Sales_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVXbklWLb_Qm"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: numpy in /home/yash/.local/lib/python3.10/site-packages (1.26.3)\n",
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: pandas in /home/yash/.local/lib/python3.10/site-packages (2.2.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3/dist-packages (from pandas) (2022.1)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /home/yash/.local/lib/python3.10/site-packages (from pandas) (1.26.3)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.2 in /home/yash/.local/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: matplotlib in /home/yash/.local/lib/python3.10/site-packages (3.8.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /home/yash/.local/lib/python3.10/site-packages (from matplotlib) (1.2.0)\n",
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            "Requirement already satisfied: pillow>=8 in /usr/lib/python3/dist-packages (from matplotlib) (9.0.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /home/yash/.local/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n",
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            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: seaborn in /home/yash/.local/lib/python3.10/site-packages (0.13.2)\n",
            "Requirement already satisfied: pandas>=1.2 in /home/yash/.local/lib/python3.10/site-packages (from seaborn) (2.2.0)\n",
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            "Requirement already satisfied: pillow>=8 in /usr/lib/python3/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (9.0.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /home/yash/.local/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3/dist-packages (from pandas>=1.2->seaborn) (2022.1)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /home/yash/.local/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: scikit-learn in /home/yash/.local/lib/python3.10/site-packages (1.4.0)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /home/yash/.local/lib/python3.10/site-packages (from scikit-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/yash/.local/lib/python3.10/site-packages (from scikit-learn) (3.2.0)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /home/yash/.local/lib/python3.10/site-packages (from scikit-learn) (1.12.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.19.5 in /home/yash/.local/lib/python3.10/site-packages (from scikit-learn) (1.26.3)\n",
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: xgboost in /home/yash/.local/lib/python3.10/site-packages (2.0.3)\n",
            "Requirement already satisfied: scipy in /home/yash/.local/lib/python3.10/site-packages (from xgboost) (1.12.0)\n",
            "Requirement already satisfied: numpy in /home/yash/.local/lib/python3.10/site-packages (from xgboost) (1.26.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install numpy\n",
        "!pip install pandas\n",
        "!pip install matplotlib\n",
        "!pip install seaborn\n",
        "!pip install scikit-learn\n",
        "!pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "fwjseYJUbhIu"
      },
      "outputs": [],
      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6Qv_wKjrP1A"
      },
      "source": [
        "Data Collection and Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "AFU14Fvbcs8A"
      },
      "outputs": [],
      "source": [
        "# loading the data from csv file to Pandas DataFrame\n",
        "big_mart_data = pd.read_csv('./Train.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "22sDgYb0da9x",
        "outputId": "139415fc-5433-4320-9b54-d9e0dc3158f1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# first 5 rows of the dataframe\n",
        "big_mart_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t_HuzdTTdiqb",
        "outputId": "6eed5dea-94dd-4285-af7f-13b41a3b106a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(8523, 12)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# number of data points & number of features\n",
        "big_mart_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzjdyEigd0GM",
        "outputId": "f7e1f5b5-7a95-4d94-97c2-8a8901e2b8b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8523 entries, 0 to 8522\n",
            "Data columns (total 12 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   Item_Identifier            8523 non-null   object \n",
            " 1   Item_Weight                7060 non-null   float64\n",
            " 2   Item_Fat_Content           8523 non-null   object \n",
            " 3   Item_Visibility            8523 non-null   float64\n",
            " 4   Item_Type                  8523 non-null   object \n",
            " 5   Item_MRP                   8523 non-null   float64\n",
            " 6   Outlet_Identifier          8523 non-null   object \n",
            " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
            " 8   Outlet_Size                6113 non-null   object \n",
            " 9   Outlet_Location_Type       8523 non-null   object \n",
            " 10  Outlet_Type                8523 non-null   object \n",
            " 11  Item_Outlet_Sales          8523 non-null   float64\n",
            "dtypes: float64(4), int64(1), object(7)\n",
            "memory usage: 799.2+ KB\n"
          ]
        }
      ],
      "source": [
        "# getting some information about thye dataset\n",
        "big_mart_data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cncN8QXfeXm"
      },
      "source": [
        "Categorical Features:\n",
        "- Item_Identifier\n",
        "- Item_Fat_Content\n",
        "- Item_Type\n",
        "- Outlet_Identifier\n",
        "- Outlet_Size\n",
        "- Outlet_Location_Type\n",
        "- Outlet_Type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcsNUlTtej1f",
        "outputId": "0aab948e-1b31-4afe-9786-af6d7e8dce13"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                  1463\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  2410\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# checking for missing values\n",
        "big_mart_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-yzq6Tvkpak"
      },
      "source": [
        "Handling Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Iim8oBml0H6",
        "outputId": "c51e4463-01e8-4eea-ada8-eeddff190695"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "12.857645184135976"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# mean value of \"Item_Weight\" column\n",
        "big_mart_data['Item_Weight'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "HonvBOteewkc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_13701/2509980927.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  big_mart_data['Item_Weight'].fillna(big_mart_data['Item_Weight'].mean(), inplace=True)\n"
          ]
        }
      ],
      "source": [
        "# filling the missing values in \"Item_weight column\" with \"Mean\" value\n",
        "big_mart_data['Item_Weight'].fillna(big_mart_data['Item_Weight'].mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euBwdTyMnzCr",
        "outputId": "00877f0a-12d7-4f4f-fc61-66c0f6866cee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    Medium\n",
              "Name: Outlet_Size, dtype: object"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# mode of \"Outlet_Size\" column\n",
        "big_mart_data['Outlet_Size'].mode()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "2-DBh6DP-cA3"
      },
      "outputs": [],
      "source": [
        "# filling the missing values in \"Outlet_Size\" column with Mode\n",
        "mode_of_Outlet_size = big_mart_data.pivot_table(values='Outlet_Size', columns='Outlet_Type', aggfunc=(lambda x: x.mode()[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yo415g27-4wp",
        "outputId": "81cdc81c-add7-42cf-d7b0-5889b7e9c8fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2  \\\n",
            "Outlet_Size         Small             Small            Medium   \n",
            "\n",
            "Outlet_Type Supermarket Type3  \n",
            "Outlet_Size            Medium  \n"
          ]
        }
      ],
      "source": [
        "print(mode_of_Outlet_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "uMBQoiBC_NQf"
      },
      "outputs": [],
      "source": [
        "miss_values = big_mart_data['Outlet_Size'].isnull()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zyt9Y0dd_20a",
        "outputId": "b12842c7-042a-4df9-89c4-c6c6b7a6bab8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0       False\n",
            "1       False\n",
            "2       False\n",
            "3        True\n",
            "4       False\n",
            "        ...  \n",
            "8518    False\n",
            "8519     True\n",
            "8520    False\n",
            "8521    False\n",
            "8522    False\n",
            "Name: Outlet_Size, Length: 8523, dtype: bool\n"
          ]
        }
      ],
      "source": [
        "print(miss_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "torjizDt_lRK"
      },
      "outputs": [],
      "source": [
        "big_mart_data.loc[miss_values, 'Outlet_Size'] = big_mart_data.loc[miss_values,'Outlet_Type'].apply(lambda x: mode_of_Outlet_size[x])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oo1Jor0qme3C",
        "outputId": "b9560cba-5a41-43a4-b96d-93f4b0b82f0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Item_Identifier              0\n",
              "Item_Weight                  0\n",
              "Item_Fat_Content             0\n",
              "Item_Visibility              0\n",
              "Item_Type                    0\n",
              "Item_MRP                     0\n",
              "Outlet_Identifier            0\n",
              "Outlet_Establishment_Year    0\n",
              "Outlet_Size                  0\n",
              "Outlet_Location_Type         0\n",
              "Outlet_Type                  0\n",
              "Item_Outlet_Sales            0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# checking for missing values\n",
        "big_mart_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rIC7GbtbsBRd"
      },
      "source": [
        "Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "E4Vyd4StBoBE",
        "outputId": "93380e15-e762-421b-98f3-14c2d0f755d8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.857645</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>140.992782</td>\n",
              "      <td>1997.831867</td>\n",
              "      <td>2181.288914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.226124</td>\n",
              "      <td>0.051598</td>\n",
              "      <td>62.275067</td>\n",
              "      <td>8.371760</td>\n",
              "      <td>1706.499616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.555000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.290000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>33.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.310000</td>\n",
              "      <td>0.026989</td>\n",
              "      <td>93.826500</td>\n",
              "      <td>1987.000000</td>\n",
              "      <td>834.247400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.857645</td>\n",
              "      <td>0.053931</td>\n",
              "      <td>143.012800</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>1794.331000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.094585</td>\n",
              "      <td>185.643700</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>3101.296400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.350000</td>\n",
              "      <td>0.328391</td>\n",
              "      <td>266.888400</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>13086.964800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
              "count  8523.000000      8523.000000  8523.000000                8523.000000   \n",
              "mean     12.857645         0.066132   140.992782                1997.831867   \n",
              "std       4.226124         0.051598    62.275067                   8.371760   \n",
              "min       4.555000         0.000000    31.290000                1985.000000   \n",
              "25%       9.310000         0.026989    93.826500                1987.000000   \n",
              "50%      12.857645         0.053931   143.012800                1999.000000   \n",
              "75%      16.000000         0.094585   185.643700                2004.000000   \n",
              "max      21.350000         0.328391   266.888400                2009.000000   \n",
              "\n",
              "       Item_Outlet_Sales  \n",
              "count        8523.000000  \n",
              "mean         2181.288914  \n",
              "std          1706.499616  \n",
              "min            33.290000  \n",
              "25%           834.247400  \n",
              "50%          1794.331000  \n",
              "75%          3101.296400  \n",
              "max         13086.964800  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "big_mart_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GdDN9rvWuICQ"
      },
      "source": [
        "Numerical Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "1GKY5qyrtJqs"
      },
      "outputs": [],
      "source": [
        "sns.set()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "5rksM5MimhOF",
        "outputId": "52165cdb-7ff3-4dcb-86b7-973aa9b0e846"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_13701/1330319193.py:3: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(big_mart_data['Item_Weight'])\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Item_Weight distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(big_mart_data['Item_Weight'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "LE1yDwIxsOAl",
        "outputId": "4bffef8d-49dc-42be-df37-63540e75db5e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_13701/193435663.py:3: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(big_mart_data['Item_Visibility'])\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Item Visibility distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(big_mart_data['Item_Visibility'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 741
        },
        "id": "-qLPjmVSsYVN",
        "outputId": "0ba023d2-cb09-4ed6-f890-4c20f6f69740"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_13701/1610987680.py:3: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(big_mart_data['Item_MRP'])\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Item MRP distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(big_mart_data['Item_MRP'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "pEPbiUEjttpz",
        "outputId": "4d91e31e-a2cf-4d41-8bd7-e7b85977b0f0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_13701/1323853436.py:3: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(big_mart_data['Item_Outlet_Sales'])\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Item_Outlet_Sales distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(big_mart_data['Item_Outlet_Sales'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "S1PNqe_3uLfU",
        "outputId": "97a34bfe-7196-476e-c223-ba656261bde9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Outlet_Establishment_Year column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='Outlet_Establishment_Year', data=big_mart_data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGtSKeFnuNJy"
      },
      "source": [
        "Categorical Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "6hXxeo9dt-Js",
        "outputId": "72858770-2fa9-4354-d080-d4cd3032d63e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Item_Fat_Content column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='Item_Fat_Content', data=big_mart_data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "RHm0Sh8vuyKM",
        "outputId": "89f9c881-9ef0-4f50-d1e6-5b101e3bb9b7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 3000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Item_Type column\n",
        "plt.figure(figsize=(30,6))\n",
        "sns.countplot(x='Item_Type', data=big_mart_data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "nLkJIy3NF2Gd",
        "outputId": "20f43299-cb0c-4d20-f04f-a8b98a20f91a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Outlet_Size column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='Outlet_Size', data=big_mart_data)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "adBk67n8GjAH"
      },
      "source": [
        "Data Pre-Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "KhYlm6NiGYa6",
        "outputId": "ce3b72ef-21a5-4644-f11c-8655d52428f7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>Small</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>"
            ],
            "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       Small               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": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "big_mart_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i2N8zbcdGnUy",
        "outputId": "1d7b3041-ddec-4c31-f0bf-8edbd52d623a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Item_Fat_Content\n",
              "Low Fat    5089\n",
              "Regular    2889\n",
              "LF          316\n",
              "reg         117\n",
              "low fat     112\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "big_mart_data['Item_Fat_Content'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "29VZp_RaG2W-"
      },
      "outputs": [],
      "source": [
        "big_mart_data.replace({'Item_Fat_Content': {'low fat':'Low Fat','LF':'Low Fat', 'reg':'Regular'}}, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Ejg24CTHRXJ",
        "outputId": "d5ead8d6-b189-4590-ce7e-8f41dcea5f87"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Item_Fat_Content\n",
              "Low Fat    5517\n",
              "Regular    3006\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "big_mart_data['Item_Fat_Content'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTN9xWEnHhhQ"
      },
      "source": [
        "Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "SxHOpkH0Hc75"
      },
      "outputs": [],
      "source": [
        "encoder = LabelEncoder()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "qAwLCxCWHsl_"
      },
      "outputs": [],
      "source": [
        "big_mart_data['Item_Identifier'] = encoder.fit_transform(big_mart_data['Item_Identifier'])\n",
        "\n",
        "big_mart_data['Item_Fat_Content'] = encoder.fit_transform(big_mart_data['Item_Fat_Content'])\n",
        "\n",
        "big_mart_data['Item_Type'] = encoder.fit_transform(big_mart_data['Item_Type'])\n",
        "\n",
        "big_mart_data['Outlet_Identifier'] = encoder.fit_transform(big_mart_data['Outlet_Identifier'])\n",
        "\n",
        "big_mart_data['Outlet_Size'] = encoder.fit_transform(big_mart_data['Outlet_Size'])\n",
        "\n",
        "big_mart_data['Outlet_Location_Type'] = encoder.fit_transform(big_mart_data['Outlet_Location_Type'])\n",
        "\n",
        "big_mart_data['Outlet_Type'] = encoder.fit_transform(big_mart_data['Outlet_Type'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "SzL_IgO1IVUv",
        "outputId": "5d1df96a-84c0-4f14-afdf-4fafd67be212"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>156</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>4</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>9</td>\n",
              "      <td>1999</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>5.92</td>\n",
              "      <td>1</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>14</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>3</td>\n",
              "      <td>2009</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>662</td>\n",
              "      <td>17.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>10</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>9</td>\n",
              "      <td>1999</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1121</td>\n",
              "      <td>19.20</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>0</td>\n",
              "      <td>1998</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1297</td>\n",
              "      <td>8.93</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>1</td>\n",
              "      <td>1987</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
              "0              156         9.30                 0         0.016047          4   \n",
              "1                8         5.92                 1         0.019278         14   \n",
              "2              662        17.50                 0         0.016760         10   \n",
              "3             1121        19.20                 1         0.000000          6   \n",
              "4             1297         8.93                 0         0.000000          9   \n",
              "\n",
              "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
              "0  249.8092                  9                       1999            1   \n",
              "1   48.2692                  3                       2009            1   \n",
              "2  141.6180                  9                       1999            1   \n",
              "3  182.0950                  0                       1998            2   \n",
              "4   53.8614                  1                       1987            0   \n",
              "\n",
              "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
              "0                     0            1          3735.1380  \n",
              "1                     2            2           443.4228  \n",
              "2                     0            1          2097.2700  \n",
              "3                     2            0           732.3800  \n",
              "4                     2            1           994.7052  "
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "big_mart_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jr-0iqKOIdUD"
      },
      "source": [
        "Splitting features and Target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "uwe-H2o2IXzJ"
      },
      "outputs": [],
      "source": [
        "X = big_mart_data.drop(columns='Item_Outlet_Sales', axis=1)\n",
        "Y = big_mart_data['Item_Outlet_Sales']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKDLyvImIuTo",
        "outputId": "9e93bc62-7a85-4683-863f-a6eed1b6a817"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  \\\n",
            "0                 156        9.300                 0         0.016047   \n",
            "1                   8        5.920                 1         0.019278   \n",
            "2                 662       17.500                 0         0.016760   \n",
            "3                1121       19.200                 1         0.000000   \n",
            "4                1297        8.930                 0         0.000000   \n",
            "...               ...          ...               ...              ...   \n",
            "8518              370        6.865                 0         0.056783   \n",
            "8519              897        8.380                 1         0.046982   \n",
            "8520             1357       10.600                 0         0.035186   \n",
            "8521              681        7.210                 1         0.145221   \n",
            "8522               50       14.800                 0         0.044878   \n",
            "\n",
            "      Item_Type  Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  \\\n",
            "0             4  249.8092                  9                       1999   \n",
            "1            14   48.2692                  3                       2009   \n",
            "2            10  141.6180                  9                       1999   \n",
            "3             6  182.0950                  0                       1998   \n",
            "4             9   53.8614                  1                       1987   \n",
            "...         ...       ...                ...                        ...   \n",
            "8518         13  214.5218                  1                       1987   \n",
            "8519          0  108.1570                  7                       2002   \n",
            "8520          8   85.1224                  6                       2004   \n",
            "8521         13  103.1332                  3                       2009   \n",
            "8522         14   75.4670                  8                       1997   \n",
            "\n",
            "      Outlet_Size  Outlet_Location_Type  Outlet_Type  \n",
            "0               1                     0            1  \n",
            "1               1                     2            2  \n",
            "2               1                     0            1  \n",
            "3               2                     2            0  \n",
            "4               0                     2            1  \n",
            "...           ...                   ...          ...  \n",
            "8518            0                     2            1  \n",
            "8519            2                     1            1  \n",
            "8520            2                     1            1  \n",
            "8521            1                     2            2  \n",
            "8522            2                     0            1  \n",
            "\n",
            "[8523 rows x 11 columns]\n"
          ]
        }
      ],
      "source": [
        "print(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgbKCgaNIvd9",
        "outputId": "85a63f8f-0bac-41fd-a555-efeebb43e924"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0       3735.1380\n",
            "1        443.4228\n",
            "2       2097.2700\n",
            "3        732.3800\n",
            "4        994.7052\n",
            "          ...    \n",
            "8518    2778.3834\n",
            "8519     549.2850\n",
            "8520    1193.1136\n",
            "8521    1845.5976\n",
            "8522     765.6700\n",
            "Name: Item_Outlet_Sales, Length: 8523, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "quPO-6hlIz3Z"
      },
      "source": [
        "Splitting the data into Training data & Testing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "ahDPS_EYIxRX"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Re4BSHovJBB-",
        "outputId": "8779001e-c608-48e0-800e-9aa232af9825"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(8523, 11) (6818, 11) (1705, 11)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xl3IeS5nJaW3"
      },
      "source": [
        "Machine Learning Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0xSe9ZcJe75"
      },
      "source": [
        "XGBoost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "OM2K1_zxJVq7"
      },
      "outputs": [],
      "source": [
        "regressor = XGBRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "dKuVHDn2JmiW",
        "outputId": "51637492-bed9-4944-b83c-c1a444ac8cc5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "regressor.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKD3Ip4KJ4lS"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "IZIMhuxJJ2m0"
      },
      "outputs": [],
      "source": [
        "# prediction on training data\n",
        "training_data_prediction = regressor.predict(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "ebkXEag7KHsI"
      },
      "outputs": [],
      "source": [
        "# R squared Value\n",
        "r2_train = metrics.r2_score(Y_train, training_data_prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnIOWZEdKTet",
        "outputId": "a98abd13-d804-44b9-e011-3bdcaa02818c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R Squared value =  0.8762174618111388\n"
          ]
        }
      ],
      "source": [
        "print('R Squared value = ', r2_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "nLrj2_8SKsHM"
      },
      "outputs": [],
      "source": [
        "# prediction on test data\n",
        "test_data_prediction = regressor.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "E_R3MjSOK2-x"
      },
      "outputs": [],
      "source": [
        "# R squared Value\n",
        "r2_test = metrics.r2_score(Y_test, test_data_prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zQxuP6v1K-Hh",
        "outputId": "b5e23efe-cb6a-4d83-ba47-f2068b3c42d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R Squared value =  0.5017253991620692\n"
          ]
        }
      ],
      "source": [
        "print('R Squared value = ', r2_test)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}