Feture_Scaling.ipynb

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        "# **Feature Extraction**\n",
        "- it is process of selecting and extracting the most important feaures from raw data\n",
        "\n",
        "**Types of feature extractions**\n",
        "- 1) feature scaling\n",
        "- 2) feature selection\n",
        "- 3) Principal component Analysis (PCA)"
      ],
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      "source": [
        "# **1) Feature Scaling**\n",
        "- we normalize/standardize the dataset far faster than calculation\n",
        "- normalization, standardization, unit vector is used\n",
        "### **1) Standardization**\n",
        "- converting the data into (mean) = 0, (sigma) = 1\n",
        "- Used basically for Machine learning algorithms"
      ],
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      "source": [
        "#importing the required libries\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"tips\")"
      ],
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        "id": "cswaFJYaHZp2"
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      "outputs": []
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        "df.head()"
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              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ],
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              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
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              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
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              "      <td>Sun</td>\n",
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              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 244,\n  \"fields\": [\n    {\n      \"column\": \"total_bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.902411954856856,\n        \"min\": 3.07,\n        \"max\": 50.81,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          22.12,\n          20.23,\n          14.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3836381890011822,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          3.35,\n          1.5,\n          6.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Sat\",\n          \"Fri\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Lunch\",\n          \"Dinner\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "source": [
        "total_bill = list(df[\"total_bill\"])"
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        "id": "rkWIkNy_Hh_2"
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      "outputs": []
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      "source": [
        "#calucating the mean and std deviation of total_bill(column)\n",
        "mean = np.mean(total_bill)\n",
        "std = np.std(total_bill)"
      ],
      "metadata": {
        "id": "0wjSPvK8HsEw"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "mean, std"
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              "(19.78594262295082, 8.884150577771132)"
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          "execution_count": 6
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    {
      "cell_type": "code",
      "source": [
        "#histogram plot of total_bill before standardization\n",
        "sns.histplot(total_bill)"
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              "<Axes: ylabel='Count'>"
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              "<Figure size 640x480 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Manual way of standardization\n",
        "normalized_data = []\n",
        "\n",
        "for i in total_bill:\n",
        "  z_score = (i - mean) / std\n",
        "  normalized_data.append(z_score)\n",
        "\n",
        "print(normalized_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vwPCy8oICfg",
        "outputId": "9f8929ac-bb5c-40c8-95b1-1afeda0b4afc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.3147113050904943, -1.0632353132988692, 0.13777989987156145, 0.43831510316725475, 0.540744704290506, 0.6195367051545455, -1.2399545152367863, 0.7985071071171495, -0.5342033074974614, -0.5634689078183903, -1.0711145133852733, 1.7417599174609364, -0.49143050702841123, -0.15262490331304146, -0.557840907756673, 0.2019391005751361, -1.0643609133112126, -0.3935033059545337, -0.31696250511518104, 0.09725829942719795, -0.2100305039425557, 0.05673669898283484, -0.45203450659639155, 2.2100095225958003, 0.003833498402694168, -0.2224121040783337, -0.7221785095588127, -0.7987193103981653, 0.21544630072325727, -0.015301701807144186, -1.1521577142739994, -0.16162970341178864, -0.5319521074727743, 0.10176069947657193, -0.22578890411536368, 0.4810879036363046, -0.3912521059298469, -0.32146490516455467, -0.12335930299211233, 1.2926455125359115, -0.4216433062631192, -0.2618081045103532, -0.6580193088552376, -1.137524914113535, 1.1947183114620337, -0.16838330348584943, 0.2751031013774587, 1.419838313930718, 0.986482309178501, -0.19652330379443494, -0.8156033105833167, -1.0688633133605865, 1.691107916905483, -1.1082593137926062, 0.6499279054878179, -0.03331130200463894, 2.051299920855377, 0.7456039065370088, -0.9619313121879614, 3.206165533519728, 0.05673669898283484, -0.672652109015702, -0.9866945124595167, -0.16838330348584943, -0.24717530434988882, 0.0330990987236229, -0.37549370575703894, -1.8815465222725365, 0.049983098908774455, -0.5375801075344916, -0.8741345112251745, -0.3057065049917467, 0.7962559070924626, 0.6184111051422023, -0.5690969078801073, -1.044100113089031, -0.2100305039425557, 0.834526307512139, 0.3347599020316602, -0.2809433047201916, -0.03893930206635573, -0.351856105497827, -1.0936265136321417, 1.451355114276334, -0.4283969063371796, 1.6933591169301694, -0.760448909978489, -0.16950890349819261, 0.5542519044386273, 0.1546639000567126, 1.0337575096969245, 0.3043687016983874, -1.5798857189644997, -0.3901265059175033, 0.3336343020193166, 2.294429523521557, 0.8435311076108866, -0.8730089112128311, 0.13777989987156145, -0.8246081106820639, -0.9495497120521837, -0.4959329070777848, 2.759302328619389, 0.29648950161198384, 0.12764949976047066, -0.49818410710247185, 0.07924869922970319, 0.6105319050557984, -0.1740113035475666, -0.616372108398531, -0.6512657087811771, -1.4110457171129864, 2.058053520929438, 0.46870630350052706, 0.6668119056729693, -0.27869210469550476, 1.141815110881893, -1.0283417129162231, -0.8279849107190942, 0.4833391036609914, -0.9112793116325074, -0.7165505094970955, -0.6220001084602481, -0.43177370637421, -0.822356910657377, 1.1271823107214287, -1.268094515545372, -0.5927345081393192, -0.9461729120151534, 0.34151350210572057, -0.07946090251071923, 0.05448549895814805, -0.9698105122743653, -0.8471201109289324, -0.1717601035228794, -1.2692201155577154, -1.0643609133112126, -0.6343817085960257, -0.4261457063124928, -0.7458161098180245, -0.26068250449801, 1.6337023162759678, 2.4092407247805854, 0.8176423073269878, -0.3777449057817257, -1.2872297157552102, -0.12898730305382952, -0.8910185114103258, -1.126268913990101, -1.3817801167920576, -0.6433865086947731, -0.7491929098550546, -0.2843201047572215, 0.5362423042411325, -0.0017945016590230187, 1.1328103107831458, 3.194909533396294, 0.5868943047965863, -0.7199273095341256, -0.3709913057076653, 0.1929343004763889, -0.8020961104351955, -0.40250810605328086, -0.672652109015702, -0.25618010444863604, 0.5328655042041021, 0.10963989956297591, 1.3421719130790222, -1.0350953129902838, -1.03059291294091, 3.492067936654957, -0.44753210654701775, -1.4110457171129864, 1.3579303132518301, -0.3338465053003322, 1.476118314547889, -0.2134073039795861, -0.5972369081886928, -1.1465297142122826, 1.6708471166833014, 1.6730983167079878, 0.39891910273523484, 2.877490329915449, 0.3809095025377405, 2.3372023239906063, 0.10176069947657193, 0.12539829973578348, 1.2014719115360946, -0.18414170365865737, 0.3730303024513365, -0.46103930669513893, 0.0027078983903505707, 0.9741007090427235, -0.4846769069543507, -0.3608609055965746, -1.3761521167303405, -1.0632353132988692, 2.6253559271505225, -0.7638257100155192, -0.7064201093860047, -0.12110810296742554, -0.7930913103364481, -0.7638257100155192, -0.3811217058187561, 0.08375109927907717, -0.3732425057323521, 0.7658647067591904, 2.1323431217441033, 0.5047255038955165, -0.7908401103117614, 1.1564479110423573, 0.6870727058951509, 3.212919133593788, -0.7334345096822469, 0.9437095087094511, -0.7750817101389533, 0.9414583086847639, -0.9225353117559416, -1.3558913165081588, 1.165452711141105, -0.8583761110523666, -0.7165505094970955, -1.2613409154713113, -0.4283969063371796, -0.7165505094970955, -0.39575450597922046, -1.0913753136074549, 0.0747462991803296, -0.7323089096699035, 0.26272150124168114, 0.47545990357458784, -0.46103930669513893, -0.9202841117312548, -1.0148345127681022, -0.4790489068926337, -1.0936265136321417, -0.8088497105092561, 1.468239114461485, 1.8059191181645116, 1.0405111097709854, 0.8322751074874521, 0.3246295019205694, -0.2212865040659901, -0.11322890288102155]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(normalized_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "QSURFUmQMH_b",
        "outputId": "fc06b7a1-8fd3-4a5e-a170-73c793f03d24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler #importing standardization library from sklearn\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BjMnECp1I1oL",
        "outputId": "bb5f7304-5360-443a-a8b0-73227cd5ce80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ],
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              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
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              "      <th>size</th>\n",
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              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-adc281cf-121c-477a-8c95-9b697d2cf87e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-adc281cf-121c-477a-8c95-9b697d2cf87e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-adc281cf-121c-477a-8c95-9b697d2cf87e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-25bd1011-ec5d-4359-a675-a96afdae35e2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 244,\n  \"fields\": [\n    {\n      \"column\": \"total_bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.902411954856856,\n        \"min\": 3.07,\n        \"max\": 50.81,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          22.12,\n          20.23,\n          14.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3836381890011822,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          3.35,\n          1.5,\n          6.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Sat\",\n          \"Fri\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Lunch\",\n          \"Dinner\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "taIspBX5JSEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "a8XyowwxJik9",
        "outputId": "d8ffc405-8030-4b50-e1c4-409b1f403b92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#it fit the model to find the required calucation for using the model ie. here, mean and std will calculate\n",
        "scaler.fit(df[[\"total_bill\", \"tip\"]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "y23p2269JjjH",
        "outputId": "5f2808df-2093-452e-b4ef-d8def2d5fea3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#transform the data into more suitable form required for the model\n",
        "scaler_fit = scaler.transform(df[[\"total_bill\", \"tip\"]]) #it apply the z-score formula to whole required dataset"
      ],
      "metadata": {
        "id": "HdZDCZypJslJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler_fit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6Xm0hNPNpSW",
        "outputId": "e27b5909-90af-4600-f282-cd0359bbac98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [-7.16550509e-01,  3.48871182e-01],\n",
              "       [-1.26134092e+00, -7.80908694e-01],\n",
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              "       [-3.95754506e-01, -3.60862330e-01],\n",
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              "       [ 7.47462992e-02,  1.24660453e-03],\n",
              "       [-7.32308910e-01, -2.01534399e-01],\n",
              "       [ 2.62721501e-01, -8.56595397e-02],\n",
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              "       [-4.61039307e-01,  1.24660453e-03],\n",
              "       [-9.20284112e-01,  2.83691573e-01],\n",
              "       [-1.01483451e+00, -1.10680673e+00],\n",
              "       [-4.79048907e-01,  1.24660453e-03],\n",
              "       [-1.09362651e+00, -1.26613467e+00],\n",
              "       [-8.08849711e-01, -1.44718913e+00],\n",
              "       [ 1.46823911e+00, -1.32407210e+00],\n",
              "       [ 1.80591912e+00,  1.21069045e+00],\n",
              "       [ 1.04051111e+00,  2.11596278e+00],\n",
              "       [ 8.32275107e-01, -7.22971264e-01],\n",
              "       [ 3.24629502e-01, -7.22971264e-01],\n",
              "       [-2.21286504e-01, -9.04025732e-01],\n",
              "       [-1.13228903e-01,  1.24660453e-03]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(scaler.transform(df[[\"total_bill\", \"tip\"]]), columns = [\"total_bill\", \"tips\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "FNDjuwGBL7Ff",
        "outputId": "46eafe3e-56ea-43fd-f063-d3b8adee3917"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     total_bill      tips\n",
              "0     -0.314711 -1.439947\n",
              "1     -1.063235 -0.969205\n",
              "2      0.137780  0.363356\n",
              "3      0.438315  0.225754\n",
              "4      0.540745  0.443020\n",
              "..          ...       ...\n",
              "239    1.040511  2.115963\n",
              "240    0.832275 -0.722971\n",
              "241    0.324630 -0.722971\n",
              "242   -0.221287 -0.904026\n",
              "243   -0.113229  0.001247\n",
              "\n",
              "[244 rows x 2 columns]"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 244,\n  \"fields\": [\n    {\n      \"column\": \"total_bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.00205550062731,\n        \"min\": -1.8815465222725365,\n        \"max\": 3.492067936654957,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          0.26272150124168114,\n          0.049983098908774455,\n          -0.5634689078183903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tips\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0020555006273097,\n        \"min\": -1.4471891333585338,\n        \"max\": 5.070771687133162,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          0.2547228586587423,\n          -1.0850801988867729,\n          2.702579255687846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(scaler_fit)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "P6h2nO5oNq-t",
        "outputId": "193c1177-dc2a-495d-e8ab-2ef024a1fcc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1) Feature Scaling**\n",
        "- we normalize/standardize the dataset far faster than calculation\n",
        "- normalization, standardization, unit vector is used\n",
        "\n",
        "### **2) Normalization - Mean Max Scaler**\n",
        "- scaling between 0 <----> 1\n",
        "- used in specifically deep learning"
      ],
      "metadata": {
        "id": "4RTVybR2LU_z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"taxis\")"
      ],
      "metadata": {
        "id": "NhJrNhzjMg1H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "XvzNeUYeMWhQ",
        "outputId": "c7373b0c-187d-4781-b5bd-3a0a47a4f2b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               pickup             dropoff  passengers  distance  fare   tip  \\\n",
              "0 2019-03-23 20:21:09 2019-03-23 20:27:24           1      1.60   7.0  2.15   \n",
              "1 2019-03-04 16:11:55 2019-03-04 16:19:00           1      0.79   5.0  0.00   \n",
              "2 2019-03-27 17:53:01 2019-03-27 18:00:25           1      1.37   7.5  2.36   \n",
              "3 2019-03-10 01:23:59 2019-03-10 01:49:51           1      7.70  27.0  6.15   \n",
              "4 2019-03-30 13:27:42 2019-03-30 13:37:14           3      2.16   9.0  1.10   \n",
              "\n",
              "   tolls  total   color      payment            pickup_zone  \\\n",
              "0    0.0  12.95  yellow  credit card        Lenox Hill West   \n",
              "1    0.0   9.30  yellow         cash  Upper West Side South   \n",
              "2    0.0  14.16  yellow  credit card          Alphabet City   \n",
              "3    0.0  36.95  yellow  credit card              Hudson Sq   \n",
              "4    0.0  13.40  yellow  credit card           Midtown East   \n",
              "\n",
              "            dropoff_zone pickup_borough dropoff_borough  \n",
              "0    UN/Turtle Bay South      Manhattan       Manhattan  \n",
              "1  Upper West Side South      Manhattan       Manhattan  \n",
              "2           West Village      Manhattan       Manhattan  \n",
              "3         Yorkville West      Manhattan       Manhattan  \n",
              "4         Yorkville West      Manhattan       Manhattan  "
            ],
            "text/html": [
              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pickup</th>\n",
              "      <th>dropoff</th>\n",
              "      <th>passengers</th>\n",
              "      <th>distance</th>\n",
              "      <th>fare</th>\n",
              "      <th>tip</th>\n",
              "      <th>tolls</th>\n",
              "      <th>total</th>\n",
              "      <th>color</th>\n",
              "      <th>payment</th>\n",
              "      <th>pickup_zone</th>\n",
              "      <th>dropoff_zone</th>\n",
              "      <th>pickup_borough</th>\n",
              "      <th>dropoff_borough</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-03-23 20:21:09</td>\n",
              "      <td>2019-03-23 20:27:24</td>\n",
              "      <td>1</td>\n",
              "      <td>1.60</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.95</td>\n",
              "      <td>yellow</td>\n",
              "      <td>credit card</td>\n",
              "      <td>Lenox Hill West</td>\n",
              "      <td>UN/Turtle Bay South</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Manhattan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-03-04 16:11:55</td>\n",
              "      <td>2019-03-04 16:19:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.79</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.30</td>\n",
              "      <td>yellow</td>\n",
              "      <td>cash</td>\n",
              "      <td>Upper West Side South</td>\n",
              "      <td>Upper West Side South</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Manhattan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-03-27 17:53:01</td>\n",
              "      <td>2019-03-27 18:00:25</td>\n",
              "      <td>1</td>\n",
              "      <td>1.37</td>\n",
              "      <td>7.5</td>\n",
              "      <td>2.36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.16</td>\n",
              "      <td>yellow</td>\n",
              "      <td>credit card</td>\n",
              "      <td>Alphabet City</td>\n",
              "      <td>West Village</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Manhattan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-03-10 01:23:59</td>\n",
              "      <td>2019-03-10 01:49:51</td>\n",
              "      <td>1</td>\n",
              "      <td>7.70</td>\n",
              "      <td>27.0</td>\n",
              "      <td>6.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36.95</td>\n",
              "      <td>yellow</td>\n",
              "      <td>credit card</td>\n",
              "      <td>Hudson Sq</td>\n",
              "      <td>Yorkville West</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Manhattan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-03-30 13:27:42</td>\n",
              "      <td>2019-03-30 13:37:14</td>\n",
              "      <td>3</td>\n",
              "      <td>2.16</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.40</td>\n",
              "      <td>yellow</td>\n",
              "      <td>credit card</td>\n",
              "      <td>Midtown East</td>\n",
              "      <td>Yorkville West</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Manhattan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6433,\n  \"fields\": [\n    {\n      \"column\": \"pickup\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-02-28 23:29:03\",\n        \"max\": \"2019-03-31 23:43:45\",\n        \"num_unique_values\": 6414,\n        \"samples\": [\n          \"2019-03-11 21:32:20\",\n          \"2019-03-01 11:36:49\",\n          \"2019-03-24 19:36:52\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dropoff\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2019-02-28 23:32:35\",\n        \"max\": \"2019-04-01 00:13:58\",\n        \"num_unique_values\": 6425,\n        \"samples\": [\n          \"2019-03-13 22:53:10\",\n          \"2019-03-12 23:25:20\",\n          \"2019-03-04 04:08:04\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.827867001011754,\n        \"min\": 0.0,\n        \"max\": 36.7,\n        \"num_unique_values\": 1079,\n        \"samples\": [\n          2.08,\n          19.43,\n          16.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.551804266414893,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 220,\n        \"samples\": [\n          143.5,\n          16.39,\n          44.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.448559591636065,\n        \"min\": 0.0,\n        \"max\": 33.2,\n        \"num_unique_values\": 489,\n        \"samples\": [\n          3.21,\n          7.31,\n          6.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tolls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4152673297904557,\n        \"min\": 0.0,\n        \"max\": 24.02,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          0.0,\n          5.76,\n          17.28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.815570041066861,\n        \"min\": 1.3,\n        \"max\": 174.82,\n        \"num_unique_values\": 898,\n        \"samples\": [\n          4.8,\n          42.0,\n          50.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"green\",\n          \"yellow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"payment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"cash\",\n          \"credit card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pickup_zone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"University Heights/Morris Heights\",\n          \"Gramercy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dropoff_zone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 203,\n        \"samples\": [\n          \"East Chelsea\",\n          \"Astoria\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pickup_borough\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Queens\",\n          \"Brooklyn\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dropoff_borough\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Queens\",\n          \"Staten Island\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "MtakeldkMXE7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_max = MinMaxScaler()"
      ],
      "metadata": {
        "id": "VUWXOHYTMc_t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fitting the model to find the required calculations\n",
        "min_max.fit(df[[\"distance\", \"fare\", \"tip\"]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "ajdMBpTlMg7d",
        "outputId": "711f75f0-427a-43b4-a111-1f8e449b0479"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#it transform the data into normalize form ie. 0 <------> 1\n",
        "min_max.transform(df[[\"distance\", \"fare\", \"tip\"]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KACxR8KKM2cO",
        "outputId": "a3c76f11-4660-4082-af30-31bd8f9522dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04359673, 0.04026846, 0.06475904],\n",
              "       [0.02152589, 0.02684564, 0.        ],\n",
              "       [0.0373297 , 0.04362416, 0.07108434],\n",
              "       ...,\n",
              "       [0.11280654, 0.10067114, 0.        ],\n",
              "       [0.03051771, 0.03355705, 0.        ],\n",
              "       [0.10490463, 0.09395973, 0.10120482]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#direct way to fit and transform the data\n",
        "min_max.fit_transform(df[[\"distance\", \"fare\", \"tip\"]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LI9mO-QsM9Za",
        "outputId": "344ea2c8-4ffe-4b1c-c2f1-49163ecfead0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04359673, 0.04026846, 0.06475904],\n",
              "       [0.02152589, 0.02684564, 0.        ],\n",
              "       [0.0373297 , 0.04362416, 0.07108434],\n",
              "       ...,\n",
              "       [0.11280654, 0.10067114, 0.        ],\n",
              "       [0.03051771, 0.03355705, 0.        ],\n",
              "       [0.10490463, 0.09395973, 0.10120482]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1) Feature Scaling**\n",
        "- we normalize/standardize the dataset far faster than calculation\n",
        "- normalization, standardization, unit vector is used\n",
        "### **3) Unit Vector**\n",
        "- scaling down whole data in magnitude of 1 then, it is called as unit vector\n",
        "- used in machine learning\n",
        "- finding the magnitude of 1"
      ],
      "metadata": {
        "id": "txvpvvULPop1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"iris\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EprvHZZMNG13",
        "outputId": "c97feaed-c316-4509-933b-6a426ecc6c39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import the respected library to unit vector\n",
        "from sklearn.preprocessing import normalize"
      ],
      "metadata": {
        "id": "jgauFYGoP9Dr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "za7RRRBJQI8N",
        "outputId": "40f12c88-2d60-4f82-f1f6-42c7f3ade89e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
              "       'species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normalize_df = normalize(df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']])"
      ],
      "metadata": {
        "id": "Tkb4WsaQQMH_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "normalize_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fkSRz3G_Qs-i",
        "outputId": "7aa17884-7379-48cd-9a7c-560a6a149a7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0.69025916, 0.35097923, 0.5966647 , 0.21058754]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(normalize_df,\n",
        "             columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ij9lulq4QWPt",
        "outputId": "626d4990-6674-44c7-b25e-97ee7a6e9f1c"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width\n",
              "0        0.803773     0.551609      0.220644     0.031521\n",
              "1        0.828133     0.507020      0.236609     0.033801\n",
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              "..            ...          ...           ...          ...\n",
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              "147      0.716539     0.330710      0.573231     0.220474\n",
              "148      0.674671     0.369981      0.587616     0.250281\n",
              "149      0.690259     0.350979      0.596665     0.210588\n",
              "\n",
              "[150 rows x 4 columns]"
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5a7952e7-1dff-405b-956c-203b5cc7f388 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5a7952e7-1dff-405b-956c-203b5cc7f388');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-eb479634-871d-48cc-9ebc-a9fc9a054f79\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eb479634-871d-48cc-9ebc-a9fc9a054f79')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eb479634-871d-48cc-9ebc-a9fc9a054f79 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"             columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04436758673145787,\n        \"min\": 0.6538774709871312,\n        \"max\": 0.8609385732675535,\n        \"num_unique_values\": 149,\n        \"samples\": [\n          0.7365989486022555,\n          0.8068282029952023,\n          0.6929909911756716\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10562375953321267,\n        \"min\": 0.23839169677842423,\n        \"max\": 0.6071249316193964,\n        \"num_unique_values\": 149,\n        \"samples\": [\n          0.3381109928010353,\n          0.5378854686634681,\n          0.3419955540866951\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15998633782645458,\n        \"min\": 0.16783627165933782,\n        \"max\": 0.63698084607488,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          0.5634982872515155,\n          0.5241779763419614,\n          0.5850498647534077\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07797683171561326,\n        \"min\": 0.014726598240177802,\n        \"max\": 0.28041899416175325,\n        \"num_unique_values\": 149,\n        \"samples\": [\n          0.1449047112004437,\n          0.04246464226290538,\n          0.1979974260501919\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    }
  ]
}