Stack2_Food_Sales_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Kamal-Moha/Food-Sales-Predictions/blob/main/Stack2_Food_Sales_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import necessary libraries**\n"
      ],
      "metadata": {
        "id": "SAXqtesVXgjp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OLQzl7QByvZW"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import make_column_transformer, make_column_selector\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import set_config\n",
        "set_config(display='diagram')\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explore the Data**"
      ],
      "metadata": {
        "id": "4UdIZg_2b-wt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "pZkRO3YMzBqn",
        "outputId": "cd457d81-9f8b-4f3f-dec3-4864e359802f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8e0b6e0d-55f7-42ad-b2e4-312a63622e4f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e0b6e0d-55f7-42ad-b2e4-312a63622e4f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-8e0b6e0d-55f7-42ad-b2e4-312a63622e4f 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-8e0b6e0d-55f7-42ad-b2e4-312a63622e4f');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
              "0           FDA15         9.30          Low Fat         0.016047   \n",
              "1           DRC01         5.92          Regular         0.019278   \n",
              "2           FDN15        17.50          Low Fat         0.016760   \n",
              "3           FDX07        19.20          Regular         0.000000   \n",
              "4           NCD19         8.93          Low Fat         0.000000   \n",
              "\n",
              "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
              "0                  Dairy  249.8092            OUT049   \n",
              "1            Soft Drinks   48.2692            OUT018   \n",
              "2                   Meat  141.6180            OUT049   \n",
              "3  Fruits and Vegetables  182.0950            OUT010   \n",
              "4              Household   53.8614            OUT013   \n",
              "\n",
              "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
              "0                       1999      Medium               Tier 1   \n",
              "1                       2009      Medium               Tier 3   \n",
              "2                       1999      Medium               Tier 1   \n",
              "3                       1998         NaN               Tier 3   \n",
              "4                       1987        High               Tier 3   \n",
              "\n",
              "         Outlet_Type  Item_Outlet_Sales  \n",
              "0  Supermarket Type1          3735.1380  \n",
              "1  Supermarket Type2           443.4228  \n",
              "2  Supermarket Type1          2097.2700  \n",
              "3      Grocery Store           732.3800  \n",
              "4  Supermarket Type1           994.7052  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "filename = '/content/drive/MyDrive/CODING DOJO DS BOOTCAMP/Stack 1 - Data Science Fundamentals/02 Week 2: Pandas/Assignments/Core Assignments/sales_predictions.csv'\n",
        "df = pd.read_csv(filename)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_UxdIfGzz0R",
        "outputId": "b4eab4cd-d2fc-4fea-b0b4-a058c15e84f2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Medium    2793\n",
              "Small     2388\n",
              "High       932\n",
              "Name: Outlet_Size, dtype: int64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Outlet_Size'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcLYrlBIZDAe"
      },
      "source": [
        "**Ordinal Encoding**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bq5UennmZGdo"
      },
      "outputs": [],
      "source": [
        "# We can see Outlet_Size has Ordinal values that we can represent as numbers\n",
        "replace_dict = {'Small':0, 'Medium':1, 'High':2}\n",
        "df['Outlet_Size'].replace(replace_dict, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RaIXsGG_ZGjj",
        "outputId": "083bacf9-f560-45cb-ea4d-59ceb47a2177"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0    2793\n",
              "0.0    2388\n",
              "2.0     932\n",
              "Name: Outlet_Size, dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Outlet_Size'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Validation Split**"
      ],
      "metadata": {
        "id": "fx-_dwwNcG0d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BOVrUso4agdg"
      },
      "outputs": [],
      "source": [
        "# Item_Outlet_Sales is our target vector\n",
        "\n",
        "# Creating the target vector\n",
        "y = df['Item_Outlet_Sales']\n",
        "# Creating the Feature Matrix\n",
        "X = df.drop(columns='Item_Outlet_Sales')\n",
        "# Train/Test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Instantiate Column Selectors**"
      ],
      "metadata": {
        "id": "MWnypJhecVjq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-eAswr-Va2wd"
      },
      "outputs": [],
      "source": [
        "# Separating our columns by dtype\n",
        "cat_selector = make_column_selector(dtype_include='object')\n",
        "num_selector = make_column_selector(dtype_include='number')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Instantiate Transformers**"
      ],
      "metadata": {
        "id": "L69HPNwuX9I-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "uyT7BJWSfVUK"
      },
      "outputs": [],
      "source": [
        "\n",
        "# median simpleImputer\n",
        "median_imputer = SimpleImputer(strategy='median')\n",
        "\n",
        "# OneHotEncoder\n",
        "ohe = OneHotEncoder(sparse=False, handle_unknown='ignore')\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Instantiate Column Transformers**"
      ],
      "metadata": {
        "id": "YVSknooKcjCu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "QzHbvLRCf0oJ"
      },
      "outputs": [],
      "source": [
        "# Making tuples for column transfomation\n",
        "num_tuple = (median_imputer, num_selector)\n",
        "cat_tuple = (ohe, cat_selector)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating col_transformer\n",
        "col_transformer = make_column_transformer(num_tuple, cat_tuple, remainder='passthrough')\n",
        "col_transformer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "zX1T-y0jY2Bo",
        "outputId": "414b7357-9333-4f5a-c0c8-84f305631d9f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(remainder='passthrough',\n",
              "                  transformers=[('simpleimputer',\n",
              "                                 SimpleImputer(strategy='median'),\n",
              "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2283a0>),\n",
              "                                ('onehotencoder',\n",
              "                                 OneHotEncoder(handle_unknown='ignore',\n",
              "                                               sparse=False),\n",
              "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2bb970>)])"
            ],
            "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>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;simpleimputer&#x27;,\n",
              "                                 SimpleImputer(strategy=&#x27;median&#x27;),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2283a0&gt;),\n",
              "                                (&#x27;onehotencoder&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                               sparse=False),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2bb970&gt;)])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;simpleimputer&#x27;,\n",
              "                                 SimpleImputer(strategy=&#x27;median&#x27;),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2283a0&gt;),\n",
              "                                (&#x27;onehotencoder&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                               sparse=False),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2bb970&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">simpleimputer</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2283a0&gt;</pre></div></div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2bb970&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Transform the Data**"
      ],
      "metadata": {
        "id": "hKSreagibwxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit X_train data\n",
        "col_transformer.fit(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "i75pFrznZICn",
        "outputId": "f3a92694-51c0-4d05-c641-b1c8c494df13"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/preprocessing/_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(remainder='passthrough',\n",
              "                  transformers=[('simpleimputer',\n",
              "                                 SimpleImputer(strategy='median'),\n",
              "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2283a0>),\n",
              "                                ('onehotencoder',\n",
              "                                 OneHotEncoder(handle_unknown='ignore',\n",
              "                                               sparse=False),\n",
              "                                 <sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2bb970>)])"
            ],
            "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>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;simpleimputer&#x27;,\n",
              "                                 SimpleImputer(strategy=&#x27;median&#x27;),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2283a0&gt;),\n",
              "                                (&#x27;onehotencoder&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                               sparse=False),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2bb970&gt;)])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;simpleimputer&#x27;,\n",
              "                                 SimpleImputer(strategy=&#x27;median&#x27;),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2283a0&gt;),\n",
              "                                (&#x27;onehotencoder&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                               sparse=False),\n",
              "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2bb970&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">simpleimputer</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2283a0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x7ffa2a2bb970&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Doing the actual transformation on X_train & X_test\n",
        "X_train_processed = col_transformer.transform(X_train)\n",
        "X_test_processed = col_transformer.transform(X_test)"
      ],
      "metadata": {
        "id": "bYst_LthZQiS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inspect the Results**"
      ],
      "metadata": {
        "id": "eNItxF-vaUI-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values and that data is scaled and one-hot encoded\n",
        "print(np.isnan(X_train_processed).sum().sum(), 'missing values in training data')\n",
        "print(np.isnan(X_test_processed).sum().sum(), 'missing values in testing data')\n",
        "print('\\n')\n",
        "print('All data in X_train_processed are', X_train_processed.dtype)\n",
        "print('All data in X_test_processed are', X_test_processed.dtype)\n",
        "print('\\n')\n",
        "print('shape of data is', X_train_processed.shape)\n",
        "print('\\n')\n",
        "X_train_processed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMG5vS0eaMM8",
        "outputId": "5229b771-2096-49b8-eedf-39e7043121a4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 missing values in training data\n",
            "0 missing values in testing data\n",
            "\n",
            "\n",
            "All data in X_train_processed are float64\n",
            "All data in X_test_processed are float64\n",
            "\n",
            "\n",
            "shape of data is (6392, 1593)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.63500000e+01, 2.95653090e-02, 2.56464600e+02, ...,\n",
              "        0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n",
              "       [1.52500000e+01, 0.00000000e+00, 1.79766000e+02, ...,\n",
              "        0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n",
              "       [1.23500000e+01, 1.58715731e-01, 1.57294600e+02, ...,\n",
              "        1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
              "       ...,\n",
              "       [1.76000000e+01, 1.89436660e-02, 2.37359000e+02, ...,\n",
              "        1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
              "       [2.03500000e+01, 5.43626950e-02, 1.17946600e+02, ...,\n",
              "        1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
              "       [1.63500000e+01, 1.69932040e-02, 9.57410000e+01, ...,\n",
              "        1.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see from the shape of the data that our columns massively increased and that's because of the **OneHotEncoder**"
      ],
      "metadata": {
        "id": "PFZjMUaQbVJh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Re-Creating back our DataFrame**"
      ],
      "metadata": {
        "id": "uweuDOxHZ8ry"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting actual names \n",
        "processed_names = col_transformer.get_feature_names_out()\n",
        "names = [i.split('__')[-1] for i in processed_names]\n",
        "\n",
        "# Re-Creating the DataFrame after transformation\n",
        "X_train_processed_df = pd.DataFrame(X_train_processed, columns=names)\n",
        "X_train_processed_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "jwDw17PfZuHk",
        "outputId": "d6f589f3-825d-49e5-a2b4-524e08a07377"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year  \\\n",
              "0       16.350         0.029565  256.4646                     2009.0   \n",
              "1       15.250         0.000000  179.7660                     2009.0   \n",
              "2       12.350         0.158716  157.2946                     1999.0   \n",
              "3        7.975         0.014628   82.3250                     2004.0   \n",
              "4       19.350         0.016645  120.9098                     2002.0   \n",
              "\n",
              "   Outlet_Size  Item_Identifier_DRA12  Item_Identifier_DRA24  \\\n",
              "0          1.0                    0.0                    0.0   \n",
              "1          1.0                    0.0                    0.0   \n",
              "2          1.0                    0.0                    0.0   \n",
              "3          0.0                    0.0                    0.0   \n",
              "4          1.0                    0.0                    0.0   \n",
              "\n",
              "   Item_Identifier_DRA59  Item_Identifier_DRB01  Item_Identifier_DRB13  ...  \\\n",
              "0                    0.0                    0.0                    0.0  ...   \n",
              "1                    0.0                    0.0                    0.0  ...   \n",
              "2                    0.0                    0.0                    0.0  ...   \n",
              "3                    0.0                    0.0                    0.0  ...   \n",
              "4                    0.0                    0.0                    0.0  ...   \n",
              "\n",
              "   Outlet_Identifier_OUT045  Outlet_Identifier_OUT046  \\\n",
              "0                       0.0                       0.0   \n",
              "1                       0.0                       0.0   \n",
              "2                       0.0                       0.0   \n",
              "3                       0.0                       0.0   \n",
              "4                       1.0                       0.0   \n",
              "\n",
              "   Outlet_Identifier_OUT049  Outlet_Location_Type_Tier 1  \\\n",
              "0                       0.0                          0.0   \n",
              "1                       0.0                          0.0   \n",
              "2                       1.0                          1.0   \n",
              "3                       0.0                          0.0   \n",
              "4                       0.0                          0.0   \n",
              "\n",
              "   Outlet_Location_Type_Tier 2  Outlet_Location_Type_Tier 3  \\\n",
              "0                          0.0                          1.0   \n",
              "1                          0.0                          1.0   \n",
              "2                          0.0                          0.0   \n",
              "3                          1.0                          0.0   \n",
              "4                          1.0                          0.0   \n",
              "\n",
              "   Outlet_Type_Grocery Store  Outlet_Type_Supermarket Type1  \\\n",
              "0                        0.0                            0.0   \n",
              "1                        0.0                            0.0   \n",
              "2                        0.0                            1.0   \n",
              "3                        0.0                            1.0   \n",
              "4                        0.0                            1.0   \n",
              "\n",
              "   Outlet_Type_Supermarket Type2  Outlet_Type_Supermarket Type3  \n",
              "0                            1.0                            0.0  \n",
              "1                            1.0                            0.0  \n",
              "2                            0.0                            0.0  \n",
              "3                            0.0                            0.0  \n",
              "4                            0.0                            0.0  \n",
              "\n",
              "[5 rows x 1593 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9916c179-84ae-4b1d-931a-73d14483d537\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Item_Identifier_DRA12</th>\n",
              "      <th>Item_Identifier_DRA24</th>\n",
              "      <th>Item_Identifier_DRA59</th>\n",
              "      <th>Item_Identifier_DRB01</th>\n",
              "      <th>Item_Identifier_DRB13</th>\n",
              "      <th>...</th>\n",
              "      <th>Outlet_Identifier_OUT045</th>\n",
              "      <th>Outlet_Identifier_OUT046</th>\n",
              "      <th>Outlet_Identifier_OUT049</th>\n",
              "      <th>Outlet_Location_Type_Tier 1</th>\n",
              "      <th>Outlet_Location_Type_Tier 2</th>\n",
              "      <th>Outlet_Location_Type_Tier 3</th>\n",
              "      <th>Outlet_Type_Grocery Store</th>\n",
              "      <th>Outlet_Type_Supermarket Type1</th>\n",
              "      <th>Outlet_Type_Supermarket Type2</th>\n",
              "      <th>Outlet_Type_Supermarket Type3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.350</td>\n",
              "      <td>0.029565</td>\n",
              "      <td>256.4646</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15.250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>179.7660</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.350</td>\n",
              "      <td>0.158716</td>\n",
              "      <td>157.2946</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.975</td>\n",
              "      <td>0.014628</td>\n",
              "      <td>82.3250</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19.350</td>\n",
              "      <td>0.016645</td>\n",
              "      <td>120.9098</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1593 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9916c179-84ae-4b1d-931a-73d14483d537')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9916c179-84ae-4b1d-931a-73d14483d537 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-9916c179-84ae-4b1d-931a-73d14483d537');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xUaUTVKoZ25j"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1-7VZPywr4Wb63VnEG9CWho00XLcR9g7b",
      "authorship_tag": "ABX9TyPTcgDD8qeQEGbPi8MifWo4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}