BigMart Sales.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from scipy.stats import mode\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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_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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('data/Train.csv')\n",
    "test_df = pd.read_csv('data/Test.csv')\n",
    "\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4705.000000</td>\n",
       "      <td>5681.000000</td>\n",
       "      <td>5681.000000</td>\n",
       "      <td>5681.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.695633</td>\n",
       "      <td>0.065684</td>\n",
       "      <td>141.023273</td>\n",
       "      <td>1997.828903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.664849</td>\n",
       "      <td>0.051252</td>\n",
       "      <td>61.809091</td>\n",
       "      <td>8.372256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.990000</td>\n",
       "      <td>1985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.645000</td>\n",
       "      <td>0.027047</td>\n",
       "      <td>94.412000</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>0.054154</td>\n",
       "      <td>141.415400</td>\n",
       "      <td>1999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.700000</td>\n",
       "      <td>0.093463</td>\n",
       "      <td>186.026600</td>\n",
       "      <td>2004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.323637</td>\n",
       "      <td>266.588400</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year\n",
       "count  4705.000000      5681.000000  5681.000000                5681.000000\n",
       "mean     12.695633         0.065684   141.023273                1997.828903\n",
       "std       4.664849         0.051252    61.809091                   8.372256\n",
       "min       4.555000         0.000000    31.990000                1985.000000\n",
       "25%       8.645000         0.027047    94.412000                1987.000000\n",
       "50%      12.500000         0.054154   141.415400                1999.000000\n",
       "75%      16.700000         0.093463   186.026600                2004.000000\n",
       "max      21.350000         0.323637   266.588400                2009.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
       "        'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
       "        'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
       "        'Outlet_Type', 'Item_Outlet_Sales'],\n",
       "       dtype='object'),\n",
       " Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
       "        'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
       "        'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
       "        'Outlet_Type'],\n",
       "       dtype='object'))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns, test_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8523, 13), (5681, 13), (14204, 13))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['source'] = 'train'\n",
    "test_df['source'] = 'test'\n",
    "test_df['Item_Outlet_Sales'] = 0\n",
    "df = pd.concat([train_df, test_df], sort = False)\n",
    "train_df.shape, test_df.shape, df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11765.000000</td>\n",
       "      <td>14204.000000</td>\n",
       "      <td>14204.000000</td>\n",
       "      <td>14204.000000</td>\n",
       "      <td>14204.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.792854</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>141.004977</td>\n",
       "      <td>1997.830681</td>\n",
       "      <td>1308.865489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.652502</td>\n",
       "      <td>0.051459</td>\n",
       "      <td>62.086938</td>\n",
       "      <td>8.371664</td>\n",
       "      <td>1699.791423</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.710000</td>\n",
       "      <td>0.027036</td>\n",
       "      <td>94.012000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.054021</td>\n",
       "      <td>142.247000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>559.272000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.750000</td>\n",
       "      <td>0.094037</td>\n",
       "      <td>185.855600</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2163.184200</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  11765.000000     14204.000000  14204.000000               14204.000000   \n",
       "mean      12.792854         0.065953    141.004977                1997.830681   \n",
       "std        4.652502         0.051459     62.086938                   8.371664   \n",
       "min        4.555000         0.000000     31.290000                1985.000000   \n",
       "25%        8.710000         0.027036     94.012000                1987.000000   \n",
       "50%       12.600000         0.054021    142.247000                1999.000000   \n",
       "75%       16.750000         0.094037    185.855600                2004.000000   \n",
       "max       21.350000         0.328391    266.888400                2009.000000   \n",
       "\n",
       "       Item_Outlet_Sales  \n",
       "count       14204.000000  \n",
       "mean         1308.865489  \n",
       "std          1699.791423  \n",
       "min             0.000000  \n",
       "25%             0.000000  \n",
       "50%           559.272000  \n",
       "75%          2163.184200  \n",
       "max         13086.964800  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                  2439\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  4016\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "source                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Weight'] = df.groupby('Item_Identifier')['Item_Weight'].transform(lambda x:x.fillna(x.mean()))\n",
    "df['Item_Weight'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\AbhrajyotiPal\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:248: RuntimeWarning: The input array could not be properly checked for nan values. nan values will be ignored.\n",
      "  \"values. nan values will be ignored.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlet_size_mode = df.pivot_table(values='Outlet_Size', \n",
    "                                    columns='Outlet_Type',\n",
    "                                    aggfunc=(lambda x:mode(x).mode[0]) )\n",
    "miss_bool = df['Outlet_Size'].isnull()\n",
    "df.loc[miss_bool,'Outlet_Size'] = df.loc[miss_bool,'Outlet_Type'].apply(lambda x: outlet_size_mode[x])\n",
    "\n",
    "df['Outlet_Size'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Medium', 'Small', 'High'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Outlet_Size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               1559\n",
       "Item_Weight                    628\n",
       "Item_Fat_Content                 5\n",
       "Item_Visibility              13006\n",
       "Item_Type                       16\n",
       "Item_MRP                      8052\n",
       "Outlet_Identifier               10\n",
       "Outlet_Establishment_Year        9\n",
       "Outlet_Size                      3\n",
       "Outlet_Location_Type             3\n",
       "Outlet_Type                      4\n",
       "Item_Outlet_Sales             3494\n",
       "source                           2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Item_Fat_Content\n",
      "Low Fat    8485\n",
      "Regular    4824\n",
      "LF          522\n",
      "reg         195\n",
      "low fat     178\n",
      "Name: Item_Fat_Content, dtype: int64\n",
      "\n",
      " Item_Type\n",
      "Fruits and Vegetables    2013\n",
      "Snack Foods              1989\n",
      "Household                1548\n",
      "Frozen Foods             1426\n",
      "Dairy                    1136\n",
      "Baking Goods             1086\n",
      "Canned                   1084\n",
      "Health and Hygiene        858\n",
      "Meat                      736\n",
      "Soft Drinks               726\n",
      "Breads                    416\n",
      "Hard Drinks               362\n",
      "Others                    280\n",
      "Starchy Foods             269\n",
      "Breakfast                 186\n",
      "Seafood                    89\n",
      "Name: Item_Type, dtype: int64\n",
      "\n",
      " Outlet_Identifier\n",
      "OUT027    1559\n",
      "OUT013    1553\n",
      "OUT035    1550\n",
      "OUT049    1550\n",
      "OUT046    1550\n",
      "OUT045    1548\n",
      "OUT018    1546\n",
      "OUT017    1543\n",
      "OUT010     925\n",
      "OUT019     880\n",
      "Name: Outlet_Identifier, dtype: int64\n",
      "\n",
      " Outlet_Establishment_Year\n",
      "1985    2439\n",
      "1987    1553\n",
      "1999    1550\n",
      "1997    1550\n",
      "2004    1550\n",
      "2002    1548\n",
      "2009    1546\n",
      "2007    1543\n",
      "1998     925\n",
      "Name: Outlet_Establishment_Year, dtype: int64\n",
      "\n",
      " Outlet_Size\n",
      "Small     7996\n",
      "Medium    4655\n",
      "High      1553\n",
      "Name: Outlet_Size, dtype: int64\n",
      "\n",
      " Outlet_Location_Type\n",
      "Tier 3    5583\n",
      "Tier 2    4641\n",
      "Tier 1    3980\n",
      "Name: Outlet_Location_Type, dtype: int64\n",
      "\n",
      " Outlet_Type\n",
      "Supermarket Type1    9294\n",
      "Grocery Store        1805\n",
      "Supermarket Type3    1559\n",
      "Supermarket Type2    1546\n",
      "Name: Outlet_Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "cols = ['Item_Fat_Content', \n",
    "        'Item_Type', \n",
    "        'Outlet_Identifier', \n",
    "        'Outlet_Establishment_Year',\n",
    "        'Outlet_Size',\n",
    "        'Outlet_Location_Type',\n",
    "        'Outlet_Type']\n",
    "\n",
    "for col in cols:\n",
    "    print('\\n %s' %col)\n",
    "    print(df[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular', 'low fat', 'LF', 'reg'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Fat_Content'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert(x):\n",
    "    if x in ['Low Fat', 'low fat', 'LF']: \n",
    "        return 'Low Fat'\n",
    "    elif x in ['Regular', 'reg']:\n",
    "        return 'Regular'\n",
    "    else:\n",
    "        return x\n",
    "    \n",
    "df['Item_Fat_Content'] = df['Item_Fat_Content'].apply(convert)\n",
    "df['Item_Fat_Content'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "879"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['Item_Visibility'] == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visibility_avg = df.pivot_table(values='Item_Visibility',\n",
    "                                index='Item_Identifier')\n",
    "miss_bool = (df['Item_Visibility'] == 0)\n",
    "df.loc[miss_bool,'Item_Visibility'] = df.loc[miss_bool,'Item_Identifier'].apply(lambda x: visibility_avg.loc[x])\n",
    "\n",
    "(df['Item_Visibility'] == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14204.000000\n",
       "mean        21.169319\n",
       "std          8.371664\n",
       "min         10.000000\n",
       "25%         15.000000\n",
       "50%         20.000000\n",
       "75%         32.000000\n",
       "max         34.000000\n",
       "Name: Years, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Years'] = 2019 - df['Outlet_Establishment_Year']\n",
    "df['Years'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fruits and Vegetables    2013\n",
       "Snack Foods              1989\n",
       "Household                1548\n",
       "Frozen Foods             1426\n",
       "Dairy                    1136\n",
       "Baking Goods             1086\n",
       "Canned                   1084\n",
       "Health and Hygiene        858\n",
       "Meat                      736\n",
       "Soft Drinks               726\n",
       "Breads                    416\n",
       "Hard Drinks               362\n",
       "Others                    280\n",
       "Starchy Foods             269\n",
       "Breakfast                 186\n",
       "Seafood                    89\n",
       "Name: Item_Type, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food              10201\n",
       "Non-Consumable     2686\n",
       "Drinks             1317\n",
       "Name: Item_Type_Combined, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Type_Combined'] = df['Item_Identifier'].apply(lambda x: x[0:2])\n",
    "df['Item_Type_Combined'] = df['Item_Type_Combined'].map({'FD':'Food',\n",
    "                                                         'NC':'Non-Consumable',\n",
    "                                                         'DR':'Drinks'})\n",
    "df['Item_Type_Combined'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat           6499\n",
       "Regular           5019\n",
       "Non-Consumable    2686\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Item_Type_Combined'] == \"Non-Consumable\",\n",
    "       'Item_Fat_Content'] = \"Non-Consumable\"\n",
    "df['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a36675e160>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "sns.countplot(df['Item_Fat_Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15]),\n",
       " <a list of 16 Text xticklabel objects>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "sns.countplot(df['Item_Type'], \n",
    "              hue = df['Item_Fat_Content'])\n",
    "plt.xticks(rotation = 30, ha = 'right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3669442b0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UtCm2vyEioui/KDOfysy7gD6awVOSJM2hRqPB0NAQ0Dz92mg0Kq5IY80k1N0aEf8JWBIR6yLifOCfZvOimfmvwGeBn9MMc48ANwAPF9OmAPQDw/NfrALuLo7dXey/YmT/OMeMEhGnR8TWiNi6a9eu2ZQtSRMaGBhg48aNXjwuqTIzCXUbaZ7mfAr4JvAL4I9n86IR8Vyao2xHAC8EngV0jbPr8EhgTLBtov5ndmZemJnrM3P9ypUrZ160JE2iu7ubbdu2efG4amvkjRKZ6We9Bc3k7tcnMvPDmfmKIhx9ODOfnOXrHg/clZm7MnMQ+EfgPwAHFadjAVYD9xSP+4FDAYrtvwY8OLJ/nGPUAppnySduS3XgVA9aDLZs2TIq1F155ZUVV6Sxpgx1EXFpRGye6GeWr/tz4FURsay4Nu4NwO3Ad4F3FPs0gEuKx5uLNsX272Tzk7UZOKW4O/YIYB3w41nWpBIM/wGYqC3VgVM9aDHo6OiYtK3qLZ16Fz471y+amddFxMU057zbDdwEXAj8H+CiiDiv6PtycciXga9HRB/NEbpTiue5LSK+TTMQ7gbem5l75rpezd4hhxzCvffeu7f9whe+sMJqpHKMN9XD+973voqrkubWPffcM2lb1Zsy1GVmKWuBZOa5wLljuu9knLtXi9O8J0/wPJ8EPjnnBWpO7NkzOmPv3r17gj2lhauzs5PLL7+cwcFB2tvbnepBUiVmMvnwuoi4OCJuj4g7h3/KLE4L3/333z9pW6qDRqOx93rRtrY2p3pQLR1//PGj2p2dnRVVoonM5O7XvwW+SPM05+uBrwFfL6MoSVpIOjo66OrqIiLo6upixYoVVZckzbmTTx59wuyd73xnRZVoIjMJdQdm5tVAZObPMvOjNCcLlqRFr9FocNRRRzlKp9q69NJLR7U3b571SqEqyUxC3ZMR0QbsiIgzIuJtwPNLqks1sWTJkknbkqSFYexar05p0npmEur+mOZyXmcCLwd+j19NMyKNa+yNEmPbUl04+bDq7uCDD560rerNZPLh6zPzsczsB94HvD0zf1ReaZK0MDj5sBaD++67b9K2qjedyYc/EhEvLh7vHxHfBX4K3BcRx09+tCTVX3d3996Fzvfs2eNonWppw4YNe+/yjghOOOGEiivSWNMZqfsd4CfF4+HTrSuB/wj8aRlFSdJC0tvbu3cOxt27dz/j2iOpDhqNxt7ropcsWeJNQS1oOqHu6fzV2k4nABdl5p7MvIPprUghSbX2W7/1W6Par33tayuqRCpPR0cH+++/PwD777+/U/e0oOmEsqci4mXAfTTnp3v/iG3LSqlKs7Jp0yb6+vqqLmNKZ555ZtUlALB27dqWqUWSWt327dt5/PHHAXj88cfp6+tj7dq1FVelkaYzUncWcDHwL8DnM/MugIh4I831WaUJtbe3T9qW6uAHP/jBqPb3v//9iiqRyvOxj31sVPvcc8eu9KmqTWft1+uAF4/Tfzlw+XA7IhqZ6dXBFWrFUaft27fznve8Z2/7ggsu8Judaqezs5PNmzeTmUSEa7+qlu6+++5J26reTOapm8pZc/hcqokjjzxy7+jcwQcfbKBTLb35zW9m+NLjzOQtb3lLxRVJWozmMtTFHD6XauSII46gra2NT33qU1WXIpXi0ksvHTXVg8snqY5cIaj1zWWoy6l30WK0bNkyjjrqKEfpVFu9vb2jRuqc0kR1dPTRR49qH3PMMRVVook4UidJ+6izs5OlS5uXKC9dutRr6lRLd9xxx6j2bbfdVlElmsi0Q11EHDFF3w/npCJJWmAajcbedY2HhoaclFW19MQTT0zaVvVmMlL3D+P0XTz8IDPP2PdyJGlhGj79OrxcmFQ3y5Ytm7St6k1n7dcXR8TbgV+LiP9rxM+pwAGlVyhJLe4zn/nMqPZnP/vZiiqRyrN8+fJJ26redFaUeBHwJuAg4M0j+h8F/ksZRUnSQnLttdeOav/wh16Novq5//77J22retOZfPgS4JKIeHVmXjvV/pIkqX5Wr15Nf3//qLZay0yuqXsgIq6OiFsBIuKoiDinpLokacHwWiMtBgcffPCo9gte8IKKKtFEZhLq/gb4IDAIkJnbgFPKKEqSFpL3v//9o9of+MAHKqpEKs8NN9wwqr1169aKKtFEZhLqlmXmj8f07Z7LYiRpIbruuutGtcdeYydJ82EmoW4gIn6dYuWIiHgHcG8pVUnSAnLVVVeNavf29lZUiVSeAw88cNK2qjedu1+HvRe4EHhxRPwrcBfwn0upSpIWkOGJhydqS3Xwy1/+ctK2qjftUJeZdwLHR8SzgLbMfLS8siRJkjQTU4a6iHjfBP0AZOafz3FNkiSpxSxZsmTUKPSSJUsqrEbjmc5I3bMn2ZZzVYgkLVTLli0btQ6mU5qojrzMoPVNZ/LhjwFExGsyc9Q06RHxmrIKk6SFwoXOJbWCmdz9ev40+yRpUVmzZs2kbakO2traJm2retO5pu7VwH8AVo65vu45gCfUJS1655xzDu95z3v2tj/ykY9UWI1UjqGhoUnbqt50YvZ+wHKaAfDZI35+AbyjvNIkaWE48sgj947OrVmzhrVr11ZbkKRFaTrX1H0P+F5EfDUzfzYPNUnSgnPOOedw1llnOUonqTIzmXz4qxHxjLtdM/O4OaxHkhakI488kp6enqrLkLSIzSTUjVyx+gDg7bj2q6R5tmnTJvr6+qou4xn6+/sBWL16dcWVjLZ27VrOPPPMqsuQNA9msqLEDWO6fhgR35vjeiRpQXLJJM2lVvzysv/++/PUU0+NarfKFwa/vDRNO9RFxPNGNNuAlwMvmO0LR8RBwJeAl9GcxPgPgJ8A3wLWADuBd2bmQ9FcvuILwBuBJ4BTM/PG4nkawDnF056Xmd2zrUlS62vVP9zDdW3atKniSqRyHH744Wzfvn1UW61lJqdfR47U7QbuAk7bh9f+AnBFZr4jIvYDlgEfAq7OzE9HxNnA2cAHgC5gXfFzLPBF4NgiaJ4LrKcZDG+IiM2Z+dA+1CVJUqVa9ctLZ2cnTz31FGvWrOFLX/pS1eVojJmcfj1irl40Ip4DvBY4tXjup4GnI+Ik4HXFbt3ANTRD3UnA1zIzgR9FxEERcUixb29mPlg8by9wIvDNuapVkiQ1HX744fT19XmXd4uaVqiLiOcD7wVeSnNE7HbgLzPz/lm+7r8DdgF/GxH/nuYo4FnAwZl5L0Bm3lu8LsAq4O4Rx/cXfRP1j/ceTgdOBzjssMNmWbYkSYvXsmXLOOqoo5yLsUVNOflwsb7r9UXza8D/LB7/eB/Wfl0KHAN8MTN/E3ic5qnWCcsYpy8n6X9mZ+aFmbk+M9evXLlypvVKkiS1tOmM1H0OeGtm3jSi75KI+F/ABTSvcZupfqA/M68r2hfTDHX3RcQhxSjdIcD9I/Y/dMTxq4F7iv7Xjem/Zhb1SJIkLWjTWSbsOWMCHQCZeTPN5cJmLDP/Dbg7Il5UdL2B5indzUCj6GsAlxSPNwPvjqZXAY8Up2mvBDZExHMj4rnAhqJPkiRpUZnOSF1ExHPH3lFa3Hk6nVA4kY3AN4o7X+8Efr94vm9HxGnAz4GTi30vpzmdSR/NKU1+HyAzH4yIT/Cr08MfH75pQpIkaTGZTqj7PLAlIt4P3Fj0vRz4s2LbrBQjfevH2fSGcfZNmjdqjPc8XwG+Mts6JEmS6mDKUJeZF0bEPcAnGH3363mZeWnJ9UmSJGkapjWlSWZeBlw22T4R8cHM/NScVCVJkqQZ2Zdr4sY6eepdJEmSVIa5DHXjzRknSZKkeTCXoW7cSX8lSZJUPkfqJEmSamAuQ93fz+FzSZIkaQamdfcrQEQcQXPC4DUjj8vMtxS//3Sui5MkSdL0TDvUAf8b+DJwKTBUTjmSJEmajZmEuiczc1NplUiSJGnWZhLqvhAR5wJbgKeGOzPzxokPkSRJ0nyYSaj7DeD3gOP41enXLNqSJEmq0ExC3duAf5eZT5dVjCRJkmZnJlOa/DNwUFmFSJIkafZmMlJ3MPAvEXE9o6+pe8ucVyVJkqQZmUmoO7e0KiRJkrRPph3qMvN7EXE4sC4zr4qIZcCS8kqTJEnSdE37mrqI+C/AxcAFRdcqmhMSS5IkqWIzuVHivcBrgF8AZOYO4PllFCVJkqSZmUmoe2rkdCYRsZTmPHWSJEmq2ExC3fci4kPAgRHRCfw9zXVgJUmSVLGZhLqzgV3ALcD/DVyemR8upSpJkiTNyEymNNmYmV8A/ma4IyLOKvokSZJUoZmM1DXG6Tt1juqQJEnSPphypC4i3gX8J+CIiNg8YtOzgQfKKkySJEnTN53Tr/8E3At0AJ8b0f8osK2MoiRJkjQzU4a6zPwZ8DPg1eWXI0mSpNmYzunXRxl/ProAMjOfM+dVSZIkaUamM1L37PkoRJIkSbM3k7tfJUmS1KIMdZIkSTVgqJMkSaoBQ50kSVINGOokSZJqwFAnSZJUA4Y6SZKkGqg01EXEkoi4KSIuK9pHRMR1EbEjIr4VEfsV/fsX7b5i+5oRz/HBov8nEXFCNe9EkiSpWlWP1J0F3DGi/WfA5zNzHfAQcFrRfxrwUGauBT5f7EdEvAQ4BXgpcCLwVxGxZJ5qlyRJahmVhbqIWA38NvCloh3AccDFxS7dwFuLxycVbYrtbyj2Pwm4KDOfysy7gD7glfPzDiRJklpHlSN1/wP478BQ0V4BPJyZu4t2P7CqeLwKuBug2P5Isf/e/nGOGSUiTo+IrRGxddeuXXP5PiRJkipXSaiLiDcB92fmDSO7x9k1p9g22TGjOzMvzMz1mbl+5cqVM6pXkiSp1S2t6HVfA7wlIt4IHAA8h+bI3UERsbQYjVsN3FPs3w8cCvRHxFLg14AHR/QPG3mMJEnSolHJSF1mfjAzV2fmGpo3OnwnM38X+C7wjmK3BnBJ8Xhz0abY/p3MzKL/lOLu2COAdcCP5+ltSJIktYyqRuom8gHgoog4D7gJ+HLR/2Xg6xHRR3OE7hSAzLwtIr4N3A7sBt6bmXvmv2xJkqRqVR7qMvMa4Jri8Z2Mc/dqZj4JnDzB8Z8EPllehZIkSa2v8lC3UG3atIm+vr6qy1gQduzYAcCZZ55ZcSWtb+3atf53kiTNiqFulvr6+rjpltsZWva8qktpefF084bkG376bxVX0tranniw6hIkSQuYoW4fDC17Hk++5E1Vl6GaOOD2y6ouQZK0gFW9TJgkSZLmgKFOkiSpBgx1kiRJNWCokyRJqgFDnSRJUg0Y6iRJkmrAUCdJklQDhjpJkqQaMNRJkiTVgCtKSJqQaxxPn2scT59rHEvlMNRJmlBfXx/bb72Rw5bvqbqUlrffYPPEx5M7r6+4ktb288eWVF2CVFuGOkmTOmz5Hs5Z/1jVZagmztu6vOoSpNrymjpJkqQaMNRJkiTVgKFOkiSpBgx1kiRJNWCokyRJqgHvfpUkLWrOxzh9zsc4fVXMx2iokyQtan19fdx0201wUNWVLABDzV83/etN1dbR6h6u5mUNdZIkHQRDrxuqugrVRNs11Vzd5jV1kiRJNWCokyRJqgFDnSRJUg0Y6iRJkmrAUCdJklQDhjpJkqQaMNRJkiTVgKFOkiSpBgx1kiRJNWCokyRJqgGXCZul/v5+2p54hANuv6zqUlQTbU88QH//7qrLkCQtUI7USZIk1YAjdbO0evVq7ntqKU++5E1Vl6KaOOD2y1i9+gVVlyFJWqAqGamLiEMj4rsRcUdE3BYRZxX9z4uI3ojYUfx+btEfEbEpIvoiYltEHDPiuRrF/jsiolHF+5EkSapaVSN1u4H/JzNvjIhnAzdERC9wKnB1Zn46Is4GzgY+AHQB64qfY4EvAsdGxPOAc4H1QBbPszkzH5r3dyTVUH9/P48/uoTzti6vuhTVxM8eXcKz+vurLkOqpUpG6jLz3sy8sXj8KHAHsAo4CegudusG3lo8Pgn4Wjb9CDgoIg4BTgB6M/PBIsj1AifO41uRJElqCZVfUxcRa4DfBK4DDs7Me6EZ/CLi+cVuq4C7RxzWX/RN1D/e65wOnA5w2GGHzd0bkGps9erVPLn7Xs5Z/1jVpagmztu6nANWr666DKmWKr37NSKWA/8A/HFm/mKyXcfDmEqxAAALB0lEQVTpy0n6n9mZeWFmrs/M9StXrpx5sZIkSS2sslAXEe00A903MvMfi+77itOqFL/vL/r7gUNHHL4auGeSfkmSpEWlqrtfA/gycEdm/vmITZuB4TtYG8AlI/rfXdwF+yrgkeI07ZXAhoh4bnGn7IaiT5IkaVGp6pq61wC/B9wSETcXfR8CPg18OyJOA34OnFxsuxx4I9AHPAH8PkBmPhgRnwCuL/b7eGY+OD9vQZIkqXVUEuoy8/9j/OvhAN4wzv4JvHeC5/oK8JW5q06SJGnhcZkwSZKkGjDUSZIk1YChTpIkqQYMdZIkSTVQ+YoSkiRVqb+/Hx6Btmsc59AceRj6c/7XOPYTLEmSVAOO1EmSFrXVq1ezK3Yx9LqhqktRTbRd08bqVfO/xrEjdZIkSTVgqJMkSaoBQ50kSVINeE3dPmh74kEOuP2yqstoefHkLwDIA55TcSWtre2JB4EXVF2GJGmBMtTN0tq1a6suYcHYseNRANb9uoFlci/wcyVJmjVD3SydeeaZVZewYAz/t9q0aVPFlUiSVF9eUydJklQDjtRJmtTPH1vCeVuXV11Gy7vvieZ35IOXOdfZZH7+2BKOrLoIqaYMdZIm5DV+0/f0jh0AHLBmXcWVtLYj8XMllcVQJ2lCXjs6fV47KqlqXlMnSZJUA4Y6SZKkGjDUSZIk1YChTpIkqQYMdZIkSTVgqJMkSaoBQ50kSVINGOokSZJqwMmHJUl6GNqucZxjSo8Vv105cHIPA6vm/2UNdZKkRc1ly6ZvR7Ec3rpVLoc3qVXVfK4MdZKkRc3l8KbP5fBam2PNkiRJNWCokyRJqgFDnSRJUg0Y6iRJkmrAUCdJklQDhjpJkqQaMNRJkiTVgKFOkiSpBgx1kiRJNVCLFSUi4kTgC8AS4EuZ+emKS6rEpk2b6Ovrq7qMZxheVqbVZm1fu3Zty9Wkqfk5nxk/59LiseBDXUQsAf4S6AT6gesjYnNm3l5tZRp24IEHVl2CVDo/55pLfnmZGb+8NC34UAe8EujLzDsBIuIi4CRg0YU6P9BaDPycS9Xxy0trq0OoWwXcPaLdDxw7dqeIOB04HeCwww6bn8okSZoFv7xoNupwo0SM05fP6Mi8MDPXZ+b6lStXzkNZkiRJ86cOoa4fOHREezVwT0W1SJIkVaIOoe56YF1EHBER+wGnAJsrrkmSJGleLfhr6jJzd0ScAVxJc0qTr2TmbRWXJUmSNK8WfKgDyMzLgcurrkOSJKkqdTj9KkmStOgZ6iRJkmrAUCdJklQDhjpJkqQaMNRJkiTVgKFOkiSpBgx1kiRJNWCokyRJqoHIzKprmHcRsQv4WdV1LDIdwEDVRUgl83OuxcDP+fw7PDNXTrXTogx1mn8RsTUz11ddh1QmP+daDPycty5Pv0qSJNWAoU6SJKkGDHWaLxdWXYA0D/ycazHwc96ivKZOkiSpBhypkyRJqgFDnSRJUg0Y6jShiMiI+PqI9tKI2BURl83wea6JiPXF48sj4qC5rlUqS0Q8NqZ9akT8RfH4DyPi3VMcv3d/qdVExIcj4raI2BYRN0fEsXPwnI8Vv9dExK37XqWma2nVBailPQ68LCIOzMxfAp3Av+7LE2bmG+ekMqkFZOZfV12DNFsR8WrgTcAxmflURHQA+1VclvaBI3WaSg/w28XjdwHfHN4QEc+KiK9ExPURcVNEnFT0HxgRFxXf/L4FHDjimJ0R0TH2G1xEvD8iPlo8viYiPh8R34+IOyLiFRHxjxGxIyLOm4f3LE1LRHw0It5fPH5F8Zm/NiI+M2aE4oURcUXxGf5/KypXGusQYCAznwLIzIHMvKf4O/2nxWd5a0QcExFXRsRPI+IPASJieURcHRE3RsQtw3//VS1DnaZyEXBKRBwAHAVcN2Lbh4HvZOYrgNcDn4mIZwH/FXgiM48CPgm8fBav+3Rmvhb4a+AS4L3Ay4BTI2LFrN+NNHMHFqelbo6Im4GPT7Df3wJ/mJmvBvaM2XY08DvAbwC/ExGHlleuNG1bgEMjYntE/FVE/McR2+4uPss/AL4KvAN4Fb/6/D8JvC0zj6H59/9zERHzV7rG4+lXTSozt0XEGpqjdJeP2bwBeMvwSAVwAHAY8Fpg04jjt83ipTcXv28BbsvMewEi4k7gUOCBWTynNBu/zMyjhxsRcSowaomk4jrRZ2fmPxVdf0fztNawqzPzkWLf24HDgbvLLFqaSmY+FhEvB36LZjD7VkScXWwe+Td4eWY+CjwaEU8Wn/fHgT+NiNcCQ8Aq4GDg3+b1TWgUQ52mYzPwWeB1wMhRsgDenpk/Gblz8WVtqgkQdzN6pPiAMdufKn4PjXg83PZzq1Yz1QjFyM/wHvwMq0Vk5h7gGuCaiLgFaBSbpvob/LvASuDlmTkYETt55t9xzTNPv2o6vgJ8PDNvGdN/JbBxeMg9In6z6P8+zf/hiYiX0TxtO9Z9wPMjYkVE7M/oUQ1pQcnMh2iOYryq6Dqlynqk6YiIF0XEuhFdRwM/m+bhvwbcXwS619McfVbFDHWaUmb2Z+YXxtn0CaAd2FZcFP6Jov+LwPLitOt/B348znMO0rw24zrgMuBfyqhdmkenARdGxLU0R+4eqbgeaSrLge6IuL34e/0S4KPTPPYbwPqI2ErzS7x/w1uAy4RJ0hyIiOWZOTw/19nAIZl5VsVlSVpEvK5DkubGb0fEB2n+Xf0ZcGq15UhabBypkyRJqgGvqZMkSaoBQ50kSVINGOokSZJqwFAnSZJUA4Y6SbUWEasj4pKI2FEsSP6FiNhvimM+NKb92BT7HxQRfzTFPm0RsSkibi0WQL8+Io4otl1eLL0kSbNmqJNUW8VqJ/8I/O/MXAccSXPC1U9OceiHptg+1kHApKEO+B3ghcBRmfkbwNuAhwEy842Z+fAMX1OSRjHUSaqz44AnM/NvYe86l38C/EFE/FFE/MXwjhFxWUS8LiI+DRwYETdHxDfGPmFE/LdilG1bRHys6P408OvFMZ+ZoJZDgHszc6iopb9YXoyI2BkRHRHxh8Vz3BwRd0XEd4vtGyLi2oi4MSL+PiKWz81/Hkl1YqiTVGcvBW4Y2ZGZvwB+zgSTr2fm2cAvM/PozPzdkdsiYgOwDnglzXUyXx4RrwXOBn5aHPPfJqjl28Cbi8D2uRFrJY987b/OzKOBVwD9wJ9HRAdwDnB8Zh4DbAXeN833L2kRcUUJSXUWwHgzrE/UP5UNxc9NRXs5zZD386kOzMz+iHgRzdHD44CrI+LkzLx6nN2/AHwnMy+NiDfRXJPzh82zyewHXDuL2iXVnKFOUp3dBrx9ZEdEPAc4FHiE0WcrDpjG8wXwqcy8YMxzrplOMZn5FNAD9ETEfcBbgVGhLiJOBQ4Hzhjxmr2Z+a7pvIakxcvTr5Lq7GpgWUS8GyAilgCfA74K3AkcXdyVeijNU6rDBiOifZznu5Lm9XjLi+dbFRHPBx4Fnj1ZIRFxTES8sHjcBhxFc43Ykfu8HHg/8J+Hr70DfgS8JiLWFvssi4gjp/sfQNLiYaiTVFvZXNz6bcDJEbED2A48SfPu1h8CdwG3AJ8Fbhxx6IXAtrE3SmTmFuDvgGsj4hbgYuDZmfkAzdOjt05yo8TzgUsj4lZgG7Ab+Isx+5wBPA/4bnHt3ZcycxdwKvDNiNhGM+S9eOb/NSTVXTT/5kmSJGkhc6ROkiSpBrxRQpLmUET8BvD1Md1PZeaxVdQjafHw9KskSVINePpVkiSpBgx1kiRJNWCokyRJqgFDnSRJUg38//G1mtTbyLmlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "sns.boxplot(x = train_df['Outlet_Size'],\n",
    "            y = train_df['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3669e4908>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "sns.boxplot(x = train_df['Outlet_Location_Type'],\n",
    "            y = train_df['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]), <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "sns.boxplot(x = train_df['Outlet_Type'],\n",
    "            y = train_df['Item_Outlet_Sales'])\n",
    "plt.xticks(rotation = 30, ha = 'right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               object\n",
       "Item_Weight                  float64\n",
       "Item_Fat_Content              object\n",
       "Item_Visibility              float64\n",
       "Item_Type                     object\n",
       "Item_MRP                     float64\n",
       "Outlet_Identifier             object\n",
       "Outlet_Establishment_Year      int64\n",
       "Outlet_Size                   object\n",
       "Outlet_Location_Type          object\n",
       "Outlet_Type                   object\n",
       "Item_Outlet_Sales            float64\n",
       "source                        object\n",
       "Years                          int64\n",
       "Item_Type_Combined            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "df['Outlet'] = encoder.fit_transform(df['Outlet_Identifier'])\n",
    "cols = ['Item_Fat_Content',\n",
    "        'Outlet_Location_Type',\n",
    "        'Outlet_Size',\n",
    "        'Item_Type_Combined',\n",
    "        'Outlet_Type',\n",
    "        'Outlet']\n",
    "\n",
    "for col in cols:\n",
    "    df[col] = encoder.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns = ['Item_Fat_Content',\n",
    "                                   'Outlet_Location_Type',\n",
    "                                   'Outlet_Size',\n",
    "                                   'Outlet_Type',\n",
    "                                   'Item_Type_Combined',\n",
    "                                   'Outlet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               object\n",
       "Item_Weight                  float64\n",
       "Item_Visibility              float64\n",
       "Item_Type                     object\n",
       "Item_MRP                     float64\n",
       "Outlet_Identifier             object\n",
       "Outlet_Establishment_Year      int64\n",
       "Item_Outlet_Sales            float64\n",
       "source                        object\n",
       "Years                          int64\n",
       "Item_Fat_Content_0             uint8\n",
       "Item_Fat_Content_1             uint8\n",
       "Item_Fat_Content_2             uint8\n",
       "Outlet_Location_Type_0         uint8\n",
       "Outlet_Location_Type_1         uint8\n",
       "Outlet_Location_Type_2         uint8\n",
       "Outlet_Size_0                  uint8\n",
       "Outlet_Size_1                  uint8\n",
       "Outlet_Size_2                  uint8\n",
       "Outlet_Type_0                  uint8\n",
       "Outlet_Type_1                  uint8\n",
       "Outlet_Type_2                  uint8\n",
       "Outlet_Type_3                  uint8\n",
       "Item_Type_Combined_0           uint8\n",
       "Item_Type_Combined_1           uint8\n",
       "Item_Type_Combined_2           uint8\n",
       "Outlet_0                       uint8\n",
       "Outlet_1                       uint8\n",
       "Outlet_2                       uint8\n",
       "Outlet_3                       uint8\n",
       "Outlet_4                       uint8\n",
       "Outlet_5                       uint8\n",
       "Outlet_6                       uint8\n",
       "Outlet_7                       uint8\n",
       "Outlet_8                       uint8\n",
       "Outlet_9                       uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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_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>Item_Outlet_Sales</th>\n",
       "      <th>source</th>\n",
       "      <th>Years</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_0</th>\n",
       "      <th>Outlet_1</th>\n",
       "      <th>Outlet_2</th>\n",
       "      <th>Outlet_3</th>\n",
       "      <th>Outlet_4</th>\n",
       "      <th>Outlet_5</th>\n",
       "      <th>Outlet_6</th>\n",
       "      <th>Outlet_7</th>\n",
       "      <th>Outlet_8</th>\n",
       "      <th>Outlet_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</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>3735.1380</td>\n",
       "      <td>train</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</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>443.4228</td>\n",
       "      <td>train</td>\n",
       "      <td>10</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</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>2097.2700</td>\n",
       "      <td>train</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>0.017834</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>train</td>\n",
       "      <td>21</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0.009780</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>train</td>\n",
       "      <td>32</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight  Item_Visibility              Item_Type  \\\n",
       "0           FDA15         9.30         0.016047                  Dairy   \n",
       "1           DRC01         5.92         0.019278            Soft Drinks   \n",
       "2           FDN15        17.50         0.016760                   Meat   \n",
       "3           FDX07        19.20         0.017834  Fruits and Vegetables   \n",
       "4           NCD19         8.93         0.009780              Household   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year  Item_Outlet_Sales  \\\n",
       "0  249.8092            OUT049                       1999          3735.1380   \n",
       "1   48.2692            OUT018                       2009           443.4228   \n",
       "2  141.6180            OUT049                       1999          2097.2700   \n",
       "3  182.0950            OUT010                       1998           732.3800   \n",
       "4   53.8614            OUT013                       1987           994.7052   \n",
       "\n",
       "  source  Years  ...  Outlet_0  Outlet_1  Outlet_2  Outlet_3  Outlet_4  \\\n",
       "0  train     20  ...         0         0         0         0         0   \n",
       "1  train     10  ...         0         0         0         1         0   \n",
       "2  train     20  ...         0         0         0         0         0   \n",
       "3  train     21  ...         1         0         0         0         0   \n",
       "4  train     32  ...         0         1         0         0         0   \n",
       "\n",
       "   Outlet_5  Outlet_6  Outlet_7  Outlet_8  Outlet_9  \n",
       "0         0         0         0         0         1  \n",
       "1         0         0         0         0         0  \n",
       "2         0         0         0         0         1  \n",
       "3         0         0         0         0         0  \n",
       "4         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\AbhrajyotiPal\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "df.drop(['Item_Type', 'Outlet_Establishment_Year'],\n",
    "       axis = 1,\n",
    "       inplace = True)\n",
    "del train_df\n",
    "del test_df\n",
    "train_df = df.loc[df['source'] == \"train\"]\n",
    "test_df = df.loc[df['source'] == \"test\"]\n",
    "test_df.drop(['Item_Outlet_Sales', 'source'],\n",
    "            axis = 1,\n",
    "            inplace = True)\n",
    "train_df.drop(['source'],\n",
    "             axis = 1,\n",
    "             inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_df['Item_Outlet_Sales']\n",
    "X = train_df.drop(['Item_Outlet_Sales', 'Item_Identifier', 'Outlet_Identifier'],\n",
    "                 axis = 1)\n",
    "\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 2, \n",
    "                                                  test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitmodel(model = None):\n",
    "    model.fit(train_X, train_y)\n",
    "    val_pred = model.predict(val_X)\n",
    "    print(mean_absolute_error(val_y, val_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "873.8877067448681\n"
     ]
    }
   ],
   "source": [
    "model1 = LinearRegression(normalize = True)\n",
    "fitmodel(model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "872.8573678134512\n"
     ]
    }
   ],
   "source": [
    "model2 = Ridge(alpha = 0.001, normalize = True)\n",
    "fitmodel(model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "860.0940399310218\n"
     ]
    }
   ],
   "source": [
    "model3 = DecisionTreeRegressor(max_depth = 30,\n",
    "                              min_samples_leaf = 10)\n",
    "fitmodel(model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "797.0854660773259\n"
     ]
    }
   ],
   "source": [
    "model4 = RandomForestRegressor(n_estimators = 200, \n",
    "                               max_depth = 5,\n",
    "                               min_samples_leaf = 100,\n",
    "                               n_jobs = 4)\n",
    "fitmodel(model4)"
   ]
  }
 ],
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