BigmartSales_KavyaSree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee52ac74",
   "metadata": {},
   "source": [
    "# Import libreries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0de1199f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import fuzzywuzzy\n",
    "from fuzzywuzzy import process\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score,accuracy_score,mean_squared_error\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.linear_model import Lasso, Ridge\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc6351c",
   "metadata": {},
   "source": [
    "# Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0e65432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(r\"C:\\Users\\Dell\\Downloads\\9961_14084_bundle_archive\\Train.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e09293ed",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6cd57259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8de249b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "962300fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8523</td>\n",
       "      <td>8523</td>\n",
       "      <td>8523</td>\n",
       "      <td>8523</td>\n",
       "      <td>6113</td>\n",
       "      <td>8523</td>\n",
       "      <td>8523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1559</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>FDW13</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10</td>\n",
       "      <td>5089</td>\n",
       "      <td>1232</td>\n",
       "      <td>935</td>\n",
       "      <td>2793</td>\n",
       "      <td>3350</td>\n",
       "      <td>5577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Identifier Item_Fat_Content              Item_Type  \\\n",
       "count             8523             8523                   8523   \n",
       "unique            1559                5                     16   \n",
       "top              FDW13          Low Fat  Fruits and Vegetables   \n",
       "freq                10             5089                   1232   \n",
       "\n",
       "       Outlet_Identifier Outlet_Size Outlet_Location_Type        Outlet_Type  \n",
       "count               8523        6113                 8523               8523  \n",
       "unique                10           3                    3                  4  \n",
       "top               OUT027      Medium               Tier 3  Supermarket Type1  \n",
       "freq                 935        2793                 3350               5577  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=\"object\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7667e841",
   "metadata": {},
   "source": [
    "# Handling null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "48cb45cb",
   "metadata": {
    "scrolled": true
   },
   "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                8523 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": [
    "data[\"Item_Weight\"]=data[\"Item_Weight\"].fillna(data[\"Item_Weight\"].mean())\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4917c740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Medium', nan, 'High', 'Small'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Outlet_Size\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8b73324",
   "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                8523 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                8523 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": [
    "data[\"Outlet_Size\"]=data[\"Outlet_Size\"].fillna(data[\"Outlet_Size\"].mode()[0])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "348a49a5",
   "metadata": {},
   "source": [
    "# EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "57c85ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>Medium</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      Medium               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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0aed2a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.set_index(\"Item_Identifier\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79b13a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\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>DRC01</th>\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>FDN15</th>\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>FDX07</th>\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>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\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_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "Item_Identifier                                                  \n",
       "FDA15                   9.30          Low Fat         0.016047   \n",
       "DRC01                   5.92          Regular         0.019278   \n",
       "FDN15                  17.50          Low Fat         0.016760   \n",
       "FDX07                  19.20          Regular         0.000000   \n",
       "NCD19                   8.93          Low Fat         0.000000   \n",
       "\n",
       "                             Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "Item_Identifier                                                      \n",
       "FDA15                            Dairy  249.8092            OUT049   \n",
       "DRC01                      Soft Drinks   48.2692            OUT018   \n",
       "FDN15                             Meat  141.6180            OUT049   \n",
       "FDX07            Fruits and Vegetables  182.0950            OUT010   \n",
       "NCD19                        Household   53.8614            OUT013   \n",
       "\n",
       "                 Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "Item_Identifier                                                               \n",
       "FDA15                                 1999      Medium               Tier 1   \n",
       "DRC01                                 2009      Medium               Tier 3   \n",
       "FDN15                                 1999      Medium               Tier 1   \n",
       "FDX07                                 1998      Medium               Tier 3   \n",
       "NCD19                                 1987        High               Tier 3   \n",
       "\n",
       "                       Outlet_Type  Item_Outlet_Sales  \n",
       "Item_Identifier                                        \n",
       "FDA15            Supermarket Type1          3735.1380  \n",
       "DRC01            Supermarket Type2           443.4228  \n",
       "FDN15            Supermarket Type1          2097.2700  \n",
       "FDX07                Grocery Store           732.3800  \n",
       "NCD19            Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d37710f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 483.,  870.,  865.,  664., 2242.,  629.,  755.,  735.,  677.,\n",
       "         603.]),\n",
       " array([ 4.555 ,  6.2345,  7.914 ,  9.5935, 11.273 , 12.9525, 14.632 ,\n",
       "        16.3115, 17.991 , 19.6705, 21.35  ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data[\"Item_Weight\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6dbae692",
   "metadata": {},
   "outputs": [],
   "source": [
    "fats=data.Item_Fat_Content.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72de0f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_matches_in_column(df, column, string_to_match, min_ratio):\n",
    "    # get a list of unique strings\n",
    "    strings = df[column].unique()\n",
    "    \n",
    "    # get the top 10 closest matches to our input string\n",
    "    matches = fuzzywuzzy.process.extract(string_to_match, strings, \n",
    "                                         limit=10, scorer=fuzzywuzzy.fuzz.token_sort_ratio)\n",
    "\n",
    "    # only get matches with a ratio > 90\n",
    "    close_matches = [matches[0] for matches in matches if matches[1] >= min_ratio]\n",
    "\n",
    "    # get the rows of all the close matches in our dataframe\n",
    "    rows_with_matches = df[column].isin(close_matches)\n",
    "\n",
    "    # replace all rows with close matches with the input matches \n",
    "    df.loc[rows_with_matches, column] = string_to_match\n",
    "    \n",
    "    # let us know the function's done\n",
    "    print(\"All done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66160a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Low Fat', 100), ('low fat', 100), ('LF', 22), ('Regular', 14), ('reg', 0)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = fuzzywuzzy.process.extract(\"low fat\", fats, limit=10, scorer=fuzzywuzzy.fuzz.token_sort_ratio)\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6975916",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All done!\n",
      "All done!\n"
     ]
    }
   ],
   "source": [
    "replace_matches_in_column(data,\"Item_Fat_Content\", \"low fat\", 20)\n",
    "replace_matches_in_column(data,\"Item_Fat_Content\", \"Regular\", 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5dd9f8d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['low fat', 'Regular'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Item_Fat_Content\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2af0b2ef",
   "metadata": {
    "scrolled": true
   },
   "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_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",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\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>DRC01</th>\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>FDN15</th>\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>FDX07</th>\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>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\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_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "Item_Identifier                                                  \n",
       "FDA15                   9.30          low fat         0.016047   \n",
       "DRC01                   5.92          Regular         0.019278   \n",
       "FDN15                  17.50          low fat         0.016760   \n",
       "FDX07                  19.20          Regular         0.000000   \n",
       "NCD19                   8.93          low fat         0.000000   \n",
       "\n",
       "                             Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "Item_Identifier                                                      \n",
       "FDA15                            Dairy  249.8092            OUT049   \n",
       "DRC01                      Soft Drinks   48.2692            OUT018   \n",
       "FDN15                             Meat  141.6180            OUT049   \n",
       "FDX07            Fruits and Vegetables  182.0950            OUT010   \n",
       "NCD19                        Household   53.8614            OUT013   \n",
       "\n",
       "                 Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "Item_Identifier                                                               \n",
       "FDA15                                 1999      Medium               Tier 1   \n",
       "DRC01                                 2009      Medium               Tier 3   \n",
       "FDN15                                 1999      Medium               Tier 1   \n",
       "FDX07                                 1998      Medium               Tier 3   \n",
       "NCD19                                 1987        High               Tier 3   \n",
       "\n",
       "                       Outlet_Type  Item_Outlet_Sales  \n",
       "Item_Identifier                                        \n",
       "FDA15            Supermarket Type1          3735.1380  \n",
       "DRC01            Supermarket Type2           443.4228  \n",
       "FDN15            Supermarket Type1          2097.2700  \n",
       "FDX07                Grocery Store           732.3800  \n",
       "NCD19            Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4e8bdcb8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables',\n",
       "       'Household', 'Baking Goods', 'Snack Foods', 'Frozen Foods',\n",
       "       'Breakfast', 'Health and Hygiene', 'Hard Drinks', 'Canned',\n",
       "       'Breads', 'Starchy Foods', 'Others', 'Seafood'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Item_Type\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2985b8",
   "metadata": {},
   "source": [
    "We can see from the above plot that house hold foods are always of low fat content. So, prefer them to stay healthy..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7e7b357d",
   "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_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",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\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>DRC01</th>\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>FDN15</th>\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>FDX07</th>\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>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\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_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "Item_Identifier                                                  \n",
       "FDA15                   9.30          low fat         0.016047   \n",
       "DRC01                   5.92          Regular         0.019278   \n",
       "FDN15                  17.50          low fat         0.016760   \n",
       "FDX07                  19.20          Regular         0.000000   \n",
       "NCD19                   8.93          low fat         0.000000   \n",
       "\n",
       "                             Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "Item_Identifier                                                      \n",
       "FDA15                            Dairy  249.8092            OUT049   \n",
       "DRC01                      Soft Drinks   48.2692            OUT018   \n",
       "FDN15                             Meat  141.6180            OUT049   \n",
       "FDX07            Fruits and Vegetables  182.0950            OUT010   \n",
       "NCD19                        Household   53.8614            OUT013   \n",
       "\n",
       "                 Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "Item_Identifier                                                               \n",
       "FDA15                                 1999      Medium               Tier 1   \n",
       "DRC01                                 2009      Medium               Tier 3   \n",
       "FDN15                                 1999      Medium               Tier 1   \n",
       "FDX07                                 1998      Medium               Tier 3   \n",
       "NCD19                                 1987        High               Tier 3   \n",
       "\n",
       "                       Outlet_Type  Item_Outlet_Sales  \n",
       "Item_Identifier                                        \n",
       "FDA15            Supermarket Type1          3735.1380  \n",
       "DRC01            Supermarket Type2           443.4228  \n",
       "FDN15            Supermarket Type1          2097.2700  \n",
       "FDX07                Grocery Store           732.3800  \n",
       "NCD19            Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "466b6d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr=data.corr()\n",
    "sns.heatmap(corr, cmap='coolwarm', annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1127b470",
   "metadata": {},
   "source": [
    "# Normalizing numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc7f3555",
   "metadata": {
    "scrolled": true
   },
   "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>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC01</th>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDN15</th>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDX07</th>\n",
       "      <td>19.20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\n",
       "      <td>8.93</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight  Item_Visibility  Item_MRP  Item_Outlet_Sales\n",
       "Item_Identifier                                                           \n",
       "FDA15                   9.30         0.016047  249.8092          3735.1380\n",
       "DRC01                   5.92         0.019278   48.2692           443.4228\n",
       "FDN15                  17.50         0.016760  141.6180          2097.2700\n",
       "FDX07                  19.20         0.000000  182.0950           732.3800\n",
       "NCD19                   8.93         0.000000   53.8614           994.7052"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_df=data.select_dtypes(include=\"number\")\n",
    "numerical_df.drop(\"Outlet_Establishment_Year\",axis=1,inplace=True)\n",
    "numerical_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e2489f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = plt.GridSpec(2,2, hspace=0.5,  wspace=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "779b588b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=0\n",
    "for i in range(2):\n",
    "    for j in range(2):    \n",
    "        plt.subplot(grid[i,j])\n",
    "        plt.hist(numerical_df.iloc[:,k])\n",
    "        k+=1\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c69e233",
   "metadata": {},
   "source": [
    "All these columns are not normal.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "50e643f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Weight          False\n",
       "Item_Visibility       True\n",
       "Item_MRP             False\n",
       "Item_Outlet_Sales    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(numerical_df <= 0).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "48bf649b",
   "metadata": {
    "scrolled": true
   },
   "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_MRP</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\n",
       "      <td>5.530263</td>\n",
       "      <td>65.652445</td>\n",
       "      <td>47.007885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC01</th>\n",
       "      <td>3.609073</td>\n",
       "      <td>19.920687</td>\n",
       "      <td>20.958294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDN15</th>\n",
       "      <td>9.519719</td>\n",
       "      <td>43.751419</td>\n",
       "      <td>37.964893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDX07</th>\n",
       "      <td>10.272881</td>\n",
       "      <td>52.395806</td>\n",
       "      <td>25.485930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\n",
       "      <td>5.330905</td>\n",
       "      <td>21.614642</td>\n",
       "      <td>28.660858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight   Item_MRP  Item_Outlet_Sales\n",
       "Item_Identifier                                           \n",
       "FDA15               5.530263  65.652445          47.007885\n",
       "DRC01               3.609073  19.920687          20.958294\n",
       "FDN15               9.519719  43.751419          37.964893\n",
       "FDX07              10.272881  52.395806          25.485930\n",
       "NCD19               5.330905  21.614642          28.660858"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_df = numerical_df[[\"Item_Weight\",\"Item_MRP\",\"Item_Outlet_Sales\"]].apply(lambda x: stats.boxcox(x)[0])\n",
    "transformed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21b69b26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=0\n",
    "for i in range(2):\n",
    "    for j in range(2): \n",
    "        if(k<=2):\n",
    "            plt.subplot(grid[i,j])\n",
    "            plt.hist(transformed_df.iloc[:,k])\n",
    "            k+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ff4c1ddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[[\"Item_Weight\",\"Item_MRP\",\"Item_Outlet_Sales\"]]=transformed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "172ef958",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "normalized_data = scaler.fit_transform(numerical_df[[\"Item_Visibility\"]])\n",
    "data[\"Item_Visibility\"]=normalized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b0b56861",
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_df=data.select_dtypes(include=\"number\")\n",
    "numerical_df.drop(\"Outlet_Establishment_Year\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "567adacb",
   "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_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",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\n",
       "      <td>5.530263</td>\n",
       "      <td>low fat</td>\n",
       "      <td>-0.970732</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>65.652445</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>47.007885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC01</th>\n",
       "      <td>3.609073</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.908111</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>19.920687</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>20.958294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDN15</th>\n",
       "      <td>9.519719</td>\n",
       "      <td>low fat</td>\n",
       "      <td>-0.956917</td>\n",
       "      <td>Meat</td>\n",
       "      <td>43.751419</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>37.964893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDX07</th>\n",
       "      <td>10.272881</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>52.395806</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>25.485930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\n",
       "      <td>5.330905</td>\n",
       "      <td>low fat</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>Household</td>\n",
       "      <td>21.614642</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>28.660858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "Item_Identifier                                                  \n",
       "FDA15               5.530263          low fat        -0.970732   \n",
       "DRC01               3.609073          Regular        -0.908111   \n",
       "FDN15               9.519719          low fat        -0.956917   \n",
       "FDX07              10.272881          Regular        -1.281758   \n",
       "NCD19               5.330905          low fat        -1.281758   \n",
       "\n",
       "                             Item_Type   Item_MRP Outlet_Identifier  \\\n",
       "Item_Identifier                                                       \n",
       "FDA15                            Dairy  65.652445            OUT049   \n",
       "DRC01                      Soft Drinks  19.920687            OUT018   \n",
       "FDN15                             Meat  43.751419            OUT049   \n",
       "FDX07            Fruits and Vegetables  52.395806            OUT010   \n",
       "NCD19                        Household  21.614642            OUT013   \n",
       "\n",
       "                 Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "Item_Identifier                                                               \n",
       "FDA15                                 1999      Medium               Tier 1   \n",
       "DRC01                                 2009      Medium               Tier 3   \n",
       "FDN15                                 1999      Medium               Tier 1   \n",
       "FDX07                                 1998      Medium               Tier 3   \n",
       "NCD19                                 1987        High               Tier 3   \n",
       "\n",
       "                       Outlet_Type  Item_Outlet_Sales  \n",
       "Item_Identifier                                        \n",
       "FDA15            Supermarket Type1          47.007885  \n",
       "DRC01            Supermarket Type2          20.958294  \n",
       "FDN15            Supermarket Type1          37.964893  \n",
       "FDX07                Grocery Store          25.485930  \n",
       "NCD19            Supermarket Type1          28.660858  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0d78011a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Fat_Content', ylabel='count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,x=\"Item_Fat_Content\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "86af8d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15]),\n",
       " [Text(0, 0, 'Dairy'),\n",
       "  Text(1, 0, 'Soft Drinks'),\n",
       "  Text(2, 0, 'Meat'),\n",
       "  Text(3, 0, 'Fruits and Vegetables'),\n",
       "  Text(4, 0, 'Household'),\n",
       "  Text(5, 0, 'Baking Goods'),\n",
       "  Text(6, 0, 'Snack Foods'),\n",
       "  Text(7, 0, 'Frozen Foods'),\n",
       "  Text(8, 0, 'Breakfast'),\n",
       "  Text(9, 0, 'Health and Hygiene'),\n",
       "  Text(10, 0, 'Hard Drinks'),\n",
       "  Text(11, 0, 'Canned'),\n",
       "  Text(12, 0, 'Breads'),\n",
       "  Text(13, 0, 'Starchy Foods'),\n",
       "  Text(14, 0, 'Others'),\n",
       "  Text(15, 0, 'Seafood')])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,x=\"Item_Type\")\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4e80509d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outlet_Size', ylabel='count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,x='Outlet_Size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "682e3fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outlet_Location_Type', ylabel='count'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,x='Outlet_Location_Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "987bf690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]),\n",
       " [Text(0, 0, 'Supermarket Type1'),\n",
       "  Text(1, 0, 'Supermarket Type2'),\n",
       "  Text(2, 0, 'Grocery Store'),\n",
       "  Text(3, 0, 'Supermarket Type3')])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,x=\"Outlet_Type\")\n",
    "plt.xticks(rotation=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b9b5b1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " [Text(0, 0, 'OUT049'),\n",
       "  Text(1, 0, 'OUT018'),\n",
       "  Text(2, 0, 'OUT010'),\n",
       "  Text(3, 0, 'OUT013'),\n",
       "  Text(4, 0, 'OUT027'),\n",
       "  Text(5, 0, 'OUT045'),\n",
       "  Text(6, 0, 'OUT017'),\n",
       "  Text(7, 0, 'OUT046'),\n",
       "  Text(8, 0, 'OUT035'),\n",
       "  Text(9, 0, 'OUT019')])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data,x=\"Outlet_Identifier\")\n",
    "plt.xticks(rotation=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fd624af9",
   "metadata": {
    "scrolled": true
   },
   "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>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\n",
       "      <td>5.530263</td>\n",
       "      <td>-0.970732</td>\n",
       "      <td>65.652445</td>\n",
       "      <td>47.007885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC01</th>\n",
       "      <td>3.609073</td>\n",
       "      <td>-0.908111</td>\n",
       "      <td>19.920687</td>\n",
       "      <td>20.958294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDN15</th>\n",
       "      <td>9.519719</td>\n",
       "      <td>-0.956917</td>\n",
       "      <td>43.751419</td>\n",
       "      <td>37.964893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDX07</th>\n",
       "      <td>10.272881</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>52.395806</td>\n",
       "      <td>25.485930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\n",
       "      <td>5.330905</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>21.614642</td>\n",
       "      <td>28.660858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight  Item_Visibility   Item_MRP  Item_Outlet_Sales\n",
       "Item_Identifier                                                            \n",
       "FDA15               5.530263        -0.970732  65.652445          47.007885\n",
       "DRC01               3.609073        -0.908111  19.920687          20.958294\n",
       "FDN15               9.519719        -0.956917  43.751419          37.964893\n",
       "FDX07              10.272881        -1.281758  52.395806          25.485930\n",
       "NCD19               5.330905        -1.281758  21.614642          28.660858"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5466ae87",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = plt.GridSpec(2,2, hspace=0.5,  wspace=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "385a4564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plots\n",
    "k=0\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax = plt.subplot(grid[i, j])\n",
    "        ax.scatter(range(len(numerical_df)), numerical_df.iloc[:, k])\n",
    "        k += 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "db19c3b1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15]),\n",
       " [Text(0, 0, 'Dairy'),\n",
       "  Text(1, 0, 'Soft Drinks'),\n",
       "  Text(2, 0, 'Meat'),\n",
       "  Text(3, 0, 'Fruits and Vegetables'),\n",
       "  Text(4, 0, 'Household'),\n",
       "  Text(5, 0, 'Baking Goods'),\n",
       "  Text(6, 0, 'Snack Foods'),\n",
       "  Text(7, 0, 'Frozen Foods'),\n",
       "  Text(8, 0, 'Breakfast'),\n",
       "  Text(9, 0, 'Health and Hygiene'),\n",
       "  Text(10, 0, 'Hard Drinks'),\n",
       "  Text(11, 0, 'Canned'),\n",
       "  Text(12, 0, 'Breads'),\n",
       "  Text(13, 0, 'Starchy Foods'),\n",
       "  Text(14, 0, 'Others'),\n",
       "  Text(15, 0, 'Seafood')])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data, x='Item_Type', hue='Item_Fat_Content')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b481d4c3",
   "metadata": {},
   "source": [
    "Home food is the best!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8ced8b32",
   "metadata": {
    "scrolled": true
   },
   "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_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",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\n",
       "      <td>5.530263</td>\n",
       "      <td>low fat</td>\n",
       "      <td>-0.970732</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>65.652445</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>47.007885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC01</th>\n",
       "      <td>3.609073</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.908111</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>19.920687</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>20.958294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDN15</th>\n",
       "      <td>9.519719</td>\n",
       "      <td>low fat</td>\n",
       "      <td>-0.956917</td>\n",
       "      <td>Meat</td>\n",
       "      <td>43.751419</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>37.964893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDX07</th>\n",
       "      <td>10.272881</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>52.395806</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>25.485930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\n",
       "      <td>5.330905</td>\n",
       "      <td>low fat</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>Household</td>\n",
       "      <td>21.614642</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>28.660858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "Item_Identifier                                                  \n",
       "FDA15               5.530263          low fat        -0.970732   \n",
       "DRC01               3.609073          Regular        -0.908111   \n",
       "FDN15               9.519719          low fat        -0.956917   \n",
       "FDX07              10.272881          Regular        -1.281758   \n",
       "NCD19               5.330905          low fat        -1.281758   \n",
       "\n",
       "                             Item_Type   Item_MRP Outlet_Identifier  \\\n",
       "Item_Identifier                                                       \n",
       "FDA15                            Dairy  65.652445            OUT049   \n",
       "DRC01                      Soft Drinks  19.920687            OUT018   \n",
       "FDN15                             Meat  43.751419            OUT049   \n",
       "FDX07            Fruits and Vegetables  52.395806            OUT010   \n",
       "NCD19                        Household  21.614642            OUT013   \n",
       "\n",
       "                 Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "Item_Identifier                                                               \n",
       "FDA15                                 1999      Medium               Tier 1   \n",
       "DRC01                                 2009      Medium               Tier 3   \n",
       "FDN15                                 1999      Medium               Tier 1   \n",
       "FDX07                                 1998      Medium               Tier 3   \n",
       "NCD19                                 1987        High               Tier 3   \n",
       "\n",
       "                       Outlet_Type  Item_Outlet_Sales  \n",
       "Item_Identifier                                        \n",
       "FDA15            Supermarket Type1          47.007885  \n",
       "DRC01            Supermarket Type2          20.958294  \n",
       "FDN15            Supermarket Type1          37.964893  \n",
       "FDX07                Grocery Store          25.485930  \n",
       "NCD19            Supermarket Type1          28.660858  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "06ccc60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_columns=[\"Item_Fat_Content\",\"Item_Type\",\"Outlet_Identifier\",\"Outlet_Size\",\"Outlet_Location_Type\",\"Outlet_Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "77f45c87",
   "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",
       "      <th>Item_Fat_Content_Regular</th>\n",
       "      <th>Item_Fat_Content_low fat</th>\n",
       "      <th>Item_Type_Baking Goods</th>\n",
       "      <th>Item_Type_Breads</th>\n",
       "      <th>Item_Type_Breakfast</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Size_High</th>\n",
       "      <th>Outlet_Size_Medium</th>\n",
       "      <th>Outlet_Size_Small</th>\n",
       "      <th>Outlet_Location_Type_Tier 1</th>\n",
       "      <th>Outlet_Location_Type_Tier 2</th>\n",
       "      <th>Outlet_Location_Type_Tier 3</th>\n",
       "      <th>Outlet_Type_Grocery Store</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDA15</th>\n",
       "      <td>5.530263</td>\n",
       "      <td>-0.970732</td>\n",
       "      <td>65.652445</td>\n",
       "      <td>1999</td>\n",
       "      <td>47.007885</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC01</th>\n",
       "      <td>3.609073</td>\n",
       "      <td>-0.908111</td>\n",
       "      <td>19.920687</td>\n",
       "      <td>2009</td>\n",
       "      <td>20.958294</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDN15</th>\n",
       "      <td>9.519719</td>\n",
       "      <td>-0.956917</td>\n",
       "      <td>43.751419</td>\n",
       "      <td>1999</td>\n",
       "      <td>37.964893</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDX07</th>\n",
       "      <td>10.272881</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>52.395806</td>\n",
       "      <td>1998</td>\n",
       "      <td>25.485930</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCD19</th>\n",
       "      <td>5.330905</td>\n",
       "      <td>-1.281758</td>\n",
       "      <td>21.614642</td>\n",
       "      <td>1987</td>\n",
       "      <td>28.660858</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight  Item_Visibility   Item_MRP  \\\n",
       "Item_Identifier                                            \n",
       "FDA15               5.530263        -0.970732  65.652445   \n",
       "DRC01               3.609073        -0.908111  19.920687   \n",
       "FDN15               9.519719        -0.956917  43.751419   \n",
       "FDX07              10.272881        -1.281758  52.395806   \n",
       "NCD19               5.330905        -1.281758  21.614642   \n",
       "\n",
       "                 Outlet_Establishment_Year  Item_Outlet_Sales  \\\n",
       "Item_Identifier                                                 \n",
       "FDA15                                 1999          47.007885   \n",
       "DRC01                                 2009          20.958294   \n",
       "FDN15                                 1999          37.964893   \n",
       "FDX07                                 1998          25.485930   \n",
       "NCD19                                 1987          28.660858   \n",
       "\n",
       "                 Item_Fat_Content_Regular  Item_Fat_Content_low fat  \\\n",
       "Item_Identifier                                                       \n",
       "FDA15                                   0                         1   \n",
       "DRC01                                   1                         0   \n",
       "FDN15                                   0                         1   \n",
       "FDX07                                   1                         0   \n",
       "NCD19                                   0                         1   \n",
       "\n",
       "                 Item_Type_Baking Goods  Item_Type_Breads  \\\n",
       "Item_Identifier                                             \n",
       "FDA15                                 0                 0   \n",
       "DRC01                                 0                 0   \n",
       "FDN15                                 0                 0   \n",
       "FDX07                                 0                 0   \n",
       "NCD19                                 0                 0   \n",
       "\n",
       "                 Item_Type_Breakfast  ...  Outlet_Size_High  \\\n",
       "Item_Identifier                       ...                     \n",
       "FDA15                              0  ...                 0   \n",
       "DRC01                              0  ...                 0   \n",
       "FDN15                              0  ...                 0   \n",
       "FDX07                              0  ...                 0   \n",
       "NCD19                              0  ...                 1   \n",
       "\n",
       "                 Outlet_Size_Medium  Outlet_Size_Small  \\\n",
       "Item_Identifier                                          \n",
       "FDA15                             1                  0   \n",
       "DRC01                             1                  0   \n",
       "FDN15                             1                  0   \n",
       "FDX07                             1                  0   \n",
       "NCD19                             0                  0   \n",
       "\n",
       "                 Outlet_Location_Type_Tier 1  Outlet_Location_Type_Tier 2  \\\n",
       "Item_Identifier                                                             \n",
       "FDA15                                      1                            0   \n",
       "DRC01                                      0                            0   \n",
       "FDN15                                      1                            0   \n",
       "FDX07                                      0                            0   \n",
       "NCD19                                      0                            0   \n",
       "\n",
       "                 Outlet_Location_Type_Tier 3  Outlet_Type_Grocery Store  \\\n",
       "Item_Identifier                                                           \n",
       "FDA15                                      0                          0   \n",
       "DRC01                                      1                          0   \n",
       "FDN15                                      0                          0   \n",
       "FDX07                                      1                          1   \n",
       "NCD19                                      1                          0   \n",
       "\n",
       "                 Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "Item_Identifier                                                                 \n",
       "FDA15                                        1                              0   \n",
       "DRC01                                        0                              1   \n",
       "FDN15                                        1                              0   \n",
       "FDX07                                        0                              0   \n",
       "NCD19                                        1                              0   \n",
       "\n",
       "                 Outlet_Type_Supermarket Type3  \n",
       "Item_Identifier                                 \n",
       "FDA15                                        0  \n",
       "DRC01                                        0  \n",
       "FDN15                                        0  \n",
       "FDX07                                        0  \n",
       "NCD19                                        0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_data=pd.get_dummies(data,columns=cat_columns)\n",
    "one_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e7001270",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# #label encoding\n",
    "# columns_to_encode=[\"Item_Fat_Content\",\"Item_Type\",\"Outlet_Identifier\",\"Outlet_Size\",\"Outlet_Location_Type\",\"Outlet_Type\"]\n",
    "# label_encoder = LabelEncoder()\n",
    "# for column in columns_to_encode:\n",
    "#     data[column] = label_encoder.fit_transform(data[column])\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9389e9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking for outliers\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=len(numerical_df.columns), figsize=(12, 6))\n",
    "\n",
    "# Iterate over the columns and create a box plot for each column\n",
    "for i, column in enumerate(numerical_df.columns):\n",
    "    axes[i].boxplot(numerical_df[column])\n",
    "    axes[i].set_title(column)\n",
    "\n",
    "# Adjust the spacing between subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n",
    "#there are not many outliers and it is not good to remove these outliers in this case"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb6cec6",
   "metadata": {},
   "source": [
    "# So, now lets look into test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ba14f792",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDW58</td>\n",
       "      <td>20.750</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>8.300</td>\n",
       "      <td>reg</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>14.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>Others</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>7.315</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDW58       20.750          Low Fat         0.007565  Snack Foods   \n",
       "1           FDW14        8.300              reg         0.038428        Dairy   \n",
       "2           NCN55       14.600          Low Fat         0.099575       Others   \n",
       "3           FDQ58        7.315          Low Fat         0.015388  Snack Foods   \n",
       "4           FDY38          NaN          Regular         0.118599        Dairy   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  107.8622            OUT049                       1999      Medium   \n",
       "1   87.3198            OUT017                       2007         NaN   \n",
       "2  241.7538            OUT010                       1998         NaN   \n",
       "3  155.0340            OUT017                       2007         NaN   \n",
       "4  234.2300            OUT027                       1985      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  \n",
       "0               Tier 1  Supermarket Type1  \n",
       "1               Tier 2  Supermarket Type1  \n",
       "2               Tier 3      Grocery Store  \n",
       "3               Tier 2  Supermarket Type1  \n",
       "4               Tier 3  Supermarket Type3  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data=pd.read_csv(r\"C:\\Users\\Dell\\Downloads\\9961_14084_bundle_archive\\Test.csv\")\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fe0d272e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5681 entries, 0 to 5680\n",
      "Data columns (total 11 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            5681 non-null   object \n",
      " 1   Item_Weight                4705 non-null   float64\n",
      " 2   Item_Fat_Content           5681 non-null   object \n",
      " 3   Item_Visibility            5681 non-null   float64\n",
      " 4   Item_Type                  5681 non-null   object \n",
      " 5   Item_MRP                   5681 non-null   float64\n",
      " 6   Outlet_Identifier          5681 non-null   object \n",
      " 7   Outlet_Establishment_Year  5681 non-null   int64  \n",
      " 8   Outlet_Size                4075 non-null   object \n",
      " 9   Outlet_Location_Type       5681 non-null   object \n",
      " 10  Outlet_Type                5681 non-null   object \n",
      "dtypes: float64(3), int64(1), object(7)\n",
      "memory usage: 488.3+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "902d13b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5681 entries, 0 to 5680\n",
      "Data columns (total 11 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            5681 non-null   object \n",
      " 1   Item_Weight                5681 non-null   float64\n",
      " 2   Item_Fat_Content           5681 non-null   object \n",
      " 3   Item_Visibility            5681 non-null   float64\n",
      " 4   Item_Type                  5681 non-null   object \n",
      " 5   Item_MRP                   5681 non-null   float64\n",
      " 6   Outlet_Identifier          5681 non-null   object \n",
      " 7   Outlet_Establishment_Year  5681 non-null   int64  \n",
      " 8   Outlet_Size                5681 non-null   object \n",
      " 9   Outlet_Location_Type       5681 non-null   object \n",
      " 10  Outlet_Type                5681 non-null   object \n",
      "dtypes: float64(3), int64(1), object(7)\n",
      "memory usage: 488.3+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data[\"Item_Weight\"]=test_data[\"Item_Weight\"].fillna(test_data[\"Item_Weight\"].mean())\n",
    "test_data[\"Outlet_Size\"]=test_data[\"Outlet_Size\"].fillna(test_data[\"Outlet_Size\"].mode()[0])\n",
    "test_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db45b1de",
   "metadata": {},
   "source": [
    "# Splitting training data into train and validation data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e5c2fa14",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting training data into train and validation data sets\n",
    "y=one_data[\"Item_Outlet_Sales\"]\n",
    "x=one_data.drop(\"Item_Outlet_Sales\",axis=1)\n",
    "train_data, val_data, train_labels, val_labels = train_test_split(x, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ea87bae",
   "metadata": {},
   "source": [
    "# Linear Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f042aa71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6816022598309789\n",
      "45.88325084634301\n"
     ]
    }
   ],
   "source": [
    "#application of linear regression model\n",
    "model=LinearRegression()\n",
    "model.fit(train_data,train_labels)\n",
    "y_pred=model.predict(val_data)\n",
    "r2=r2_score(val_labels,y_pred)\n",
    "print(r2)\n",
    "mse = mean_squared_error(val_labels, y_pred)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ab5ec267",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDW58</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>reg</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>Others</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>7.315000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>12.695633</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDW58    20.750000          Low Fat         0.007565  Snack Foods   \n",
       "1           FDW14     8.300000              reg         0.038428        Dairy   \n",
       "2           NCN55    14.600000          Low Fat         0.099575       Others   \n",
       "3           FDQ58     7.315000          Low Fat         0.015388  Snack Foods   \n",
       "4           FDY38    12.695633          Regular         0.118599        Dairy   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  107.8622            OUT049                       1999      Medium   \n",
       "1   87.3198            OUT017                       2007      Medium   \n",
       "2  241.7538            OUT010                       1998      Medium   \n",
       "3  155.0340            OUT017                       2007      Medium   \n",
       "4  234.2300            OUT027                       1985      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  \n",
       "0               Tier 1  Supermarket Type1  \n",
       "1               Tier 2  Supermarket Type1  \n",
       "2               Tier 3      Grocery Store  \n",
       "3               Tier 2  Supermarket Type1  \n",
       "4               Tier 3  Supermarket Type3  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b9b687b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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_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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDW58</th>\n",
       "      <td>20.750000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDW14</th>\n",
       "      <td>8.300000</td>\n",
       "      <td>reg</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCN55</th>\n",
       "      <td>14.600000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>Others</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDQ58</th>\n",
       "      <td>7.315000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDY38</th>\n",
       "      <td>12.695633</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "Item_Identifier                                                               \n",
       "FDW58              20.750000          Low Fat         0.007565  Snack Foods   \n",
       "FDW14               8.300000              reg         0.038428        Dairy   \n",
       "NCN55              14.600000          Low Fat         0.099575       Others   \n",
       "FDQ58               7.315000          Low Fat         0.015388  Snack Foods   \n",
       "FDY38              12.695633          Regular         0.118599        Dairy   \n",
       "\n",
       "                 Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "Item_Identifier                                                          \n",
       "FDW58            107.8622            OUT049                       1999   \n",
       "FDW14             87.3198            OUT017                       2007   \n",
       "NCN55            241.7538            OUT010                       1998   \n",
       "FDQ58            155.0340            OUT017                       2007   \n",
       "FDY38            234.2300            OUT027                       1985   \n",
       "\n",
       "                Outlet_Size Outlet_Location_Type        Outlet_Type  \n",
       "Item_Identifier                                                      \n",
       "FDW58                Medium               Tier 1  Supermarket Type1  \n",
       "FDW14                Medium               Tier 2  Supermarket Type1  \n",
       "NCN55                Medium               Tier 3      Grocery Store  \n",
       "FDQ58                Medium               Tier 2  Supermarket Type1  \n",
       "FDY38                Medium               Tier 3  Supermarket Type3  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.set_index(\"Item_Identifier\",inplace=True)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d5ce90c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All done!\n",
      "All done!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['low fat', 'Regular'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fuzzywuzzy\n",
    "replace_matches_in_column(test_data,\"Item_Fat_Content\", \"low fat\", 20)\n",
    "replace_matches_in_column(test_data,\"Item_Fat_Content\", \"Regular\", 20)\n",
    "test_data[\"Item_Fat_Content\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "65ef825f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test_data_one=pd.get_dummies(test_data,columns=cat_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3c2ba434",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Outlet_Type_Supermarket Type2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FDW58</th>\n",
       "      <td>20.750000</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDW14</th>\n",
       "      <td>8.300000</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCN55</th>\n",
       "      <td>14.600000</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>1998</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDQ58</th>\n",
       "      <td>7.315000</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FDY38</th>\n",
       "      <td>12.695633</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight  Item_Visibility  Item_MRP  \\\n",
       "Item_Identifier                                           \n",
       "FDW58              20.750000         0.007565  107.8622   \n",
       "FDW14               8.300000         0.038428   87.3198   \n",
       "NCN55              14.600000         0.099575  241.7538   \n",
       "FDQ58               7.315000         0.015388  155.0340   \n",
       "FDY38              12.695633         0.118599  234.2300   \n",
       "\n",
       "                 Outlet_Establishment_Year  Item_Fat_Content_Regular  \\\n",
       "Item_Identifier                                                        \n",
       "FDW58                                 1999                         0   \n",
       "FDW14                                 2007                         1   \n",
       "NCN55                                 1998                         0   \n",
       "FDQ58                                 2007                         0   \n",
       "FDY38                                 1985                         1   \n",
       "\n",
       "                 Item_Fat_Content_low fat  Item_Type_Baking Goods  \\\n",
       "Item_Identifier                                                     \n",
       "FDW58                                   1                       0   \n",
       "FDW14                                   0                       0   \n",
       "NCN55                                   1                       0   \n",
       "FDQ58                                   1                       0   \n",
       "FDY38                                   0                       0   \n",
       "\n",
       "                 Item_Type_Breads  Item_Type_Breakfast  Item_Type_Canned  ...  \\\n",
       "Item_Identifier                                                           ...   \n",
       "FDW58                           0                    0                 0  ...   \n",
       "FDW14                           0                    0                 0  ...   \n",
       "NCN55                           0                    0                 0  ...   \n",
       "FDQ58                           0                    0                 0  ...   \n",
       "FDY38                           0                    0                 0  ...   \n",
       "\n",
       "                 Outlet_Size_High  Outlet_Size_Medium  Outlet_Size_Small  \\\n",
       "Item_Identifier                                                            \n",
       "FDW58                           0                   1                  0   \n",
       "FDW14                           0                   1                  0   \n",
       "NCN55                           0                   1                  0   \n",
       "FDQ58                           0                   1                  0   \n",
       "FDY38                           0                   1                  0   \n",
       "\n",
       "                 Outlet_Location_Type_Tier 1  Outlet_Location_Type_Tier 2  \\\n",
       "Item_Identifier                                                             \n",
       "FDW58                                      1                            0   \n",
       "FDW14                                      0                            1   \n",
       "NCN55                                      0                            0   \n",
       "FDQ58                                      0                            1   \n",
       "FDY38                                      0                            0   \n",
       "\n",
       "                 Outlet_Location_Type_Tier 3  Outlet_Type_Grocery Store  \\\n",
       "Item_Identifier                                                           \n",
       "FDW58                                      0                          0   \n",
       "FDW14                                      0                          0   \n",
       "NCN55                                      1                          1   \n",
       "FDQ58                                      0                          0   \n",
       "FDY38                                      1                          0   \n",
       "\n",
       "                 Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "Item_Identifier                                                                 \n",
       "FDW58                                        1                              0   \n",
       "FDW14                                        1                              0   \n",
       "NCN55                                        0                              0   \n",
       "FDQ58                                        1                              0   \n",
       "FDY38                                        0                              0   \n",
       "\n",
       "                 Outlet_Type_Supermarket Type3  \n",
       "Item_Identifier                                 \n",
       "FDW58                                        0  \n",
       "FDW14                                        0  \n",
       "NCN55                                        0  \n",
       "FDQ58                                        0  \n",
       "FDY38                                        1  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_one.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "06ce1527",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#on test data\n",
    "y_pred=model.predict(test_data_one)\n",
    "test_data[\"Item_Outlet_Sales\"]=y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "97c5704d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.to_csv('LR_testResults.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28cbb99",
   "metadata": {},
   "source": [
    "# Regularized Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2c152ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso regression MSE:  46.119235309472344\n",
      "r2: 0.6799646923441631\n"
     ]
    }
   ],
   "source": [
    "#lasso regression\n",
    "lasso = Lasso(alpha=0.1)\n",
    "lasso.fit(train_data, train_labels)\n",
    "y_pred_lasso = lasso.predict(val_data)\n",
    "mse_lasso = mean_squared_error(val_labels, y_pred_lasso)\n",
    "print(\"Lasso regression MSE: \", mse_lasso)\n",
    "r2_lasso = r2_score(val_labels,y_pred_lasso)\n",
    "print(\"r2:\",r2_lasso)\n",
    "y_pred_lasso=lasso.predict(test_data_one)\n",
    "test_data[\"Item_Outlet_Sales\"]=y_pred_lasso\n",
    "test_data.to_csv('lasso_testResults.csv',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "24caf269",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge regression MSE:  45.88330431942168\n",
      "r2: 0.6816018887651303\n"
     ]
    }
   ],
   "source": [
    "#ridge regression\n",
    "ridge = Ridge(alpha=0.1)\n",
    "ridge.fit(train_data, train_labels)\n",
    "y_pred_ridge = ridge.predict(val_data)\n",
    "mse_ridge = mean_squared_error(val_labels, y_pred_ridge)\n",
    "print(\"Ridge regression MSE: \", mse_ridge)\n",
    "r2_ridge = r2_score(val_labels,y_pred_ridge)\n",
    "print(\"r2:\",r2_ridge)\n",
    "y_pred_ridge=ridge.predict(test_data_one)\n",
    "test_data[\"Item_Outlet_Sales\"]=y_pred_ridge\n",
    "test_data.to_csv('ridge_testResults.csv',index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b89c47",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e8cec06e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest regression MSE:  49.015497641320835\n",
      "r2: 0.6598666529858487\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "rf = RandomForestRegressor(n_estimators=150, random_state=42)\n",
    "rf.fit(train_data,train_labels)\n",
    "y_pred_rf = rf.predict(val_data)\n",
    "mse_rf = mean_squared_error(val_labels, y_pred_rf)\n",
    "print(\"Random Forest regression MSE: \", mse_rf)\n",
    "r2_rf = r2_score(val_labels,y_pred_rf)\n",
    "print(\"r2:\",r2_rf)\n",
    "y_pred_rf=rf.predict(test_data_one)\n",
    "test_data[\"Item_Outlet_Sales\"]=y_pred_rf\n",
    "test_data.to_csv('rf_testResults.csv',index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b12145f",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "89f8b205",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost regression MSE:  51.43506463812477\n",
      "r2: 0.6430765465797041\n"
     ]
    }
   ],
   "source": [
    "#XGBoost\n",
    "xgb_model = XGBRegressor(n_estimators=100, random_state=42)\n",
    "xgb_model.fit(train_data, train_labels)\n",
    "y_pred_xgb = xgb_model.predict(val_data)\n",
    "mse_xgb = mean_squared_error(val_labels, y_pred_xgb)\n",
    "print(\"XGBoost regression MSE: \", mse_xgb)\n",
    "r2_xgb = r2_score(val_labels,y_pred_xgb)\n",
    "print(\"r2:\",r2_xgb)\n",
    "y_pred_xgb=xgb_model.predict(test_data_one)\n",
    "test_data[\"Item_Outlet_Sales\"]=y_pred_xgb\n",
    "test_data.to_csv('xgboost_testResults.csv',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a52cc9d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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