Big_Mart_Sales_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Big Mart Sales Prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNpaRjnLU8TDmCzHKHH64FZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nandikonda-vishal21/Machine-learning-projects/blob/main/Big_Mart_Sales_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INBGPe8B6M_C"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3h4wknUJ583p"
      },
      "source": [
        "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"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40zwq-BO6Usj"
      },
      "source": [
        "Data Collection and Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iP96RnzK6V2x"
      },
      "source": [
        "# loading the data from csv file to Pandas DataFrame\n",
        "big_mart_data = pd.read_csv('/content/Train.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "7G9v4Of86-Wo",
        "outputId": "307d8b1b-7bc0-4970-ba77-8f97a591d0d6"
      },
      "source": [
        "# first 5 rows of the dataframe\n",
        "big_mart_data.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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  ...        Outlet_Type  Item_Outlet_Sales\n",
              "0           FDA15         9.30  ...  Supermarket Type1          3735.1380\n",
              "1           DRC01         5.92  ...  Supermarket Type2           443.4228\n",
              "2           FDN15        17.50  ...  Supermarket Type1          2097.2700\n",
              "3           FDX07        19.20  ...      Grocery Store           732.3800\n",
              "4           NCD19         8.93  ...  Supermarket Type1           994.7052\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVc1IvBt7Cv4",
        "outputId": "37144fcb-8ffc-4b71-f1f2-bff6514698a5"
      },
      "source": [
        "# number of data points & number of features\n",
        "big_mart_data.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8523, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYv2nJ097GoE",
        "outputId": "990b3979-f8cb-4031-d916-5b53cf535161"
      },
      "source": [
        "# getting some information about thye dataset\n",
        "big_mart_data.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQ3TIgBQ7LOJ"
      },
      "source": [
        "Categorical Features:\n",
        "\n",
        "Item_Identifier\n",
        "\n",
        "Item_Fat_Content\n",
        "\n",
        "Item_Type\n",
        "\n",
        "Outlet_Identifier\n",
        "\n",
        "Outlet_Size\n",
        "\n",
        "Outlet_Location_Type\n",
        "\n",
        "Outlet_Type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GgBKY6Zn7P3X",
        "outputId": "c50c5704-fcf7-4788-f489-94812497b1cd"
      },
      "source": [
        "# checking for missing values\n",
        "big_mart_data.isnull().sum()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AA3EUxZV7WoR"
      },
      "source": [
        "Handling Missing Values\n",
        "\n",
        "Mean --> average\n",
        "\n",
        "Mode --> more repeated value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGi-X35S7YvI",
        "outputId": "ce427af4-f0ea-4ae4-ac36-3fa9b82dbdab"
      },
      "source": [
        "# mean value of \"Item_Weight\" column\n",
        "big_mart_data['Item_Weight'].mean()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.857645184136183"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SuNCKnEo7e3B"
      },
      "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)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCgrdNxg7kCk",
        "outputId": "675f9624-7fdd-4b2c-91e2-84a375703045"
      },
      "source": [
        "# mode of \"Outlet_Size\" column\n",
        "big_mart_data['Outlet_Size'].mode()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Medium\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6H79esCF7oJb"
      },
      "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]))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNWLCO747sOn",
        "outputId": "6eeb6906-236c-4308-81d4-78f37bbca3d8"
      },
      "source": [
        "print(mode_of_Outlet_size)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2 Supermarket Type3\n",
            "Outlet_Size         Small             Small            Medium            Medium\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIwwfkbI7wUu"
      },
      "source": [
        "miss_values = big_mart_data['Outlet_Size'].isnull()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SnFVRjF70F8",
        "outputId": "591b34b3-9926-45ae-b346-337838b57ee2"
      },
      "source": [
        "print(miss_values)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWEr2-HI74XU"
      },
      "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])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9AH_xi-k78ED",
        "outputId": "9936fe7a-a2e2-4877-c177-c65ed7128e1e"
      },
      "source": [
        "# checking for missing values\n",
        "big_mart_data.isnull().sum()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNYY-3uf7_57"
      },
      "source": [
        "Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "9CHcsIoe8BAF",
        "outputId": "f724d80f-ab9b-49a7-8ab3-5577d729fa52"
      },
      "source": [
        "big_mart_data.describe()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\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_Outlet_Sales\n",
              "count  8523.000000  ...        8523.000000\n",
              "mean     12.857645  ...        2181.288914\n",
              "std       4.226124  ...        1706.499616\n",
              "min       4.555000  ...          33.290000\n",
              "25%       9.310000  ...         834.247400\n",
              "50%      12.857645  ...        1794.331000\n",
              "75%      16.000000  ...        3101.296400\n",
              "max      21.350000  ...       13086.964800\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-ek45aG8Gvb"
      },
      "source": [
        "Numerical Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ODcjMEI8HpM"
      },
      "source": [
        "sns.set()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "IkfNsWZE8Qc2",
        "outputId": "4fd6e563-80ef-4ad4-bc0c-0099d5f38381"
      },
      "source": [
        "# Item_Weight distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(big_mart_data['Item_Weight'])\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "OC3oAhi48Uvd",
        "outputId": "5f8499f6-a5c5-4b27-9af3-f9e17d15010f"
      },
      "source": [
        "# Item Visibility distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(big_mart_data['Item_Visibility'])\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "Vjl-3nTJ8YnF",
        "outputId": "81960f04-79f5-4bf3-ff5c-37360794da6c"
      },
      "source": [
        "# Item MRP distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(big_mart_data['Item_MRP'])\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "b8HmyiNH8cx1",
        "outputId": "75e4101d-7e92-4d3f-ac58-c4909529e3ef"
      },
      "source": [
        "# Item_Outlet_Sales distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.distplot(big_mart_data['Item_Outlet_Sales'])\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "eqLQ-3If8gyK",
        "outputId": "97140ace-8aa0-4340-f9eb-2768544b59c0"
      },
      "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()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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q1aux8gAAmiB2KwAA1igTAIA1ygQAYI0yAQBYo0wAANYoEwCANcoEAGCNMgEAWKNMAADWKBMAgDXKBABgjTIBAFijTAAA1gKiTMrLy5WRkaHbb79dI0eO1OzZsyVJBw8eVEpKihITE5WSkqIvv/zS85iaZgAA3wqIMlm4cKHCwsKUl5ennJwcTZw4UZKUkZGh0aNHKy8vT6NHj1Z6errnMTXNAAC+5fcyOXv2rN5++21NnDhRQUFBkqT27duruLhYe/fuVVJSkiQpKSlJe/fuVUlJSY0zAIDv1eniWI3h0KFDCg8P17Jly/Txxx+rVatWmjhxopo3b67o6GgFBwdLkoKDg9WhQwcVFhbKGFPtLCIiwp9fDgBck/xeJi6XS4cOHdJNN92kadOmadeuXRo/fryWLFnS6OuOjGzd6OuoTVRUG39HuEIgZpICN1egCcTtRCbvBWIubzL5vUxiYmIUEhLiOWR18803q127dmrevLmOHTsml8ul4OBguVwuHT9+XDExMTLGVDuri+LiM3K7jV+/eSdOnK525q9cgZhJqjlXoAnE7RSImSR+zi8XyN8/hyOo2hfhfn/PJCIiQv3799dHH30k6cKntIqLi3X99dcrLi5Oubm5kqTc3FzFxcUpIiJCkZGR1c4AAL7n9z0TSZo7d65mzpyprKwshYSEaMGCBWrbtq3mzJmj6dOn68UXX1Tbtm2VlZXleUxNMwCAbwVEmXTp0kX/9V//dcXybt266Y033rjqY2qaAQB8y++HuQAATR9lAgCwRpkAAKxRJgAAawHxBjxgI7xNqJo1D/P5es+Xlevk6QqfrxcIRJQJmrxmzcO0/hf3+3y9d77+mkSZAJI4zAUAaACUCQDAGmUCALBGmQAArFEmAABrlAkAwBplAgCwRpkAAKxRJgAAa5QJAMAaZQIAsEaZAACsUSYAAGuUCQDAGmUCALBGmQAArFEmAABrlAkAwBplAgCwRpkAAKxRJgAAa5QJAMAaZQIAsEaZAACsUSYAAGuUCQDAGmUCALAWUGWybNky9ejRQ/v375ck7dy5U8nJyUpMTFRaWpqKi4s9961pBgDwrYApkz179mjnzp2KjY2VJLndbk2ZMkXp6enKy8uT0+nUokWLap0BAHwvIMqkoqJC8+bN05w5czzL8vPzFRYWJqfTKUlKTU3Vhg0bap0BAHwvIMpkyZIlSk5OVufOnT3LCgsL1alTJ8/tiIgIud1unTx5ssYZAMD3QvwdYMeOHcrPz9fkyZN9vu7IyNY+X+floqLa+DvCFQIxkxSYucjkHTJ5LxBzeZPJ72WydetWHThwQMOGDZMkHT16VA888IB+/vOfq6CgwHO/kpISORwOhYeHKyYmptpZXRQXn5Hbbfz6zTtx4nS1M3/lCsRMUvW5yFRVU8ok8XN+uUD+/jkcQdW+CPf7Ya5x48bpww8/1KZNm7Rp0yZ17NhRK1as0NixY1VWVqZt27ZJktasWaMRI0ZIkuLj46udAQB8z+97JtVxOBxasGCBMjIyVF5ertjYWC1cuLDWGQDA9wKuTDZt2uT5e58+fZSTk3PV+9U0AwD4lt8PcwEAmj7KBABgjTIBAFijTAAA1igTAIA1ygQAYI0yAQBYo0wAANYoEwCANcoEAGCNMgEAWKNMAADWKBMAgDXKBABgjTIBAFijTAAA1igTAIA1ygQAYI0yAQBYo0wAANYoEwCANcoEAGCNMgEAWKNMAADWKBMAgDXKBABgjTIBAFijTAAA1igTAIA1ygQAYI0yAQBYo0wAANYoEwCANcoEAGDN72VSWlqqBx98UImJiRo5cqQmTJigkpISSdLOnTuVnJysxMREpaWlqbi42PO4mmYAAN/ye5kEBQVp7NixysvLU05Ojrp06aJFixbJ7XZrypQpSk9PV15enpxOpxYtWiRJNc4AAL7n9zIJDw9X//79Pbd79eqlgoIC5efnKywsTE6nU5KUmpqqDRs2SFKNMwCA7/m9TC7ldru1evVqJSQkqLCwUJ06dfLMIiIi5Ha7dfLkyRpnAADfC/F3gEs99dRTatmypcaMGaN333230dcXGdm60ddRm6ioNv6OcIVAzCQFZi4yeYdM3gvEXN5kCpgyycrK0ldffaXly5fL4XAoJiZGBQUFnnlJSYkcDofCw8NrnNVFcfEZud3Gr9+8EydOVzvzV65AzCRVn4tMVTWlTBI/55cL5O+fwxFU7YvwgDjMtXjxYuXn5ys7O1uhoaGSpPj4eJWVlWnbtm2SpDVr1mjEiBG1zgAAvuf3PZPPP/9cL7/8sq6//nqlpqZKkjp37qzs7GwtWLBAGRkZKi8vV2xsrBYuXChJcjgc1c4AAL7n9zL57ne/q88+++yqsz59+ignJ6fOMwCAbwXEYS4AQNNGmQAArFEmAABrlAkAwBplAgCwRpkAAKxRJgAAa5QJAMAaZQIAsEaZAACsUSYAAGuUCQDAGmUCALBGmQAArFEmAABrlAkAwBplAgCwRpkAAKxRJgAAa5QJAMAaZQIAsEaZAACsUSYAAGuUCQDAGmUCALBGmQAArFEmAABrlAkAwBplAgCwRpkAAKxRJgAAa5QJAMAaZQIAsNaky+TgwYNKSUlRYmKiUlJS9OWXX/o7EgBck5p0mWRkZGj06NHKy8vT6NGjlZ6e7u9IAHBNarJlUlxcrL179yopKUmSlJSUpL1796qkpMTPyQDg2hPi7wD1VVhYqOjoaAUHB0uSgoOD1aFDBxUWFioiIsKr53A4gjx/b9+uVaPkrEuGqwltG+mjJP9UW6b2rb3bvg2tplwt2vt+O0k1Z7ouvKUPk/xTTZnahgfedpKkZm2a+yjJP9WWqW3btj5KUlVNucJahPswyT9dzFRTtiBjjPFVoIaUn5+vadOmad26dZ5ld955pxYuXKjvf//7fkwGANeeJnuYKyYmRseOHZPL5ZIkuVwuHT9+XDExMX5OBgDXniZbJpGRkYqLi1Nubq4kKTc3V3FxcV4f4gIANJwme5hLkg4cOKDp06fr1KlTatu2rbKysnTDDTf4OxYAXHOadJkAAAJDkz3MBQAIHJQJAMAaZQIAsEaZAACsUSYAAGtN9nQqjSUrK0t5eXk6cuSIcnJy1L17d0nSX//6Vy1ZskSVlZW67rrr9Oyzz6pLly6SpPfee09LliyRMUbGGE2YMEG33367JCkhIUGhoaEKCwuTJE2ePFmDBw/2W6bDhw/r0Ucf9Tz36dOndebMGf39739v9EzVzQIxU03b0J+Zapp5q7S0VFOnTtXXX3+t0NBQfec739G8efMUERGhnTt3Kj09XeXl5YqNjdXChQsVGXnhFCzVzQ4ePKj09HSdOHFCISEh+vd//3dlZGSoeXPvT5HS0JkuNWPGDL355pvavn27WrWq22mTGjrX9u3bNXfuXM/zFxcXKyoqSm+99ZZft9XatWu1cuVKud1udenSRfPnz1d4eB1P3WJQxdatW01BQYEZOnSo+eyzz4wxxpw8edLccsst5osvvjDGGPP222+btLQ0Y4wxbrfbOJ1Oz3337dtnevXqZVwulzHGVHmeQMl0qczMTDN37txGz1TTLBAz1WUb+ipTXbZhTUpLS82WLVs8t+fPn29mzJhhXC6XGT58uNm6dasxxpjs7Gwzffp0Y4ypcXbo0CGzZ88ez/0mTpxoli1b5tdMF/3lL38xM2bMMN27dzdnzpypU6bGzHXRww8/bF599VW/ZvrHP/5hBg0aZIqLiz2z2bNn1ymTMcZwmOsyTqfzilOyfPXVV2rfvr26du0qSRoyZIg+/PBDzxmKHQ6HTp8+LenCq+oOHTrI4Wi4TdtYmSoqKpSTk6O777670TPVljcQMzXE97UhM3m7DWsTHh6u/v37e2736tVLBQUFys/PV1hYmJxOpyQpNTVVGzZskKQaZ507d9ZNN90k6cI269mzpwoKCvyaSbrwCn7ZsmWaMWNGnbI0dq6LiouL9dFHH2nUqFF+zbR///4qZw8ZMmSIcnJy6pRJ4j0Tr3Tt2lVFRUXavXu3JHk2dGFhoYKCgvT888/rkUce0dChQ/Xoo48qKyuryuMnT56skSNHas6cOTp16lRAZJKkTZs2KTo6usFOjFlTpppmgZjJ223oy0zebsO6cLvdWr16tRISElRYWKhOnTp5ZhEREXK73Tp58mSNs0uVlZVp7dq1SkhI8HumefPm6bHHHlObNm3qnaUxcl309ttva+DAgWrfvr1fM33ve9/TJ598okOHDskYo9zcXH377bdX5K0NZeKFNm3a6LnnntOzzz6ru+66S8XFxWrbtq2Cg4NVWVmpl19+WS+++KLee+89vfTSS3r88cd19uxZSdKqVav0zjvvaO3atTLGaN68eX7PdNHatWvrtQdQn0w1zQIxk7fb0JeZvN2GdfHUU0+pZcuWGjNmjPXXVVlZqUmTJmnAgAEaNmyYXzOtX79ezZo10w9/+MN6P0dj5LrUm2++af2z3hCZunbtqlmzZmnSpEm69957dd1110mSQkLq+JZ6nQ+MXSNqeq/jxIkTJj4+3pw9e9bs3r3b3HHHHVXmI0aMMLt27bricZ9++qkZOnRoQGQ6evSoufnmm01JSUm989QlkzezQMpUl++rrzLVZeaN+fPnm/vvv9+Ul5cbY4zZtWuX+dGPfuSZFxcXm169etU6M8aYyspKM3HiRDNt2jTjdrvrlachM2VkZJjBgweboUOHmqFDh5ru3bubH/7wh+bzzz/3a66LduzYYQYOHGjOnz9frzyNkemiXbt2mcGDB9c5D3smXjpx4oSkC7uVixcvVmpqqlq2bKmOHTvq6NGj+uKLLyRdOPlkcXGx/u3f/k3ffvut55i7MUbr169XXFycXzNd9NZbb2nIkCFq165dg+WpKVNts0DL5M029HWm2mZ1sXjxYuXn5ys7O1uhoaGSpPj4eJWVlWnbtm2SpDVr1mjEiBG1ztxut6ZPn67g4GA9/fTTCgqq+aJTvsg0Z84cbd68WZs2bdKmTZskXTiz+I033ujXXBetXbtWycnJdX/130iZLv5clZeX64UXXlBaWlqdM3Gix8tkZmZq48aNKioqUrt27RQeHq5169bpySef1Pbt23X+/HkNHDhQM2fO9Hzc95133tFvfvMbzz+ixx57TMOHD9ehQ4f0y1/+Ui6XS263W926ddOsWbPUoUMHv2W6KDExUU8++aRuvfVWn22nmmaBmKm2beiPTLVtQ298/vnnSkpK0vXXX+/5+G7nzp2VnZ2t7du3KyMjo8rHRy8e069u9te//lUPPfSQunfv7vmAQp8+fZSRkeG3TJfr0aNHvT4a3Bi5ysrKNHDgQP3hD39Qt27d6pSnsTKNHTtWBQUFOn/+vO68805NnDixzh82oUwAANY4zAUAsEaZAACsUSYAAGuUCQDAGmUCALBGmQAArFEmaBIOHz6sHj16qLKy0t9R6mXp0qWaPHlytfOEhAT97//+ryRp+fLlevLJJ2t9zp///Od64403GiwjYIMyQaN58803NXLkSN18880aOHCgMjIyvD7R5aW/XOuz3p/97Gde3Xf69OmKj49X7969PX+Sk5NrfVxj/iIfP368nn766UZ57sbg7fZ+4403dMcdd6iiosKzrLS0VD/4wQ+0efPmxowIH6BM0Ch++9vfatGiRZoyZYq2bdum//7v/1ZBQYHuv//+Kr9MAsEDDzygHTt2eP688847/o70L+mee+5RdHS0srOzPcueeeYZDRkypN5nPbhcU91z/VdAmaDBnTlzRkuXLtWsWbN06623qlmzZurcubOef/55HTlyRO+8846mT5+u5557zvOYjz/+2PMLZcqUKSooKND48ePVu3dv/eY3v7liHadPn9bMmTM1aNAgDR48WM8995xcLpcOHDigjIwM7dy5U7179/Zcv6E+ysvLNXnyZPXv319Op1N33323ioqK9Nxzz2nbtm2aN2+eevfu7TkTdGZmpoYMGaI+ffrorrvu8pwH6aKKigo9/vjj6t27t37yk5/o008/vep6Lz0kVl2Gi44cOaLU1FT17t1baWlpnmubXDwsuHbtWg0ZMkT9+vXT6tWrtXv3bo0cOVJOp/OKM1j/8Y9/1B133KF+/frpgaATTQYAAAdASURBVAce0JEjRzyzHj16aPXq1br99tvldDo1d+5cGWPqvL0zMzP1+9//Xvv27dMHH3ygv/3tb5oxY4aOHTumX/7ylxowYIASEhL0+uuvex6ze/dupaSkyOl0atCgQZo3b16VFyQ9evTQqlWrdPvtt9f5SphoQHU+NSRQi/fff9/ExcVd9YyoU6dONZMmTTLTpk0zixcv9izfsmVLlTOVDh061Hz00Uee24cOHTLdu3f3POcjjzxiZs+ebc6ePWuKiorM3XffbVavXm2MMWbt2rUmNTXVq6yX57jU6tWrzUMPPWS+/fZbU1lZaT755BNz+vRpY4wxY8aMMX/4wx+q3P/tt982JSUl5vz582bFihXmP/7jP0xZWZkxxpgXXnjB3HTTTebPf/6zqaioMK+++qoZOnSoqaiouOLrfeGFF8yvfvUrrzIMGzbMfPHFF+bcuXNmzJgxZuHChVW21+zZs01ZWZn54IMPTHx8vHn44YdNUVGROXr0qBkwYID5+OOPjTHGvPvuu2b48OHmH//4hzl//rzJzs42KSkpnq+te/fuZty4ceabb74xR44cMf379zfvv/9+nbe3Mca8/vrr5sc//rEZOnSoeffdd43L5TI/+clPzNKlS015ebn5+uuvTUJCgtm8ebMxxphPPvnE7Nixw5w/f94cOnTIjBgxwrz22mtVst13332mtLTUnDt3zuscaFjsmaDBlZaWql27dlc9I2pUVJRKS0utnr+oqEjvv/++Zs6cqZYtWyoyMlL33Xef1q1bV6/n++1vfyun0+n5M23aNEkXrudw8uRJffXVVwoODlZ8fLxat25d7fOMGjXK83WnpaWpoqJCBw8e9My///3va8SIEWrWrJnncN+uXbtqzFZbhrvuuktdu3ZV8+bNNWLECO3bt6/K4x999FGFhYVp0KBBatmypZKSkhQZGano6Gg5nU7t3btX0oWzyI4bN07dunVTSEiIxo8fr3379lXZO3nwwQfVtm1bderUSf379692z6o2Y8aMUUhIiOLi4jR8+HB98sknKikp0YQJExQaGqouXbro3nvv1fr16yVdOONtr169FBISos6dOyslJUVbt26t8pzjxo1TeHh4na47j4ZVv/MfAzVo166dSktLVVlZeUWhnDhxwvoU8wUFBaqsrNSgQYM8y9xu9xWXx/VWWlqaJk2adMXyUaNG6ejRo3riiSd06tQpJScna9KkSWrWrNlVn2fFihX64x//qOPHjysoKEhnzpypUpwdO3b0/N3hcCg6OlrHjx+vMVttGaKiojz3bdGihb799tsqj4+MjPT8PSws7IrbF+9fUFCgZ555psrVJI0xOnbsmGJjY6+6rvpeKCwoKEjdunVTdHS0pAuH6o4fP17lEJnL5fLcPnjwoObPn6/8/HydO3dOLpfriitx1vd7j4ZDmaDB9e7dW6Ghodq4caPuvPNOz/KzZ89q8+bNeuKJJ7R3716VlZV5Zpe+D1Cbjh07KjQ0VFu2bLnq3k99r6dxuWbNmmnChAmaMGGCDh8+rHHjxqlr16665557rrjvtm3b9Oqrr2rlypX67ne/K4fDoX79+slcclLuo0ePev7udrt17NixWi9HUJcMNmJiYjR+/HivPsl2OdvtHRMTo86dO2vjxo1Xnc+ZM0c33XSTfv3rX6t169ZauXKl8vLyGjQD7HGYCw2uTZs2evTRR5WZmanNmzfr/PnzOnz4sB5//HF17NhRo0aNUlxcnN5//32dPHlSJ06c0O9+97sqz9G+fXsdOnToqs/foUMHDRw4UPPnz9eZM2fkdrv19ddf6+9//7ukC6/Gjx07Zv2psS1btuizzz6Ty+VS69atFRIS4rnGw+X5zp49q+DgYEVERKiyslLLli3TmTNnqjzfnj17tHHjRlVWVup3v/udQkNDdfPNN9c7Q0NKTU3VK6+8os8//1zShQ84/PnPf/bqsbbbu2fPnmrVqpVeeeUVlZWVyeVyaf/+/Z5r3Z89e1atWrVSq1atdODAAa1evbpe60HjokzQKB588EFNmjRJCxYsUN++fXXvvfcqJiZGK1euVGhoqEaNGqXvfe97SkhIUFpaWpU9GOnCMfCXXnpJTqdTK1asuOL5FyxY4LmQT79+/fTYY495rhY3YMAA3XjjjRo0aJD69+9fa9YVK1ZU+X8mFx9TVFSkxx57TH379tWdd96pW265RaNGjZIk/eIXv1BeXp769eunzMxMz6fKEhMTlZCQoLCwsCsOvQwbNkzr169Xv3799Kc//UlLly6t9pDZRTVlaEi33Xabxo4dqyeeeEJ9+vRRUlKS1//3o67b+3LBwcFavny5Pv30Uw0bNkwDBgzQrFmzPGU8bdo05ebmqk+fPpo9e/YVPysIDFwcCwBgjT0TAIA13oDHv7wf/ehHKigouGL53Llz6/WGM2rG9r42cZgLAGCNw1wAAGuUCQDAGmUCALBGmQAArFEmAABr/w8oeXzmGh56pgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwIVtFWS8ksX"
      },
      "source": [
        "Categorical Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "prDFh4rg8ls5",
        "outputId": "8a3fdf14-16e7-433a-91a6-8366c27ef8ed"
      },
      "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()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "jrDgYDwu8r86",
        "outputId": "463dd4a3-242e-40d4-8c8c-10d08b5ef307"
      },
      "source": [
        "# Item_Type column\n",
        "plt.figure(figsize=(30,6))\n",
        "sns.countplot(x='Item_Type', data=big_mart_data)\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 2160x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "53Xc9Ew_8wRN",
        "outputId": "823495e2-7a24-4a8e-bc42-0c3d8a4782e9"
      },
      "source": [
        "# Outlet_Size column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x='Outlet_Size', data=big_mart_data)\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpOqrDaK80dp"
      },
      "source": [
        "Data Pre-Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "fYe9wuSI81rW",
        "outputId": "9f3bcf76-e505-469a-e9bc-024c67ecb3e5"
      },
      "source": [
        "big_mart_data.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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  ...        Outlet_Type  Item_Outlet_Sales\n",
              "0           FDA15         9.30  ...  Supermarket Type1          3735.1380\n",
              "1           DRC01         5.92  ...  Supermarket Type2           443.4228\n",
              "2           FDN15        17.50  ...  Supermarket Type1          2097.2700\n",
              "3           FDX07        19.20  ...      Grocery Store           732.3800\n",
              "4           NCD19         8.93  ...  Supermarket Type1           994.7052\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BE5wmYyx88lf",
        "outputId": "0e3f10cd-d270-43a4-c161-3c76f80ccd69"
      },
      "source": [
        "big_mart_data['Item_Fat_Content'].value_counts()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    5089\n",
              "Regular    2889\n",
              "LF          316\n",
              "reg         117\n",
              "low fat     112\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSsykcFX9B9g"
      },
      "source": [
        "big_mart_data.replace({'Item_Fat_Content': {'low fat':'Low Fat','LF':'Low Fat', 'reg':'Regular'}}, inplace=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hJam8zy9FpL",
        "outputId": "6ee5b96a-1539-4cb8-b1cb-d54c2c0da9bf"
      },
      "source": [
        "big_mart_data['Item_Fat_Content'].value_counts()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    5517\n",
              "Regular    3006\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hxe9G4vL9Jgp"
      },
      "source": [
        "Label Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_hUrh7r9Kmo"
      },
      "source": [
        "encoder = LabelEncoder()"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BLlvWPS9O6m"
      },
      "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'])"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "U_SZ_6hA9XTH",
        "outputId": "510f6df6-df4e-4057-daeb-ef99d367161b"
      },
      "source": [
        "big_mart_data.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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  ...  Outlet_Type  Item_Outlet_Sales\n",
              "0              156         9.30  ...            1          3735.1380\n",
              "1                8         5.92  ...            2           443.4228\n",
              "2              662        17.50  ...            1          2097.2700\n",
              "3             1121        19.20  ...            0           732.3800\n",
              "4             1297         8.93  ...            1           994.7052\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_F-Ha4u9bt3"
      },
      "source": [
        "Splitting features and Target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RizX7QI9cyg"
      },
      "source": [
        "X = big_mart_data.drop(columns='Item_Outlet_Sales', axis=1)\n",
        "Y = big_mart_data['Item_Outlet_Sales']"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTf9oPT-9jfP",
        "outputId": "e314d594-cbd7-436b-dd91-38f17278c221"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Item_Identifier  Item_Weight  ...  Outlet_Location_Type  Outlet_Type\n",
            "0                 156        9.300  ...                     0            1\n",
            "1                   8        5.920  ...                     2            2\n",
            "2                 662       17.500  ...                     0            1\n",
            "3                1121       19.200  ...                     2            0\n",
            "4                1297        8.930  ...                     2            1\n",
            "...               ...          ...  ...                   ...          ...\n",
            "8518              370        6.865  ...                     2            1\n",
            "8519              897        8.380  ...                     1            1\n",
            "8520             1357       10.600  ...                     1            1\n",
            "8521              681        7.210  ...                     2            2\n",
            "8522               50       14.800  ...                     0            1\n",
            "\n",
            "[8523 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvGSje5P9nZ4",
        "outputId": "bbae567d-890f-4700-a3d5-92a8c734944b"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PVgT0Pl9scI"
      },
      "source": [
        "Splitting the data into Training data & Testing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kcPr4a-9thb"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ni46ATp91WP",
        "outputId": "2bd230ff-bb8f-49e6-b1a9-5dbcf089601c"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8523, 11) (6818, 11) (1705, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KuDUK26796gK"
      },
      "source": [
        "Machine Learning Model Training\n",
        "\n",
        "XGBoost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4y21vWZ998Oq"
      },
      "source": [
        "regressor = XGBRegressor()"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCzvfReC-B65",
        "outputId": "e1ef65e0-d379-42f9-cc83-3f8bb557a984"
      },
      "source": [
        "regressor.fit(X_train, Y_train)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10:49:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
              "             max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
              "             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
              "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "             silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9IGq--3-GrX"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSLY4nvJ-HqZ"
      },
      "source": [
        "# prediction on training data\n",
        "training_data_prediction = regressor.predict(X_train)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GziDbtHD-O2G"
      },
      "source": [
        "# R squared Value\n",
        "r2_train = metrics.r2_score(Y_train, training_data_prediction)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11KQLdlE-S6v",
        "outputId": "6fe5ee54-571f-4bba-c9b9-268e0af07798"
      },
      "source": [
        "print('R Squared value = ', r2_train)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Squared value =  0.6364457030941357\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdFqwdWA-WxA"
      },
      "source": [
        "# prediction on test data\n",
        "test_data_prediction = regressor.predict(X_test)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHZSOox5-bXw"
      },
      "source": [
        "# R squared Value\n",
        "r2_test = metrics.r2_score(Y_test, test_data_prediction)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZolLzkL-fVt",
        "outputId": "8939052e-3811-4f25-a29e-4835d0f2b6ae"
      },
      "source": [
        "print('R Squared value = ', r2_test)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R Squared value =  0.5867640914432671\n"
          ]
        }
      ]
    }
  ]
}