Big Mart Sales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e90f704b",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05134190",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96852fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb98cb1b",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6a7c79b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./Train.csv')\n",
    "# df_test = pd.read_csv('./Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2b0bfc69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# frames = [df_train, df_test]\n",
    "# df = pd.concat(frames, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6fd1975a",
   "metadata": {},
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56400740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38c0282",
   "metadata": {},
   "source": [
    "# Basic Information about Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae175e33",
   "metadata": {},
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87ba7d11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDW13    10\n",
      "FDG33    10\n",
      "NCY18     9\n",
      "FDD38     9\n",
      "DRE49     9\n",
      "         ..\n",
      "FDY43     1\n",
      "FDQ60     1\n",
      "FDO33     1\n",
      "DRF48     1\n",
      "FDC23     1\n",
      "Name: Item_Identifier, Length: 1559, dtype: int64\n",
      "Low Fat    5089\n",
      "Regular    2889\n",
      "LF          316\n",
      "reg         117\n",
      "low fat     112\n",
      "Name: Item_Fat_Content, dtype: int64\n",
      "Fruits and Vegetables    1232\n",
      "Snack Foods              1200\n",
      "Household                 910\n",
      "Frozen Foods              856\n",
      "Dairy                     682\n",
      "Canned                    649\n",
      "Baking Goods              648\n",
      "Health and Hygiene        520\n",
      "Soft Drinks               445\n",
      "Meat                      425\n",
      "Breads                    251\n",
      "Hard Drinks               214\n",
      "Others                    169\n",
      "Starchy Foods             148\n",
      "Breakfast                 110\n",
      "Seafood                    64\n",
      "Name: Item_Type, dtype: int64\n",
      "OUT027    935\n",
      "OUT013    932\n",
      "OUT049    930\n",
      "OUT046    930\n",
      "OUT035    930\n",
      "OUT045    929\n",
      "OUT018    928\n",
      "OUT017    926\n",
      "OUT010    555\n",
      "OUT019    528\n",
      "Name: Outlet_Identifier, dtype: int64\n",
      "Medium    2793\n",
      "Small     2388\n",
      "High       932\n",
      "Name: Outlet_Size, dtype: int64\n",
      "Tier 3    3350\n",
      "Tier 2    2785\n",
      "Tier 1    2388\n",
      "Name: Outlet_Location_Type, dtype: int64\n",
      "Supermarket Type1    5577\n",
      "Grocery Store        1083\n",
      "Supermarket Type3     935\n",
      "Supermarket Type2     928\n",
      "Name: Outlet_Type, dtype: int64\n",
      "Supermarket Type1    5577\n",
      "Grocery Store        1083\n",
      "Supermarket Type3     935\n",
      "Supermarket Type2     928\n",
      "Name: Outlet_Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['Item_Identifier'].value_counts())\n",
    "print(df['Item_Fat_Content'].value_counts())\n",
    "print(df['Item_Type'].value_counts())\n",
    "print(df['Outlet_Identifier'].value_counts())\n",
    "print(df['Outlet_Size'].value_counts())\n",
    "print(df['Outlet_Location_Type'].value_counts())\n",
    "print(df['Outlet_Type'].value_counts())\n",
    "print(df['Outlet_Type'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbad5b56",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b27ba6f1",
   "metadata": {},
   "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>7060.000000</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>4.643456</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>8.773750</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>12.600000</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>16.850000</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  7060.000000      8523.000000  8523.000000                8523.000000   \n",
       "mean     12.857645         0.066132   140.992782                1997.831867   \n",
       "std       4.643456         0.051598    62.275067                   8.371760   \n",
       "min       4.555000         0.000000    31.290000                1985.000000   \n",
       "25%       8.773750         0.026989    93.826500                1987.000000   \n",
       "50%      12.600000         0.053931   143.012800                1999.000000   \n",
       "75%      16.850000         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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d6744e5",
   "metadata": {},
   "source": [
    "# Check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a02b2628",
   "metadata": {},
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ab542b",
   "metadata": {},
   "source": [
    "# Fill Missing Value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e39bae0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.857645184136183\n"
     ]
    }
   ],
   "source": [
    "print(df['Item_Weight'].mean())\n",
    "df['Item_Weight'].fillna(df['Item_Weight'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "892d64fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "value = df['Outlet_Size'].mode()\n",
    "df['Outlet_Size'].fillna(value, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0c40c405",
   "metadata": {},
   "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                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e4f4a4",
   "metadata": {},
   "source": [
    "# Distribution of Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "86ab1b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(df['Item_Weight'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec56c481",
   "metadata": {},
   "source": [
    "# Distribution of Item visibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53d50975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(df['Item_Visibility'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59218d49",
   "metadata": {},
   "source": [
    "# Monthly Progress Report (MRP) Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "31a9e70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(df['Item_MRP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b63f713",
   "metadata": {},
   "source": [
    "# Outlet Sales Distrbution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e136b06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(df['Item_Outlet_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc295a8",
   "metadata": {},
   "source": [
    "# Outlet_Establishment_Year ?Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "44847e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(x='Outlet_Establishment_Year', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2795823",
   "metadata": {},
   "source": [
    "# Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4f858f",
   "metadata": {},
   "source": [
    "# Item_Fat_Content Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "744ed5e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAF0CAYAAADSJBVHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZDUlEQVR4nO3de5RlZX3m8e8DKMRwl5YhgGlGMQ4ZFU0PYtBIZALoRGEcIDheWiRpTdCIkWQ0MwmIkuBCBy+oMyxBG9cooIYBiRPsQbloRGjuNxk6CAEWSocG1KhkAb/5Y79FH5qqfquhTlV19/ez1l6197v32ft3dp2q5+zLeU+qCkmS1maTuS5AkjT/GRaSpC7DQpLUZVhIkroMC0lSl2EhSerabK4LGIcddtihFi5cONdlSNJ65corr/ynqlow2bwNMiwWLlzI8uXL57oMSVqvJLljqnmehpIkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVLXBtnr7Nr8xp+eMdcljMWVJ71lrkuQtAHzyEKS1DXWsEhye5Lrk1yTZHlr2z7JsiS3tp/btfYk+USSFUmuS/KSkfUsbsvfmmTxOGuWJD3RbBxZ/HZV7VlVi9r0+4ALq2p34MI2DfBqYPc2LAE+A0O4AMcCLwX2Ao6dCBhJ0uyYi9NQBwFL2/hS4OCR9jNqcBmwbZKdgAOAZVW1qqruB5YBB85yzZK0URt3WBTwjSRXJlnS2nasqnva+A+BHdv4zsCdI4+9q7VN1S5JmiXjvhvq5VV1d5JnAcuSfH90ZlVVkpqJDbUwWgLw7Gc/eyZWKUlqxnpkUVV3t5/3AucwXHP4UTu9RPt5b1v8bmDXkYfv0tqmal9zW6dW1aKqWrRgwYKZfiqStFEbW1gk+eUkW02MA/sDNwDnARN3NC0Gzm3j5wFvaXdF7Q082E5XXQDsn2S7dmF7/9YmSZol4zwNtSNwTpKJ7Xyxqv4uyRXA2UmOBO4ADmvLfx14DbAC+BlwBEBVrUryQeCKttzxVbVqjHVLktYwtrCoqtuAF03Sfh+w3yTtBRw1xbpOB06f6RolSdPjJ7glSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkrrGHRZJNk1yd5Pw2vVuS7yVZkeSsJE9v7Zu36RVt/sKRdby/td+S5IBx1yxJerzZOLJ4N3DzyPSHgZOr6rnA/cCRrf1I4P7WfnJbjiR7AIcDvw4cCHw6yaazULckqRlrWCTZBfgPwGfbdIBXAV9piywFDm7jB7Vp2vz92vIHAWdW1UNV9QNgBbDXOOuWJD3euI8sPgb8GfBom34m8EBVPdym7wJ2buM7A3cCtPkPtuUfa5/kMZKkWTC2sEjyu8C9VXXluLaxxvaWJFmeZPnKlStnY5OStNEY55HFPsDrktwOnMlw+unjwLZJNmvL7ALc3cbvBnYFaPO3Ae4bbZ/kMY+pqlOralFVLVqwYMHMPxtJ2oiNLSyq6v1VtUtVLWS4QP3Nqnoj8C3gkLbYYuDcNn5em6bN/2ZVVWs/vN0ttRuwO3D5uOqWJD3RZv1FZtx/Ac5M8iHgauC01n4a8IUkK4BVDAFDVd2Y5GzgJuBh4KiqemT2y5akjdeshEVVXQRc1MZvY5K7marqF8ChUzz+BOCE8VUoSVobP8EtSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdY0tLJJskeTyJNcmuTHJB1r7bkm+l2RFkrOSPL21b96mV7T5C0fW9f7WfkuSA8ZVsyRpcuM8sngIeFVVvQjYEzgwyd7Ah4GTq+q5wP3AkW35I4H7W/vJbTmS7AEcDvw6cCDw6SSbjrFuSdIaxhYWNfhpm3xaGwp4FfCV1r4UOLiNH9SmafP3S5LWfmZVPVRVPwBWAHuNq25J0hON9ZpFkk2TXAPcCywD/gF4oKoebovcBezcxncG7gRo8x8EnjnaPsljJEmzYKxhUVWPVNWewC4MRwPPH9e2kixJsjzJ8pUrV45rM5K0UZqVu6Gq6gHgW8DLgG2TbNZm7QLc3cbvBnYFaPO3Ae4bbZ/kMaPbOLWqFlXVogULFozjaUjSRmucd0MtSLJtG/8l4HeAmxlC45C22GLg3DZ+Xpumzf9mVVVrP7zdLbUbsDtw+bjqliQ90Wb9RZ60nYCl7c6lTYCzq+r8JDcBZyb5EHA1cFpb/jTgC0lWAKsY7oCiqm5McjZwE/AwcFRVPTLGuiVJaxhbWFTVdcCLJ2m/jUnuZqqqXwCHTrGuE4ATZrpGSdL0+AluSVKXYSFJ6hrnNQvNc/94/AvmuoSxePZfXj/XJUgbnGkdWSS5cDptkqQN01qPLJJsATwD2CHJdkDarK3xU9SStNHonYZ6O3A08CvAlawOix8Dp4yvLEnSfLLWsKiqjwMfT/KuqvrkLNUkSZpnpnWBu6o+meQ3gYWjj6mqM8ZUlyRpHplWWCT5AvAc4Bpg4tPTBRgWkrQRmO6ts4uAPVpfTZKkjcx0P5R3A/CvxlmIJGn+mu6RxQ7ATUkuZ/i6VACq6nVjqUqSNK9MNyyOG2cRkqT5bbp3Q1087kIkSfPXdO+G+gnD3U8ATweeBvxzVW09rsIkSfPHdI8stpoYTxLgIGDvcRUlSZpf1rmL8hr8b+CAmS9HkjQfTfc01OtHJjdh+NzFL8ZSkSRp3pnu3VCvHRl/GLid4VSUJGkjMN1rFkeMuxBJ0vw13S8/2iXJOUnubcNXk+wy7uIkSfPDdC9wfw44j+F7LX4F+FprkyRtBKYbFguq6nNV9XAbPg8sGGNdkqR5ZLphcV+SNyXZtA1vAu4bZ2GSpPljumHxNuAw4IfAPcAhwFvHVJMkaZ6Z7q2zxwOLq+p+gCTbAx9hCBFJ0gZuukcWL5wICoCqWgW8eDwlSZLmm+mGxSZJtpuYaEcW0z0qkSSt56b7D/+jwHeTfLlNHwqcMJ6SJEnzzXQ/wX1GkuXAq1rT66vqpvGVJUmaT6Z9KqmFgwEhSRuhde6iXJK08TEsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkrrGFRZJdk3wryU1Jbkzy7ta+fZJlSW5tP7dr7UnyiSQrklyX5CUj61rclr81yeJx1SxJmtw4jyweBt5bVXsAewNHJdkDeB9wYVXtDlzYpgFeDezehiXAZ2AIF+BY4KXAXsCxEwEjSZodYwuLqrqnqq5q4z8BbgZ2Bg4ClrbFlgIHt/GDgDNqcBmwbZKdgAOAZVW1qqruB5YBB46rbknSE83KNYskC4EXA98Ddqyqe9qsHwI7tvGdgTtHHnZXa5uqXZI0S8YeFkm2BL4KHF1VPx6dV1UF1AxtZ0mS5UmWr1y5ciZWKUlqxhoWSZ7GEBT/q6r+pjX/qJ1eov28t7XfDew68vBdWttU7Y9TVadW1aKqWrRgwYKZfSKStJEb591QAU4Dbq6q/z4y6zxg4o6mxcC5I+1vaXdF7Q082E5XXQDsn2S7dmF7/9YmSZolm41x3fsAbwauT3JNa/tz4ETg7CRHAncAh7V5XwdeA6wAfgYcAVBVq5J8ELiiLXd8Va0aY92SpDWMLSyq6ttAppi93yTLF3DUFOs6HTh95qqTJK0LP8EtSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdY0tLJKcnuTeJDeMtG2fZFmSW9vP7Vp7knwiyYok1yV5ychjFrflb02yeFz1SpKmNs4ji88DB67R9j7gwqraHbiwTQO8Gti9DUuAz8AQLsCxwEuBvYBjJwJGkjR7xhYWVXUJsGqN5oOApW18KXDwSPsZNbgM2DbJTsABwLKqWlVV9wPLeGIASZLGbLavWexYVfe08R8CO7bxnYE7R5a7q7VN1S5JmkVzdoG7qgqomVpfkiVJlidZvnLlyplarSSJ2Q+LH7XTS7Sf97b2u4FdR5bbpbVN1f4EVXVqVS2qqkULFiyY8cIlaWM222FxHjBxR9Ni4NyR9re0u6L2Bh5sp6suAPZPsl27sL1/a5MkzaLNxrXiJF8C9gV2SHIXw11NJwJnJzkSuAM4rC3+deA1wArgZ8ARAFW1KskHgSvacsdX1ZoXzSVJYza2sKiqN0wxa79Jli3gqCnWczpw+gyWJklaR36CW5LUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2bzXUBkuaXi3/rlXNdwox75SUXz3UJ6z2PLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHXZN5QE7PPJfea6hLH4zru+M9clrNdOee/X5rqEGffOj772ST3OIwtJUpdhIUnqWm/CIsmBSW5JsiLJ++a6HknamKwXYZFkU+BTwKuBPYA3JNljbquSpI3HehEWwF7Aiqq6rar+BTgTOGiOa5Kkjcb6EhY7A3eOTN/V2iRJsyBVNdc1dCU5BDiwqn6/Tb8ZeGlVvXNkmSXAkjb5a8Ats17oE+0A/NNcFzFPuC9Wc1+s5r5YbT7si1+tqgWTzVhfPmdxN7DryPQure0xVXUqcOpsFtWTZHlVLZrrOuYD98Vq7ovV3Berzfd9sb6chroC2D3JbkmeDhwOnDfHNUnSRmO9OLKoqoeTvBO4ANgUOL2qbpzjsiRpo7FehAVAVX0d+Ppc17GO5tVpsTnmvljNfbGa+2K1eb0v1osL3JKkubW+XLOQJM0hw2ISSX46pvW+NcnKJNe04Yy1LLtvkt8cRx3rIskjrdYbknwtybZj2MZFSebtXSBrmunXR5JDk9yc5FtrWWZhkv88k9udbZPttyTHJbl75G/ixLmoTX2Gxew7q6r2bMNb1rLcvsCchwXw81brvwVWAUfNdUGt+5cNyZHAH1TVb69lmYXAeh0Wa3HyyN/ERtHvWwbr1f/f9arYuZRkzySXJbkuyTlJtkvyrCRXtvkvSlJJnt2m/yHJM6ax3tcm+V6Sq5P83yQ7JlkIvAN4T3u39YqxPrnp+y7tk/NJnpPk75JcmeTSJM8fab8syfVJPjTxbrIdKZ0/saIkpyR565obSPKZJMuT3JjkAyPttyf5cJKrgEPH/Dynpf3Bn9SOuq5P8nut/VNJXtfGz0lyeht/W5IT1ljHXwIvB05r61rY9udVbZh4w3Ai8Ir2enjP7D1LzZT2u72lnVG4AfiLJFe0/ymjr/W/aMt9O8mXkhwzd1WPqCqHNQbgp5O0XQe8so0fD3ysjd8IbA28k+HzIG8EfhX47iTreCuwErimDUcA27H6RoPfBz7axo8Djpkv+4LhluUvM3ySHuBCYPc2/lLgm238fOANbfwdI4/fFzh/ZL2nAG9t4xcBi9r49iPbuwh4YZu+Hfizud4fa+yT/wQsa7XuCPwjsBPD54BOastcDlzWxj8HHDDJ+kaf/zOALdr47sDyyfbf+jhM8Xd1HMMHbCf+Jp6wfzaUgeHo8FFgb2B/hrufwvCm/Xzgt4B/1/bDFsBWwK3z4f9AVa0/t87OpSTbANtW1cWtaSnDP06Avwf2YfhF/xVwIMML4NIpVndWPb6bkhcAZyXZCXg68IOZfwZPyS8luYbhiOJmYFmSLRlOkX05ycRym7efLwMObuNfBD6yjts7rHXdshnDP949GIIa4KwnUf84vRz4UlU9AvwoycUMf+yXAkdn6Bn5JmC79vt9GfDHnXU+DTglyZ7AI8DzxlX8PHJyVa3r62R9dUdVXZbkIwyBcXVr35LhzcFWwLlV9QvgF0nmzVf1eRrqqbsEeAXD0cS5wIsY/olMFRZr+iRwSlW9AHg7wzuK+eTnVbUnw/MLwzWLTYAHavV55j2r6t901vMwj3+9PeF5JtkNOAbYr6peCPztGsv985N/GrOnqu4GtmV443AJw2vhMIZ31j/pPPw9wI8YXkeLGN5AaMMx8RoO8Ncjfz/PrarT5rKwHsNiGqrqQeD+kWsHbwYmjjIuBd4E3FpVjzJcBH4N8O1prn4bVvdztXik/ScM7zLmhar6GcO74vcCPwN+kORQeOzc/YvaopcxnJ6B4XTMhDuAPZJs3u6o2m+SzWzN8Mf0YJIdGb6/ZD67FPi9JJsmWcBwdHl5m3cZcDSrw+IYpvcGYhvgnvZaejPDKS6YZ68HPWUXAG9rR+kk2TnJs4DvAK9NskWb97tzWeQow2Jyz0hy18jwJwz/yE9Kch2wJ8N1C6rqdoZ3CZe0x36b4V33/dPc1nEMp3Ou5PE9Tn4N+I/z6QJ3VV3NcEroDQzXZo5Mci3DdZuJ7xc5GviTtp+eCzzYHnsncDbDhb2zWX34Pbr+a1v79xlOYX1njE9nJpzDsD+uBb7JcE3lh23epcBmVbUCuArYnumFxaeBxW2/Pp/V70SvAx5Jcu16fIF7sr+rjVJVfYPhNf7dJNcDXwG2qqorGPq9uw74P8D1tL+hueYnuDWj2h1gP6+qSnI4w8Vuv6hKmqYkW1bVT9vf0iXAkqq6aq7r8gK3ZtpvMFygDfAA8La5LUda75zabo7YAlg6H4ICPLKQJE2D1ywkSV2GhSSpy7CQJHUZFpKkLsNCG4SRDgvH3pV31qFb7SQHtztbeus8Jsn32/quSLK2HonXtp6n1LV9km2T/NGTfbw2XIaFNjQLmZ2uvE+u6XWrfTBD/1ZTSvIO4HeAvVrXKvsxfNDzydiXp9a1/baAYaEnMCy0oXlcV96tK46TRrqCfjs89g784iTnJrktyYlJ3pjk8tbd+HPWZaNJ/qBt49okX03yjPYO/3UMn/y/Zi3r/HPgD6vqxwBV9eOqWtrWu1+G7uuvT3J6ks1b++1JPpChG/Prkzw/k3Rtn2RBq+eKNuzTHn9cW99F7flPdHB4IvCc9viT1mUfaAM3193eOjjMxMDUXaEvAf5bG98cWA7s1pZ7gKFn280Z+uf6QFvu3bQu6KfY1nGs0a028MyR+R8C3tXGPw8cspZ1bQ3cP8W8LYA7gee16TOAo9v47SPb+CPgsyO1HTOyji8CL2/jzwZuHlnu79tz3wG4j6HH24XADXP9+3SYf4Of4NaGbn/ghUkOadPbMHQF/S/AFVV1DwxfVgV8oy1zPbC2b62DNbrVTvLKJB9iOI2zJUNHcU/VrwE/qKr/16aXMvT6+7E2/Tft55XA66dYx79n6MBxYnrric7rgL+tqoeAh5Lcy/CdHNKkDAtt6MLwDvxx/7yT7As8NNL06Mj0o6z738bngYOr6toM3wC473QeVFU/TvLTJP+6qm5bx21O1PsIU9e7CbB3Dd+P8JgWHqPPf23rkLxmoQ3Oml15XwD8YZKnASR5XpJfHsN2twLuadt541rqmcxfA59KsnWrcct2N9QtwMIkz23LjXaNP5U1t/cN4F0TExm+VGldHi8BhoU2PGt25f1Zhm+ruyrJDcD/ZDzvoP8C+B5Dt+rfH2k/E/jTdpF6qgvcnwG+BVzRarwUeLQdDRzB0IX99QxHPP+jU8eaXdv/MbCoXdy/ieEC+JSq6j7gOxm+V9wL3HqMHQlKkro8spAkdXlBS5pCkv8KHLpG85er6oQnub5PAfus0fzxqvrck1mfNJs8DSVJ6vI0lCSpy7CQJHUZFpKkLsNCktRlWEiSuv4/PxC+uKZCii4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(x='Item_Fat_Content', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11de0b29",
   "metadata": {},
   "source": [
    "# Item_Type Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fd9dae19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "ax = sns.countplot(x='Item_Type', data=df)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9418a1d3",
   "metadata": {},
   "source": [
    "# Outlet_Size Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b536f15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(x='Outlet_Size', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea315aa3",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0741772f",
   "metadata": {},
   "source": [
    "# Convert text to numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2b68eb3",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "95f4416f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({'Item_Fat_Content' : {'low fat' : 'Low Fat', 'LF' : 'Low Fat', \n",
    "                    'reg' : 'Regular'}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "996dd1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9ef657",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d92a81a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c72cb359",
   "metadata": {},
   "outputs": [],
   "source": [
    "el = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e6c869d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Item_Identifier'] = el.fit_transform(df['Item_Identifier'])\n",
    "df['Item_Fat_Content'] = el.fit_transform(df['Item_Fat_Content'])\n",
    "df['Item_Type'] = el.fit_transform(df['Item_Type'])\n",
    "df['Outlet_Identifier'] = el.fit_transform(df['Outlet_Identifier'])\n",
    "df['Outlet_Size'] = el.fit_transform(df['Outlet_Size'])\n",
    "df['Outlet_Location_Type'] = el.fit_transform(df['Outlet_Location_Type'])\n",
    "df['Outlet_Type'] = el.fit_transform(df['Outlet_Type'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b531cfc",
   "metadata": {},
   "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>3</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            3   \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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439753ca",
   "metadata": {},
   "source": [
    "# Seperating Dependent and Independent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cae787f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns='Item_Outlet_Sales', axis=1)\n",
    "Y = df['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8d74251b",
   "metadata": {},
   "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               3                     2            0  \n",
      "4               0                     2            1  \n",
      "...           ...                   ...          ...  \n",
      "8518            0                     2            1  \n",
      "8519            3                     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": 31,
   "id": "caa18a09",
   "metadata": {},
   "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",
   "id": "40e50abf",
   "metadata": {},
   "source": [
    "# Splitting Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "26abcdc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "99684a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b5c0b92d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 11) (6818, 11) (1705,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae18e3bb",
   "metadata": {},
   "source": [
    "# Train Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b53a175c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2e968f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "011297c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on Training data :  0.5012583266882864\n"
     ]
    }
   ],
   "source": [
    "lr_train_pred = lr_model.predict(X_train)\n",
    "lr_train_r2_score = r2_score(Y_train, lr_train_pred)\n",
    "print(\"R2 Score on Training data : \", lr_train_r2_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "484c0ae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on Training data :  0.5048147459132308\n"
     ]
    }
   ],
   "source": [
    "lr_test_pred = lr_model.predict(X_test)\n",
    "lr_test_r2_score = r2_score(Y_test, lr_test_pred)\n",
    "print(\"R2 Score on Training data : \", lr_test_r2_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c98ffff",
   "metadata": {},
   "source": [
    "# Apply GridSearchCV for improve performance of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b5aa2136",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7c76dcd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LinearRegression(),\n",
       "             param_grid={'fit_intercept': [True, False],\n",
       "                         'normalize': [True, False]})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {\n",
    "    'fit_intercept': [True, False],\n",
    "    'normalize': [True, False]\n",
    "}\n",
    "\n",
    "lr = LinearRegression()\n",
    "grid_search = GridSearchCV(estimator=lr, param_grid=parameters, cv=5)\n",
    "\n",
    "grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0756427f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fit_intercept': False, 'normalize': True}\n",
      "0.4992946707808176\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a6a713b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on Training data :  0.5011729270604669\n"
     ]
    }
   ],
   "source": [
    "lr_train_pred = grid_search.predict(X_train)\n",
    "lr_train_r2_score = r2_score(Y_train, lr_train_pred)\n",
    "print(\"R2 Score on Training data : \", lr_train_r2_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dcf8dc2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on Training data :  0.5044241148425286\n"
     ]
    }
   ],
   "source": [
    "lr_test_pred = grid_search.predict(X_test)\n",
    "lr_test_r2_score = r2_score(Y_test, lr_test_pred)\n",
    "print(\"R2 Score on Training data : \", lr_test_r2_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2760bc97",
   "metadata": {},
   "source": [
    "# Ridge Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4a31e3a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={'alpha': [0.1, 1.0, 10.0],\n",
       "                         'fit_intercept': [True, False],\n",
       "                         'normalize': [True, False]})"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso\n",
    "param_grid = {\n",
    "    'alpha': [0.1, 1.0, 10.0],\n",
    "    'fit_intercept': [True, False],\n",
    "    'normalize': [True, False]\n",
    "}\n",
    "\n",
    "# grid_search = GridSearchCV(Ridge(), param_grid, cv=5)\n",
    "grid_search = GridSearchCV(Lasso(), param_grid, cv=5)\n",
    "\n",
    "grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "aa2fd156",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.1, 'fit_intercept': True, 'normalize': True}\n",
      "0.49938746961947\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b098f9",
   "metadata": {},
   "source": [
    "# Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "02258412",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2ff0189b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d3c2f814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "92b1af31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on Training data :  0.5011729270604669\n"
     ]
    }
   ],
   "source": [
    "rf_train_pred = rf.predict(X_train)\n",
    "rf_train_r2_score = r2_score(Y_train, lr_train_pred)\n",
    "print(\"R2 Score on Training data : \", rf_train_r2_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d2cd71b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on Training data :  0.5044241148425286\n"
     ]
    }
   ],
   "source": [
    "rf_test_pred = rf.predict(X_test)\n",
    "rf_test_r2_score = r2_score(Y_test, lr_test_pred)\n",
    "print(\"R2 Score on Training data : \", rf_test_r2_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2edf7c9",
   "metadata": {},
   "source": [
    "# Apply GridSearchCV for improve performance of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a2eaa5e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': [None, 2, 4, 6, 8],\n",
       "                         'min_samples_leaf': [1, 2, 3, 4, 5],\n",
       "                         'min_samples_split': [2, 4, 6, 8, 10],\n",
       "                         'n_estimators': [10, 50, 100, 200]},\n",
       "             scoring='r2')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [10, 50, 100, 200],\n",
    "    'max_depth': [None, 2, 4, 6, 8],\n",
    "    'min_samples_split': [2, 4, 6, 8, 10],\n",
    "    'min_samples_leaf': [1, 2, 3, 4, 5]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(RandomForestRegressor(), param_grid, cv=5, scoring='r2')\n",
    "grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3f367e39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 6, 'min_samples_leaf': 4, 'min_samples_split': 2, 'n_estimators': 50}\n",
      "0.5956585212239676\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)\n",
    "print(grid_search.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "66469cb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on Training data :  0.5011729270604669\n"
     ]
    }
   ],
   "source": [
    "rf_train_pred = grid_search.predict(X_train)\n",
    "rf_train_r2_score = r2_score(Y_train, lr_train_pred)\n",
    "print(\"R2 Score on Training data : \", rf_train_r2_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e114cfb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on Training data :  0.5044241148425286\n"
     ]
    }
   ],
   "source": [
    "rf_test_pred = grid_search.predict(X_test)\n",
    "rf_test_r2_score = r2_score(Y_test, lr_test_pred)\n",
    "print(\"R2 Score on Training data : \", rf_test_r2_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c2903ab2",
   "metadata": {},
   "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>3</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            3   \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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97a7f723",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}