      <td>9.23</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1980</td>\n",
              "      <td>92</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1980-05-01</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1980</td>\n",
              "      <td>122</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  temp  date1  weekday  ...  weekofyear  quarter  semester  is_weekend\n",
              "0 1980-01-01  4.16      1        1  ...           1        1         1           0\n",
              "1 1980-02-01  4.06      1        4  ...           5        1         1           0\n",
              "2 1980-03-01  7.12      1        5  ...           9        1         1           1\n",
              "3 1980-04-01  9.23      1        1  ...          14        2         1           0\n",
              "4 1980-05-01  3.20      1        3  ...          18        2         1           0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "91GwvDgPrG9d",
        "outputId": "e3a72cc5-db16-4f29-9d1f-81788f9b12d7"
      },
      "source": [
        "dft['date1'] = dft['date'].map(get_dom)\n",
        "dft['weekday'] = dft['date'].map(get_weekday)\n",
        "dft['month'] = dft['date'].map(get_month)\n",
        "dft['year'] = dft['date'].map(get_year)\n",
        "dft['dayofyear'] = dft['date'].map(get_dayofyear)\n",
        "dft['weekofyear'] = dft['date'].map(get_weekofyear)\n",
        "dft['quarter'] = dft['date'].dt.quarter\n",
        "dft['semester'] = np.where(dft.quarter.isin([1,2]),1,2)\n",
        "dft['dayofweek_name'] = dft['date'].dt.day_name()\n",
        "dft['is_weekend'] = np.where(dft['dayofweek_name'].isin(['Sunday','Saturday']),1,0)\n",
        "del dft['dayofweek_name']\n",
        "dft.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>date</th>\n",
              "      <th>date1</th>\n",
              "      <th>weekday</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>weekofyear</th>\n",
              "      <th>quarter</th>\n",
              "      <th>semester</th>\n",
              "      <th>is_weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2011</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2011</td>\n",
              "      <td>60</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2011</td>\n",
              "      <td>91</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>121</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  date1  weekday  month  ...  weekofyear  quarter  semester  is_weekend\n",
              "0 2011-01-01      1        5      1  ...          52        1         1           1\n",
              "1 2011-02-01      1        1      2  ...           5        1         1           0\n",
              "2 2011-03-01      1        1      3  ...           9        1         1           0\n",
              "3 2011-04-01      1        4      4  ...          13        2         1           0\n",
              "4 2011-05-01      1        6      5  ...          17        2         1           1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEYdPKA5z8RX"
      },
      "source": [
        "df[\"date1_Mean\"] = df.groupby('date1')[\"temp\"].transform('mean')\n",
        "date1_Mean_mean_dict = df.groupby(['date1'])['temp'].mean().to_dict()\n",
        "dft['date1_Mean'] = dft['date1'].apply(lambda x:date1_Mean_mean_dict.get(x,0))\n",
        "df[\"date1_min\"] = df.groupby('date1')[\"temp\"].transform('min')\n",
        "date1_min_min_dict = df.groupby(['date1'])['temp'].min().to_dict()\n",
        "dft['date1_min'] = dft['date1'].apply(lambda x:date1_min_min_dict.get(x,0))\n",
        "df[\"date1_max\"] = df.groupby('date1')[\"temp\"].transform('max')\n",
        "date1_max_max_dict = df.groupby(['date1'])['temp'].max().to_dict()\n",
        "dft['date1_max'] = dft['date1'].apply(lambda x:date1_max_max_dict.get(x,0))\n",
        "df[\"date1_count\"] = df.groupby('date1')[\"temp\"].transform('count')\n",
        "date1_count_count_dict = df.groupby(['date1'])['temp'].count().to_dict()\n",
        "dft['date1_count'] = dft['date1'].apply(lambda x:date1_count_count_dict.get(x,0))\n",
        "\n",
        "df[\"weekday_Mean\"] = df.groupby('weekday')[\"temp\"].transform('mean')\n",
        "weekday_Mean_mean_dict = df.groupby(['weekday'])['temp'].mean().to_dict()\n",
        "dft['weekday_Mean'] = dft['weekday'].apply(lambda x:weekday_Mean_mean_dict.get(x,0))\n",
        "df[\"weekday_min\"] = df.groupby('weekday')[\"temp\"].transform('min')\n",
        "weekday_min_min_dict = df.groupby(['weekday'])['temp'].min().to_dict()\n",
        "dft['weekday_min'] = dft['weekday'].apply(lambda x:weekday_min_min_dict.get(x,0))\n",
        "df[\"weekday_max\"] = df.groupby('weekday')[\"temp\"].transform('max')\n",
        "weekday_max_max_dict = df.groupby(['weekday'])['temp'].max().to_dict()\n",
        "dft['weekday_max'] = dft['weekday'].apply(lambda x:weekday_max_max_dict.get(x,0))\n",
        "df[\"weekday_count\"] = df.groupby('weekday')[\"temp\"].transform('count')\n",
        "weekday_count_count_dict = df.groupby(['weekday'])['temp'].count().to_dict()\n",
        "dft['weekday_count'] = dft['weekday'].apply(lambda x:weekday_count_count_dict.get(x,0))\n",
        "\n",
        "df[\"month_Mean\"] = df.groupby('month')[\"temp\"].transform('mean')\n",
        "month_Mean_mean_dict = df.groupby(['month'])['temp'].mean().to_dict()\n",
        "dft['month_Mean'] = dft['month'].apply(lambda x:month_Mean_mean_dict.get(x,0))\n",
        "df[\"month_min\"] = df.groupby('month')[\"temp\"].transform('min')\n",
        "month_min_min_dict = df.groupby(['month'])['temp'].min().to_dict()\n",
        "dft['month_min'] = dft['month'].apply(lambda x:month_min_min_dict.get(x,0))\n",
        "df[\"month_max\"] = df.groupby('month')[\"temp\"].transform('max')\n",
        "month_max_max_dict = df.groupby(['month'])['temp'].max().to_dict()\n",
        "dft['month_max'] = dft['month'].apply(lambda x:month_max_max_dict.get(x,0))\n",
        "df[\"month_count\"] = df.groupby('month')[\"temp\"].transform('count')\n",
        "month_count_count_dict = df.groupby(['month'])['temp'].count().to_dict()\n",
        "dft['month_count'] = dft['month'].apply(lambda x:month_count_count_dict.get(x,0))\n",
        "\n",
        "df[\"dayofyear_Mean\"] = df.groupby('dayofyear')[\"temp\"].transform('mean')\n",
        "dayofyear_Mean_mean_dict = df.groupby(['dayofyear'])['temp'].mean().to_dict()\n",
        "dft['dayofyear_Mean'] = dft['dayofyear'].apply(lambda x:dayofyear_Mean_mean_dict.get(x,0))\n",
        "df[\"dayofyear_min\"] = df.groupby('dayofyear')[\"temp\"].transform('min')\n",
        "dayofyear_min_min_dict = df.groupby(['dayofyear'])['temp'].min().to_dict()\n",
        "dft['dayofyear_min'] = dft['dayofyear'].apply(lambda x:dayofyear_min_min_dict.get(x,0))\n",
        "df[\"dayofyear_max\"] = df.groupby('dayofyear')[\"temp\"].transform('max')\n",
        "dayofyear_max_max_dict = df.groupby(['dayofyear'])['temp'].max().to_dict()\n",
        "dft['dayofyear_max'] = dft['dayofyear'].apply(lambda x:dayofyear_max_max_dict.get(x,0))\n",
        "df[\"dayofyear_count\"] = df.groupby('dayofyear')[\"temp\"].transform('count')\n",
        "dayofyear_count_count_dict = df.groupby(['dayofyear'])['temp'].count().to_dict()\n",
        "dft['dayofyear_count'] = dft['dayofyear'].apply(lambda x:dayofyear_count_count_dict.get(x,0))\n",
        "\n",
        "df[\"weekofyear_Mean\"] = df.groupby('weekofyear')[\"temp\"].transform('mean')\n",
        "weekofyear_Mean_mean_dict = df.groupby(['weekofyear'])['temp'].mean().to_dict()\n",
        "dft['weekofyear_Mean'] = dft['weekofyear'].apply(lambda x:weekofyear_Mean_mean_dict.get(x,0))\n",
        "df[\"weekofyear_min\"] = df.groupby('weekofyear')[\"temp\"].transform('min')\n",
        "weekofyear_min_min_dict = df.groupby(['weekofyear'])['temp'].min().to_dict()\n",
        "dft['weekofyear_min'] = dft['weekofyear'].apply(lambda x:weekofyear_min_min_dict.get(x,0))\n",
        "df[\"weekofyear_max\"] = df.groupby('weekofyear')[\"temp\"].transform('max')\n",
        "weekofyear_max_max_dict = df.groupby(['weekofyear'])['temp'].max().to_dict()\n",
        "dft['weekofyear_max'] = dft['weekofyear'].apply(lambda x:weekofyear_max_max_dict.get(x,0))\n",
        "df[\"weekofyear_count\"] = df.groupby('weekofyear')[\"temp\"].transform('count')\n",
        "weekofyear_count_count_dict = df.groupby(['weekofyear'])['temp'].count().to_dict()\n",
        "dft['weekofyear_count'] = dft['weekofyear'].apply(lambda x:weekofyear_count_count_dict.get(x,0))\n",
        "\n",
        "df[\"quarter_Mean\"] = df.groupby('quarter')[\"temp\"].transform('mean')\n",
        "quarter_Mean_mean_dict = df.groupby(['quarter'])['temp'].mean().to_dict()\n",
        "dft['quarter_Mean'] = dft['quarter'].apply(lambda x:quarter_Mean_mean_dict.get(x,0))\n",
        "df[\"quarter_min\"] = df.groupby('quarter')[\"temp\"].transform('min')\n",
        "quarter_min_min_dict = df.groupby(['quarter'])['temp'].min().to_dict()\n",
        "dft['quarter_min'] = dft['quarter'].apply(lambda x:quarter_min_min_dict.get(x,0))\n",
        "df[\"quarter_max\"] = df.groupby('quarter')[\"temp\"].transform('max')\n",
        "quarter_max_max_dict = df.groupby(['quarter'])['temp'].max().to_dict()\n",
        "dft['quarter_max'] = dft['quarter'].apply(lambda x:quarter_max_max_dict.get(x,0))\n",
        "df[\"quarter_count\"] = df.groupby('quarter')[\"temp\"].transform('count')\n",
        "quarter_count_count_dict = df.groupby(['quarter'])['temp'].count().to_dict()\n",
        "dft['quarter_count'] = dft['quarter'].apply(lambda x:quarter_count_count_dict.get(x,0))\n",
        "\n",
        "df[\"semester_Mean\"] = df.groupby('semester')[\"temp\"].transform('mean')\n",
        "semester_Mean_mean_dict = df.groupby(['semester'])['temp'].mean().to_dict()\n",
        "dft['semester_Mean'] = dft['semester'].apply(lambda x:semester_Mean_mean_dict.get(x,0))\n",
        "df[\"semester_min\"] = df.groupby('semester')[\"temp\"].transform('min')\n",
        "semester_min_min_dict = df.groupby(['semester'])['temp'].min().to_dict()\n",
        "dft['semester_min'] = dft['semester'].apply(lambda x:semester_min_min_dict.get(x,0))\n",
        "df[\"semester_max\"] = df.groupby('semester')[\"temp\"].transform('max')\n",
        "semester_max_max_dict = df.groupby(['semester'])['temp'].max().to_dict()\n",
        "dft['semester_max'] = dft['semester'].apply(lambda x:semester_max_max_dict.get(x,0))\n",
        "df[\"semester_count\"] = df.groupby('semester')[\"temp\"].transform('count')\n",
        "semester_count_count_dict = df.groupby(['semester'])['temp'].count().to_dict()\n",
        "dft['semester_count'] = dft['semester'].apply(lambda x:semester_count_count_dict.get(x,0))\n",
        "\n",
        "df[\"is_weekend_Mean\"] = df.groupby('is_weekend')[\"temp\"].transform('mean')\n",
        "is_weekend_Mean_mean_dict = df.groupby(['is_weekend'])['temp'].mean().to_dict()\n",
        "dft['is_weekend_Mean'] = dft['is_weekend'].apply(lambda x:is_weekend_Mean_mean_dict.get(x,0))\n",
        "df[\"is_weekend_min\"] = df.groupby('is_weekend')[\"temp\"].transform('min')\n",
        "is_weekend_min_min_dict = df.groupby(['is_weekend'])['temp'].min().to_dict()\n",
        "dft['is_weekend_min'] = dft['is_weekend'].apply(lambda x:is_weekend_min_min_dict.get(x,0))\n",
        "df[\"is_weekend_max\"] = df.groupby('is_weekend')[\"temp\"].transform('max')\n",
        "is_weekend_max_max_dict = df.groupby(['is_weekend'])['temp'].max().to_dict()\n",
        "dft['is_weekend_max'] = dft['is_weekend'].apply(lambda x:is_weekend_max_max_dict.get(x,0))\n",
        "df[\"is_weekend_count\"] = df.groupby('is_weekend')[\"temp\"].transform('count')\n",
        "is_weekend_count_count_dict = df.groupby(['is_weekend'])['temp'].count().to_dict()\n",
        "dft['is_weekend_count'] = dft['is_weekend'].apply(lambda x:is_weekend_count_count_dict.get(x,0))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "_ykwC7Ne0sF-",
        "outputId": "d9b812a8-862a-42f7-c971-a04929c7fd83"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>date</th>\n",
              "      <th>temp</th>\n",
              "      <th>date1</th>\n",
              "      <th>weekday</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>weekofyear</th>\n",
              "      <th>quarter</th>\n",
              "      <th>semester</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>date1_Mean</th>\n",
              "      <th>date1_min</th>\n",
              "      <th>date1_max</th>\n",
              "      <th>date1_count</th>\n",
              "      <th>weekday_Mean</th>\n",
              "      <th>weekday_min</th>\n",
              "      <th>weekday_max</th>\n",
              "      <th>weekday_count</th>\n",
              "      <th>month_Mean</th>\n",
              "      <th>month_min</th>\n",
              "      <th>month_max</th>\n",
              "      <th>month_count</th>\n",
              "      <th>dayofyear_Mean</th>\n",
              "      <th>dayofyear_min</th>\n",
              "      <th>dayofyear_max</th>\n",
              "      <th>dayofyear_count</th>\n",
              "      <th>weekofyear_Mean</th>\n",
              "      <th>weekofyear_min</th>\n",
              "      <th>weekofyear_max</th>\n",
              "      <th>weekofyear_count</th>\n",
              "      <th>quarter_Mean</th>\n",
              "      <th>quarter_min</th>\n",
              "      <th>quarter_max</th>\n",
              "      <th>quarter_count</th>\n",
              "      <th>semester_Mean</th>\n",
              "      <th>semester_min</th>\n",
              "      <th>semester_max</th>\n",
              "      <th>semester_count</th>\n",
              "      <th>is_weekend_Mean</th>\n",
              "      <th>is_weekend_min</th>\n",
              "      <th>is_weekend_max</th>\n",
              "      <th>is_weekend_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1980-01-01</td>\n",
              "      <td>4.16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1980</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.577985</td>\n",
              "      <td>-2.05</td>\n",
              "      <td>32.39</td>\n",
              "      <td>1618</td>\n",
              "      <td>8.905016</td>\n",
              "      <td>-2.05</td>\n",
              "      <td>30.21</td>\n",
              "      <td>961</td>\n",
              "      <td>5.603871</td>\n",
              "      <td>3.08</td>\n",
              "      <td>12.60</td>\n",
              "      <td>31</td>\n",
              "      <td>13.195880</td>\n",
              "      <td>0.34</td>\n",
              "      <td>30.21</td>\n",
              "      <td>216</td>\n",
              "      <td>10.264371</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.64</td>\n",
              "      <td>2798</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.598332</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>32.39</td>\n",
              "      <td>8089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1980-02-01</td>\n",
              "      <td>4.06</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1980</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.659753</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.17</td>\n",
              "      <td>1618</td>\n",
              "      <td>9.935354</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.51</td>\n",
              "      <td>876</td>\n",
              "      <td>5.658065</td>\n",
              "      <td>2.41</td>\n",
              "      <td>9.20</td>\n",
              "      <td>31</td>\n",
              "      <td>7.247880</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>25.76</td>\n",
              "      <td>217</td>\n",
              "      <td>10.264371</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.64</td>\n",
              "      <td>2798</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.598332</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>32.39</td>\n",
              "      <td>8089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1980-03-01</td>\n",
              "      <td>7.12</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1980</td>\n",
              "      <td>61</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.550186</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>30.72</td>\n",
              "      <td>1617</td>\n",
              "      <td>11.923642</td>\n",
              "      <td>-1.80</td>\n",
              "      <td>31.64</td>\n",
              "      <td>961</td>\n",
              "      <td>5.098710</td>\n",
              "      <td>0.72</td>\n",
              "      <td>9.23</td>\n",
              "      <td>31</td>\n",
              "      <td>7.969908</td>\n",
              "      <td>-1.80</td>\n",
              "      <td>25.38</td>\n",
              "      <td>217</td>\n",
              "      <td>10.264371</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.64</td>\n",
              "      <td>2798</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.510544</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>30.72</td>\n",
              "      <td>3234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1980-04-01</td>\n",
              "      <td>9.23</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1980</td>\n",
              "      <td>92</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.577985</td>\n",
              "      <td>-2.05</td>\n",
              "      <td>32.39</td>\n",
              "      <td>1618</td>\n",
              "      <td>15.267731</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.17</td>\n",
              "      <td>930</td>\n",
              "      <td>4.954839</td>\n",
              "      <td>0.81</td>\n",
              "      <td>9.49</td>\n",
              "      <td>31</td>\n",
              "      <td>15.445714</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.17</td>\n",
              "      <td>217</td>\n",
              "      <td>17.492347</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.17</td>\n",
              "      <td>2821</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.598332</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>32.39</td>\n",
              "      <td>8089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1980-05-01</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1980</td>\n",
              "      <td>122</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.591292</td>\n",
              "      <td>-3.56</td>\n",
              "      <td>31.73</td>\n",
              "      <td>1618</td>\n",
              "      <td>17.630000</td>\n",
              "      <td>-1.68</td>\n",
              "      <td>29.67</td>\n",
              "      <td>961</td>\n",
              "      <td>4.908065</td>\n",
              "      <td>1.67</td>\n",
              "      <td>9.14</td>\n",
              "      <td>31</td>\n",
              "      <td>13.388341</td>\n",
              "      <td>-1.68</td>\n",
              "      <td>29.67</td>\n",
              "      <td>217</td>\n",
              "      <td>17.492347</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.17</td>\n",
              "      <td>2821</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.598332</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>32.39</td>\n",
              "      <td>8089</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  temp  date1  ...  is_weekend_min  is_weekend_max  is_weekend_count\n",
              "0 1980-01-01  4.16      1  ...           -5.11           32.39              8089\n",
              "1 1980-02-01  4.06      1  ...           -5.11           32.39              8089\n",
              "2 1980-03-01  7.12      1  ...           -2.75           30.72              3234\n",
              "3 1980-04-01  9.23      1  ...           -5.11           32.39              8089\n",
              "4 1980-05-01  3.20      1  ...           -5.11           32.39              8089\n",
              "\n",
              "[5 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "lHjImon-0tuu",
        "outputId": "60cc25fb-de6f-44c3-ac8d-b47d993c2e39"
      },
      "source": [
        "dft.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>date</th>\n",
              "      <th>date1</th>\n",
              "      <th>weekday</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>weekofyear</th>\n",
              "      <th>quarter</th>\n",
              "      <th>semester</th>\n",
              "      <th>is_weekend</th>\n",
              "      <th>date1_Mean</th>\n",
              "      <th>date1_min</th>\n",
              "      <th>date1_max</th>\n",
              "      <th>date1_count</th>\n",
              "      <th>weekday_Mean</th>\n",
              "      <th>weekday_min</th>\n",
              "      <th>weekday_max</th>\n",
              "      <th>weekday_count</th>\n",
              "      <th>month_Mean</th>\n",
              "      <th>month_min</th>\n",
              "      <th>month_max</th>\n",
              "      <th>month_count</th>\n",
              "      <th>dayofyear_Mean</th>\n",
              "      <th>dayofyear_min</th>\n",
              "      <th>dayofyear_max</th>\n",
              "      <th>dayofyear_count</th>\n",
              "      <th>weekofyear_Mean</th>\n",
              "      <th>weekofyear_min</th>\n",
              "      <th>weekofyear_max</th>\n",
              "      <th>weekofyear_count</th>\n",
              "      <th>quarter_Mean</th>\n",
              "      <th>quarter_min</th>\n",
              "      <th>quarter_max</th>\n",
              "      <th>quarter_count</th>\n",
              "      <th>semester_Mean</th>\n",
              "      <th>semester_min</th>\n",
              "      <th>semester_max</th>\n",
              "      <th>semester_count</th>\n",
              "      <th>is_weekend_Mean</th>\n",
              "      <th>is_weekend_min</th>\n",
              "      <th>is_weekend_max</th>\n",
              "      <th>is_weekend_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.550186</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>30.72</td>\n",
              "      <td>1617</td>\n",
              "      <td>8.905016</td>\n",
              "      <td>-2.05</td>\n",
              "      <td>30.21</td>\n",
              "      <td>961</td>\n",
              "      <td>5.603871</td>\n",
              "      <td>3.08</td>\n",
              "      <td>12.60</td>\n",
              "      <td>31</td>\n",
              "      <td>6.133767</td>\n",
              "      <td>0.64</td>\n",
              "      <td>12.35</td>\n",
              "      <td>215</td>\n",
              "      <td>10.264371</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.64</td>\n",
              "      <td>2798</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.510544</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>30.72</td>\n",
              "      <td>3234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-02-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2011</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.577985</td>\n",
              "      <td>-2.05</td>\n",
              "      <td>32.39</td>\n",
              "      <td>1618</td>\n",
              "      <td>9.935354</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.51</td>\n",
              "      <td>876</td>\n",
              "      <td>5.658065</td>\n",
              "      <td>2.41</td>\n",
              "      <td>9.20</td>\n",
              "      <td>31</td>\n",
              "      <td>7.247880</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>25.76</td>\n",
              "      <td>217</td>\n",
              "      <td>10.264371</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.64</td>\n",
              "      <td>2798</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.598332</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>32.39</td>\n",
              "      <td>8089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-03-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2011</td>\n",
              "      <td>60</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.577985</td>\n",
              "      <td>-2.05</td>\n",
              "      <td>32.39</td>\n",
              "      <td>1618</td>\n",
              "      <td>11.923642</td>\n",
              "      <td>-1.80</td>\n",
              "      <td>31.64</td>\n",
              "      <td>961</td>\n",
              "      <td>6.022581</td>\n",
              "      <td>1.24</td>\n",
              "      <td>13.57</td>\n",
              "      <td>31</td>\n",
              "      <td>7.969908</td>\n",
              "      <td>-1.80</td>\n",
              "      <td>25.38</td>\n",
              "      <td>217</td>\n",
              "      <td>10.264371</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.64</td>\n",
              "      <td>2798</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.598332</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>32.39</td>\n",
              "      <td>8089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-04-01</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2011</td>\n",
              "      <td>91</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.659753</td>\n",
              "      <td>-2.42</td>\n",
              "      <td>31.17</td>\n",
              "      <td>1618</td>\n",
              "      <td>15.267731</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.17</td>\n",
              "      <td>930</td>\n",
              "      <td>7.287419</td>\n",
              "      <td>2.56</td>\n",
              "      <td>16.08</td>\n",
              "      <td>31</td>\n",
              "      <td>9.524009</td>\n",
              "      <td>0.81</td>\n",
              "      <td>16.08</td>\n",
              "      <td>217</td>\n",
              "      <td>17.492347</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.17</td>\n",
              "      <td>2821</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.598332</td>\n",
              "      <td>-5.11</td>\n",
              "      <td>32.39</td>\n",
              "      <td>8089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-05-01</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>121</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5.40707</td>\n",
              "      <td>0.37</td>\n",
              "      <td>12.87</td>\n",
              "      <td>372</td>\n",
              "      <td>15.470903</td>\n",
              "      <td>-2.33</td>\n",
              "      <td>30.44</td>\n",
              "      <td>1617</td>\n",
              "      <td>17.630000</td>\n",
              "      <td>-1.68</td>\n",
              "      <td>29.67</td>\n",
              "      <td>961</td>\n",
              "      <td>7.938065</td>\n",
              "      <td>1.54</td>\n",
              "      <td>20.42</td>\n",
              "      <td>31</td>\n",
              "      <td>15.190092</td>\n",
              "      <td>1.67</td>\n",
              "      <td>21.02</td>\n",
              "      <td>217</td>\n",
              "      <td>17.492347</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.17</td>\n",
              "      <td>2821</td>\n",
              "      <td>13.893152</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>31.64</td>\n",
              "      <td>5619</td>\n",
              "      <td>15.510544</td>\n",
              "      <td>-2.75</td>\n",
              "      <td>30.72</td>\n",
              "      <td>3234</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  date1  weekday  ...  is_weekend_min  is_weekend_max  is_weekend_count\n",
              "0 2011-01-01      1        5  ...           -2.75           30.72              3234\n",
              "1 2011-02-01      1        1  ...           -5.11           32.39              8089\n",
              "2 2011-03-01      1        1  ...           -5.11           32.39              8089\n",
              "3 2011-04-01      1        4  ...           -5.11           32.39              8089\n",
              "4 2011-05-01      1        6  ...           -2.75           30.72              3234\n",
              "\n",
              "[5 rows x 42 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ydg3HNqRE7Yb"
      },
      "source": [
        "!pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n",
        "from pandas_profiling import ProfileReport\n",
        "profile = ProfileReport(df, title='Pandas Profiling Report', html={'style':{'full_width':False}})\n",
        "#profile.to_notebook_iframe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4c7z2kJJi68",
        "outputId": "89f9c3ff-83b2-4ae7-eb9f-1e3bd38136aa"
      },
      "source": [
        "#!pip install mlxtend\n",
        "import joblib\n",
        "import sys\n",
        "sys.modules['sklearn.externals.joblib'] = joblib\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score\n",
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "# separate train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df.drop(labels=['temp','date'], axis=1),\n",
        "    df['temp'],\n",
        "    test_size=0.3,\n",
        "    random_state=0)\n",
        "\n",
        "X_train.shape, X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7926, 41), (3397, 41))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0Wk_YiBL-Gy",
        "outputId": "c7361ce7-a510-4daa-b596-839ffba8c296"
      },
      "source": [
        "#find and remove correlated features\n",
        "def correlation(dataset, threshold):\n",
        "    col_corr = set()  # Set of all the names of correlated columns\n",
        "    corr_matrix = dataset.corr()\n",
        "    for i in range(len(corr_matrix.columns)):\n",
        "        for j in range(i):\n",
        "            if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value\n",
        "                colname = corr_matrix.columns[i]  # getting the name of column\n",
        "                col_corr.add(colname)\n",
        "    return col_corr\n",
        "\n",
        "corr_features = correlation(X_train, 0.8)\n",
        "print('correlated features: ', len(set(corr_features)) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "correlated features:  20\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EgSG4Ie_MMLM",
        "outputId": "98bf8272-5df3-48cb-c924-d8554c8cbcdd"
      },
      "source": [
        "# removed correlated  features\n",
        "X_train.drop(labels=corr_features, axis=1, inplace=True)\n",
        "X_test.drop(labels=corr_features, axis=1, inplace=True)\n",
        "\n",
        "X_train.shape, X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7926, 21), (3397, 21))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emGZmE-RMNly",
        "outputId": "b9ca7ecc-6462-4067-b729-8ba8b157f9e5"
      },
      "source": [
        "# step forward feature selection\n",
        "\n",
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
        "\n",
        "sfs1 = SFS(RandomForestRegressor(), \n",
        "           k_features=15, \n",
        "           forward=True, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='r2',\n",
        "           cv=3)\n",
        "\n",
        "sfs1 = sfs1.fit(np.array(X_train), y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.6s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:   10.4s finished\n",
            "\n",
            "[2021-03-07 12:13:16] Features: 1/15 -- score: 0.8550302405869113[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.3s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:   25.5s finished\n",
            "\n",
            "[2021-03-07 12:13:42] Features: 2/15 -- score: 0.8970462999299612[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.5s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:   32.3s finished\n",
            "\n",
            "[2021-03-07 12:14:14] Features: 3/15 -- score: 0.9126074157962311[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:   54.3s finished\n",
            "\n",
            "[2021-03-07 12:15:08] Features: 4/15 -- score: 0.9156280282311168[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.4s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:   57.2s finished\n",
            "\n",
            "[2021-03-07 12:16:05] Features: 5/15 -- score: 0.9162281403012651[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.5s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:   55.1s finished\n",
            "\n",
            "[2021-03-07 12:17:01] Features: 6/15 -- score: 0.9164974055220679[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:   57.6s finished\n",
            "\n",
            "[2021-03-07 12:17:58] Features: 7/15 -- score: 0.9168902373826554[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:   55.7s finished\n",
            "\n",
            "[2021-03-07 12:18:54] Features: 8/15 -- score: 0.9170136205341043[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.3s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:   54.9s finished\n",
            "\n",
            "[2021-03-07 12:19:49] Features: 9/15 -- score: 0.9169065185887652[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.4s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:   52.0s finished\n",
            "\n",
            "[2021-03-07 12:20:41] Features: 10/15 -- score: 0.9160879790113355[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.7s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:   50.0s finished\n",
            "\n",
            "[2021-03-07 12:21:31] Features: 11/15 -- score: 0.9159106736138988[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:   49.9s finished\n",
            "\n",
            "[2021-03-07 12:22:21] Features: 12/15 -- score: 0.9152629853891027[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.4s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:   48.8s finished\n",
            "\n",
            "[2021-03-07 12:23:10] Features: 13/15 -- score: 0.9150592359557684[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.8s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   45.9s finished\n",
            "\n",
            "[2021-03-07 12:23:55] Features: 14/15 -- score: 0.9148910705244631[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.9s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:   41.4s finished\n",
            "\n",
            "[2021-03-07 12:24:37] Features: 15/15 -- score: 0.9143307751690347"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udfKFv3DMePP",
        "outputId": "16d6f62d-087e-48d5-f2c2-2c8cd4eb603c"
      },
      "source": [
        "sfs1.k_feature_idx_\n",
        "X_train.columns[list(sfs1.k_feature_idx_)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date1', 'weekday', 'month', 'year', 'is_weekend', 'date1_Mean',\n",
              "       'date1_min', 'date1_count', 'weekday_max', 'weekday_count',\n",
              "       'month_Mean', 'month_count', 'dayofyear_Mean', 'dayofyear_count',\n",
              "       'weekofyear_count'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-svJB3plMkJT",
        "outputId": "63d2fb43-75dc-4b2b-8a62-f4d99993ee2e"
      },
      "source": [
        "# step backward feature elimination\n",
        "\n",
        "sfs1 = SFS(RandomForestRegressor(), \n",
        "           k_features=10, \n",
        "           forward=False, \n",
        "           floating=False, \n",
        "           verbose=2,\n",
        "           scoring='r2',\n",
        "           cv=3)\n",
        "\n",
        "sfs1 = sfs1.fit(np.array(X_train), y_train)\n",
        "\n",
        "sfs1.k_feature_idx_\n",
        "X_train.columns[list(sfs1.k_feature_idx_)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    8.3s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:  2.8min finished\n",
            "\n",
            "[2021-03-07 10:29:45] Features: 20/10 -- score: 0.9129541112907971[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.8s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:  2.5min finished\n",
            "\n",
            "[2021-03-07 10:32:14] Features: 19/10 -- score: 0.913611542326605[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.6s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:  2.3min finished\n",
            "\n",
            "[2021-03-07 10:34:30] Features: 18/10 -- score: 0.9137233902467311[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:  2.0min finished\n",
            "\n",
            "[2021-03-07 10:36:31] Features: 17/10 -- score: 0.9140718432274356[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.7s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:  1.8min finished\n",
            "\n",
            "[2021-03-07 10:38:21] Features: 16/10 -- score: 0.9141502711993876[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.3s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:  1.6min finished\n",
            "\n",
            "[2021-03-07 10:39:57] Features: 15/10 -- score: 0.9139602850149219[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  1.5min finished\n",
            "\n",
            "[2021-03-07 10:41:25] Features: 14/10 -- score: 0.9143517827415574[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.7s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:  1.3min finished\n",
            "\n",
            "[2021-03-07 10:42:41] Features: 13/10 -- score: 0.9145780552359244[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.5s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:  1.1min finished\n",
            "\n",
            "[2021-03-07 10:43:48] Features: 12/10 -- score: 0.9152231190684987[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:   56.6s finished\n",
            "\n",
            "[2021-03-07 10:44:44] Features: 11/10 -- score: 0.9151798377500077[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.8s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:   49.1s finished\n",
            "\n",
            "[2021-03-07 10:45:33] Features: 10/10 -- score: 0.915585073074098"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date1', 'month', 'year', 'date1_Mean', 'date1_count', 'weekday_count',\n",
              "       'dayofyear_Mean', 'dayofyear_count', 'weekofyear_max',\n",
              "       'weekofyear_count'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWgpb2x2PkUZ"
      },
      "source": [
        "df_new  = df[['date','date1', 'month', 'year', 'date1_Mean', 'date1_count', 'weekday_count',\n",
        "       'dayofyear_Mean', 'dayofyear_count', 'weekofyear_max','weekofyear_count','is_weekend','temp']]\n",
        "dft_new = dft[['date','date1', 'month', 'year', 'date1_Mean', 'date1_count', 'weekday_count',\n",
        "       'dayofyear_Mean', 'dayofyear_count', 'weekofyear_max','weekofyear_count','is_weekend']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "dc7a5f4d5d944b079566adf27d05cc2e",
            "91c3bfc034e64767840fb865f34f1ec1",
            "11fb4bf94538457a8034b9a494b8dd88",
            "8c0cb088fc91473d9a698bc547517ebd",
            "a09888c55f5240fd9deab188fda56900",
            "cedfab03514a4e92b4517ad5b421be3b"
          ]
        },
        "id": "973HzN42rflR",
        "outputId": "e991b83d-b0ad-4b00-c5f6-182af42f6b04"
      },
      "source": [
        "from pycaret.regression import *\n",
        "exp_reg = setup(df_new,\n",
        "                target='temp',\n",
        "                use_gpu = True,\n",
        "                #numeric_imputation = 'mean', #median\n",
        "                #categorical_imputation = 'mode', #constant\n",
        "                #numeric_features = [],\n",
        "                #categorical_features = [],\n",
        "                date_features = ['date'],\n",
        "                #ignore_features = [],\n",
        "                #ordinal_features = {'column_name' : [‘low’, ‘medium’, ‘high’] },\n",
        "                #high_cardinality_features = [],\n",
        "                #high_cardinality_method = 'frequency', #clustering,\n",
        "                #group_features = [],\n",
        "                #handle_unknown_categorical = True,\n",
        "                #unknown_categorical_method = 'least_frequent', or 'most_frequent',\n",
        "                combine_rare_levels = True,\n",
        "                #bin_numeric_features = ['Quantity','Country'],\n",
        "                #transformation = True,\n",
        "                #transformation_method = 'yeo-johnson',#'quantile',yeo-johnson,\n",
        "                transform_target = True,\n",
        "                transform_target_method = 'yeo-johnson', #box-cox\n",
        "                #feature_selection = True,\n",
        "                normalize = True,\n",
        "                normalize_method = 'robust',#minmax ,maxabs ,robust, zscore,\n",
        "                #pca = True,\n",
        "                #pca_method = 'incremental', #kernel, incremental, linear\n",
        "                feature_interaction = True,\n",
        "                feature_ratio = True,\n",
        "                trigonometry_features=False,\n",
        "                polynomial_features = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>4351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>temp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(11323, 13)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(7926, 61)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(3397, 61)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>USI</td>\n",
              "      <td>06f0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>robust</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Transform Target</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Transform Target Method</td>\n",
              "      <td>yeo-johnson</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id              4351\n",
              "1                                   Target              temp\n",
              "2                            Original Data       (11323, 13)\n",
              "3                           Missing Values             False\n",
              "4                         Numeric Features                 5\n",
              "5                     Categorical Features                 6\n",
              "6                         Ordinal Features             False\n",
              "7                High Cardinality Features             False\n",
              "8                  High Cardinality Method              None\n",
              "9                    Transformed Train Set        (7926, 61)\n",
              "10                    Transformed Test Set        (3397, 61)\n",
              "11                      Shuffle Train-Test              True\n",
              "12                     Stratify Train-Test             False\n",
              "13                          Fold Generator             KFold\n",
              "14                             Fold Number                10\n",
              "15                                CPU Jobs                -1\n",
              "16                                 Use GPU              True\n",
              "17                          Log Experiment             False\n",
              "18                         Experiment Name  reg-default-name\n",
              "19                                     USI              06f0\n",
              "20                         Imputation Type            simple\n",
              "21          Iterative Imputation Iteration              None\n",
              "22                         Numeric Imputer              mean\n",
              "23      Iterative Imputation Numeric Model              None\n",
              "24                     Categorical Imputer          constant\n",
              "25  Iterative Imputation Categorical Model              None\n",
              "26           Unknown Categoricals Handling    least_frequent\n",
              "27                               Normalize              True\n",
              "28                        Normalize Method            robust\n",
              "29                          Transformation             False\n",
              "30                   Transformation Method              None\n",
              "31                                     PCA             False\n",
              "32                              PCA Method              None\n",
              "33                          PCA Components              None\n",
              "34                     Ignore Low Variance             False\n",
              "35                     Combine Rare Levels              True\n",
              "36                    Rare Level Threshold               0.1\n",
              "37                         Numeric Binning             False\n",
              "38                         Remove Outliers             False\n",
              "39                      Outliers Threshold              None\n",
              "40                Remove Multicollinearity             False\n",
              "41             Multicollinearity Threshold              None\n",
              "42                              Clustering             False\n",
              "43                    Clustering Iteration              None\n",
              "44                     Polynomial Features             False\n",
              "45                       Polynomial Degree              None\n",
              "46                    Trignometry Features             False\n",
              "47                    Polynomial Threshold              None\n",
              "48                          Group Features             False\n",
              "49                       Feature Selection             False\n",
              "50                Feature Selection Method           classic\n",
              "51            Features Selection Threshold              None\n",
              "52                     Feature Interaction              True\n",
              "53                           Feature Ratio              True\n",
              "54                   Interaction Threshold              0.01\n",
              "55                        Transform Target              True\n",
              "56                 Transform Target Method       yeo-johnson"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpFzLtEesTZ-"
      },
      "source": [
        "#compare_models(sort='RMSE')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 994,
          "referenced_widgets": [
            "f93eb2f73c314726a2b21a6fa892ab5e",
            "bf4f7a8eb19f4205a52d34184eb90871",
            "ac80a82af6734672b9e975b9502dfff4",
            "fee804a92d954e43b0b16158c3ea439f",
            "16509114a92c4dea8160eb875d3d6ee1",
            "c78de6be33094defa6626e6eb8c52ba7"
          ]
        },
        "id": "whU12yantePM",
        "outputId": "f50b5a41-96e5-4706-8ddf-313148f445a5"
      },
      "source": [
        "lightgbm = create_model('lightgbm')\n",
        "predict_model(lightgbm)\n",
        "tuned_lgbm = tune_model(lightgbm, optimize='RMSE', search_library ='scikit-optimize',n_iter=20)\n",
        "predict_model(tuned_lgbm)\n",
        "#Finalizing the Model for Deployment\n",
        "finalize_model(tuned_lgbm)\n",
        "#predict for test data\n",
        "test_predictions = predict_model(tuned_lgbm, data=dft_new)\n",
        "#load prediction into submission file\n",
        "sub['prediction'] = test_predictions['Label']\n",
        "sub.to_csv('subn5.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.7936</td>\n",
              "      <td>5.4410</td>\n",
              "      <td>2.3326</td>\n",
              "      <td>0.9134</td>\n",
              "      <td>0.2492</td>\n",
              "      <td>0.3171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.7141</td>\n",
              "      <td>4.8923</td>\n",
              "      <td>2.2118</td>\n",
              "      <td>0.9221</td>\n",
              "      <td>0.2301</td>\n",
              "      <td>0.2639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.7967</td>\n",
              "      <td>5.3866</td>\n",
              "      <td>2.3209</td>\n",
              "      <td>0.9135</td>\n",
              "      <td>0.2395</td>\n",
              "      <td>0.2221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.7304</td>\n",
              "      <td>4.8495</td>\n",
              "      <td>2.2022</td>\n",
              "      <td>0.9191</td>\n",
              "      <td>0.2104</td>\n",
              "      <td>0.2125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.7232</td>\n",
              "      <td>4.7618</td>\n",
              "      <td>2.1822</td>\n",
              "      <td>0.9223</td>\n",
              "      <td>0.2306</td>\n",
              "      <td>0.2411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.7969</td>\n",
              "      <td>5.3045</td>\n",
              "      <td>2.3032</td>\n",
              "      <td>0.9115</td>\n",
              "      <td>0.2347</td>\n",
              "      <td>0.2213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.8519</td>\n",
              "      <td>5.5586</td>\n",
              "      <td>2.3577</td>\n",
              "      <td>0.9091</td>\n",
              "      <td>0.2386</td>\n",
              "      <td>0.2294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.8458</td>\n",
              "      <td>5.6162</td>\n",
              "      <td>2.3699</td>\n",
              "      <td>0.9113</td>\n",
              "      <td>0.2403</td>\n",
              "      <td>0.2306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.7892</td>\n",
              "      <td>5.4482</td>\n",
              "      <td>2.3341</td>\n",
              "      <td>0.9167</td>\n",
              "      <td>0.2319</td>\n",
              "      <td>0.2238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.7068</td>\n",
              "      <td>4.8955</td>\n",
              "      <td>2.2126</td>\n",
              "      <td>0.9209</td>\n",
              "      <td>0.2304</td>\n",
              "      <td>0.2289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>1.7749</td>\n",
              "      <td>5.2154</td>\n",
              "      <td>2.2827</td>\n",
              "      <td>0.9160</td>\n",
              "      <td>0.2336</td>\n",
              "      <td>0.2391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0504</td>\n",
              "      <td>0.3110</td>\n",
              "      <td>0.0684</td>\n",
              "      <td>0.0046</td>\n",
              "      <td>0.0096</td>\n",
              "      <td>0.0292</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
              "0     1.7936  5.4410  2.3326  0.9134  0.2492  0.3171\n",
              "1     1.7141  4.8923  2.2118  0.9221  0.2301  0.2639\n",
              "2     1.7967  5.3866  2.3209  0.9135  0.2395  0.2221\n",
              "3     1.7304  4.8495  2.2022  0.9191  0.2104  0.2125\n",
              "4     1.7232  4.7618  2.1822  0.9223  0.2306  0.2411\n",
              "5     1.7969  5.3045  2.3032  0.9115  0.2347  0.2213\n",
              "6     1.8519  5.5586  2.3577  0.9091  0.2386  0.2294\n",
              "7     1.8458  5.6162  2.3699  0.9113  0.2403  0.2306\n",
              "8     1.7892  5.4482  2.3341  0.9167  0.2319  0.2238\n",
              "9     1.7068  4.8955  2.2126  0.9209  0.2304  0.2289\n",
              "Mean  1.7749  5.2154  2.2827  0.9160  0.2336  0.2391\n",
              "SD    0.0504  0.3110  0.0684  0.0046  0.0096  0.0292"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>1.7704</td>\n",
              "      <td>5.1125</td>\n",
              "      <td>2.2611</td>\n",
              "      <td>0.9173</td>\n",
              "      <td>0.2276</td>\n",
              "      <td>0.288</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Model     MAE     MSE  ...      R2   RMSLE   MAPE\n",
              "0  Light Gradient Boosting Machine  1.7704  5.1125  ...  0.9173  0.2276  0.288\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n",
            "[LightGBM] [Warning] feature_fraction is set=0.5245772583529271, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.5245772583529271\n",
            "[LightGBM] [Warning] bagging_fraction is set=0.8675193286906737, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8675193286906737\n",
            "[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
          "referenced_widgets": [
            "aa0d6f72b63d4962a706732741b3c0ab",
            "9acc9d820e1a4b929fd731ae9213c2e1",
            "880403c4ecd34c43ba2a3eb1996bae8a",
            "4d4314810c104f57ba9ae572d84b5e5d",
            "a345352930704b5294531f082f1ad378",
            "0370e713d28b43dc90339d137ab9d2fd"
          ]
        },
        "id": "J0Bl1UoYlb0d",
        "outputId": "ecbd537d-8a65-44a8-c17a-5c32893550d5"
      },
      "source": [
        "catboost = create_model('catboost')\n",
        "#predict_model(catboost)\n",
        "tuned_cb = tune_model(catboost, optimize='RMSE')\n",
        "#predict_model(tuned_cb)\n",
        "#Finalizing the Model for Deployment\n",
        "finalize_model(tuned_cb)\n",
        "#predict for test data\n",
        "test_predictions = predict_model(tuned_cb, data=dft)\n",
        "#load prediction into submission file\n",
        "sub['prediction'] = test_predictions['Label']\n",
        "sub.to_csv('subcb1.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.8318</td>\n",
              "      <td>5.2987</td>\n",
              "      <td>2.3019</td>\n",
              "      <td>0.9088</td>\n",
              "      <td>0.2252</td>\n",
              "      <td>0.2167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.7695</td>\n",
              "      <td>5.0483</td>\n",
              "      <td>2.2469</td>\n",
              "      <td>0.9189</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.2046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.8007</td>\n",
              "      <td>5.1490</td>\n",
              "      <td>2.2691</td>\n",
              "      <td>0.9223</td>\n",
              "      <td>0.2412</td>\n",
              "      <td>0.2393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.7248</td>\n",
              "      <td>5.0301</td>\n",
              "      <td>2.2428</td>\n",
              "      <td>0.9189</td>\n",
              "      <td>0.2325</td>\n",
              "      <td>0.2355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.7069</td>\n",
              "      <td>4.5167</td>\n",
              "      <td>2.1253</td>\n",
              "      <td>0.9260</td>\n",
              "      <td>0.2237</td>\n",
              "      <td>0.2242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.6750</td>\n",
              "      <td>4.5846</td>\n",
              "      <td>2.1412</td>\n",
              "      <td>0.9296</td>\n",
              "      <td>0.2204</td>\n",
              "      <td>0.2053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.7260</td>\n",
              "      <td>4.7974</td>\n",
              "      <td>2.1903</td>\n",
              "      <td>0.9220</td>\n",
              "      <td>0.2328</td>\n",
              "      <td>0.2193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.7453</td>\n",
              "      <td>5.0673</td>\n",
              "      <td>2.2511</td>\n",
              "      <td>0.9166</td>\n",
              "      <td>0.2270</td>\n",
              "      <td>0.2800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.8354</td>\n",
              "      <td>5.2728</td>\n",
              "      <td>2.2963</td>\n",
              "      <td>0.9085</td>\n",
              "      <td>0.2209</td>\n",
              "      <td>0.2110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.8137</td>\n",
              "      <td>5.1295</td>\n",
              "      <td>2.2648</td>\n",
              "      <td>0.9155</td>\n",
              "      <td>0.2410</td>\n",
              "      <td>0.3164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>1.7629</td>\n",
              "      <td>4.9895</td>\n",
              "      <td>2.2330</td>\n",
              "      <td>0.9187</td>\n",
              "      <td>0.2290</td>\n",
              "      <td>0.2352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0529</td>\n",
              "      <td>0.2562</td>\n",
              "      <td>0.0579</td>\n",
              "      <td>0.0064</td>\n",
              "      <td>0.0072</td>\n",
              "      <td>0.0343</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         MAE     MSE    RMSE      R2   RMSLE    MAPE\n",
              "0     1.8318  5.2987  2.3019  0.9088  0.2252  0.2167\n",
              "1     1.7695  5.0483  2.2469  0.9189  0.2256  0.2046\n",
              "2     1.8007  5.1490  2.2691  0.9223  0.2412  0.2393\n",
              "3     1.7248  5.0301  2.2428  0.9189  0.2325  0.2355\n",
              "4     1.7069  4.5167  2.1253  0.9260  0.2237  0.2242\n",
              "5     1.6750  4.5846  2.1412  0.9296  0.2204  0.2053\n",
              "6     1.7260  4.7974  2.1903  0.9220  0.2328  0.2193\n",
              "7     1.7453  5.0673  2.2511  0.9166  0.2270  0.2800\n",
              "8     1.8354  5.2728  2.2963  0.9085  0.2209  0.2110\n",
              "9     1.8137  5.1295  2.2648  0.9155  0.2410  0.3164\n",
              "Mean  1.7629  4.9895  2.2330  0.9187  0.2290  0.2352\n",
              "SD    0.0529  0.2562  0.0579  0.0064  0.0072  0.0343"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzULavzAcqLd"
      },
      "source": [
        "xgb = create_model('xgboost')\n",
        "#predict_model(xgb)\n",
        "tuned_xgb = tune_model(xgb, optimize='RMSE', search_library ='scikit-optimize')\n",
        "#predict_model(tuned_xgb)\n",
        "finalize_model(tuned_xgb)\n",
        "#predict for test data\n",
        "test_predictions = predict_model(tuned_xgb, data=dft)\n",
        "#load prediction into submission file\n",
        "sub['prediction'] = test_predictions['Label']\n",
        "sub.to_csv('subxgb1.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywKJi96NZ2QC"
      },
      "source": [
        "# stack models dynamically\n",
        "top5 = compare_models(n_select = 5)\n",
        "stacker = stack_models(estimator_list = top5[1:], meta_model = top5[0])\n",
        "finalize_model(stacker)\n",
        "#predict for test data\n",
        "test_predictions = predict_model(stacker, data=dft_new)\n",
        "#load prediction into submission file\n",
        "sub['prediction'] = test_predictions['Label']\n",
        "sub.to_csv('substack1.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIZgBGWNbTFs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuCtcU4NaEXl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}