Prediction Analysis using Regression of Commercial Sales.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_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": [
    "df = pd.read_csv('Dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              1559\n",
       "Item_Weight                   416\n",
       "Item_Fat_Content                5\n",
       "Item_Visibility              7880\n",
       "Item_Type                      16\n",
       "Item_MRP                     5938\n",
       "Outlet_Identifier              10\n",
       "Outlet_Establishment_Year       9\n",
       "Outlet_Size                     4\n",
       "Outlet_Location_Type            3\n",
       "Outlet_Type                     4\n",
       "Item_Outlet_Sales            3493\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: len(x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                  1463\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Identifier',\n",
       " 'Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col = []\n",
    "for x in df.dtypes.index:\n",
    "    if df.dtypes[x] == 'object':\n",
    "        cat_col.append(x)\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col.remove('Item_Identifier')\n",
    "cat_col.remove('Outlet_Identifier')\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Fat_Content\n",
      "Low Fat    5089\n",
      "Regular    2889\n",
      "LF          316\n",
      "reg         117\n",
      "low fat     112\n",
      "Name: Item_Fat_Content, dtype: int64\n",
      "\n",
      "Item_Type\n",
      "Fruits and Vegetables    1232\n",
      "Snack Foods              1200\n",
      "Household                 910\n",
      "Frozen Foods              856\n",
      "Dairy                     682\n",
      "Canned                    649\n",
      "Baking Goods              648\n",
      "Health and Hygiene        520\n",
      "Soft Drinks               445\n",
      "Meat                      425\n",
      "Breads                    251\n",
      "Hard Drinks               214\n",
      "Others                    169\n",
      "Starchy Foods             148\n",
      "Breakfast                 110\n",
      "Seafood                    64\n",
      "Name: Item_Type, dtype: int64\n",
      "\n",
      "Outlet_Size\n",
      "Medium    2793\n",
      "Small     2388\n",
      "High       932\n",
      "Name: Outlet_Size, dtype: int64\n",
      "\n",
      "Outlet_Location_Type\n",
      "Tier 3    3350\n",
      "Tier 2    2785\n",
      "Tier 1    2388\n",
      "Name: Outlet_Location_Type, dtype: int64\n",
      "\n",
      "Outlet_Type\n",
      "Supermarket Type1    5577\n",
      "Grocery Store        1083\n",
      "Supermarket Type3     935\n",
      "Supermarket Type2     928\n",
      "Name: Outlet_Type, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in cat_col:\n",
    "    print(col)\n",
    "    print(df[col].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DRA12</th>\n",
       "      <td>11.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRA24</th>\n",
       "      <td>19.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRA59</th>\n",
       "      <td>8.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB01</th>\n",
       "      <td>7.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB13</th>\n",
       "      <td>6.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ30</th>\n",
       "      <td>6.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ41</th>\n",
       "      <td>19.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ42</th>\n",
       "      <td>10.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ53</th>\n",
       "      <td>9.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ54</th>\n",
       "      <td>14.650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight\n",
       "Item_Identifier             \n",
       "DRA12                 11.600\n",
       "DRA24                 19.350\n",
       "DRA59                  8.270\n",
       "DRB01                  7.390\n",
       "DRB13                  6.115\n",
       "...                      ...\n",
       "NCZ30                  6.590\n",
       "NCZ41                 19.850\n",
       "NCZ42                 10.500\n",
       "NCZ53                  9.600\n",
       "NCZ54                 14.650\n",
       "\n",
       "[1555 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_weight_mean = df.pivot_table(values = \"Item_Weight\", index = 'Item_Identifier')\n",
    "item_weight_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "8518    False\n",
       "8519    False\n",
       "8520    False\n",
       "8521    False\n",
       "8522    False\n",
       "Name: Item_Weight, Length: 8523, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss_bool = df['Item_Weight'].isnull()\n",
    "miss_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, item in enumerate(df['Item_Identifier']):\n",
    "    if miss_bool[i]:\n",
    "        if item in item_weight_mean:\n",
    "            df['Item_Weight'][i] = item_weight_mean.loc[item]['Item_Weight']\n",
    "        else:\n",
    "            df['Item_Weight'][i] = np.mean(df['Item_Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Weight'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Outlet_Type</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <th>Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>Small</td>\n",
       "      <td>Small</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2  \\\n",
       "Outlet_Size         Small             Small            Medium   \n",
       "\n",
       "Outlet_Type Supermarket Type3  \n",
       "Outlet_Size            Medium  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlet_size_mode = df.pivot_table(values='Outlet_Size', columns='Outlet_Type', aggfunc=(lambda x: x.mode()[0]))\n",
    "outlet_size_mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Outlet_Size'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "526"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Item_Visibility']==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:, 'Item_Visibility'].replace([0], [df['Item_Visibility'].mean()], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Item_Visibility']==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Fat_Content'] = df['Item_Fat_Content'].replace({'LF':'Low Fat', 'reg':'Regular', 'low fat':'Low Fat'})\n",
    "df['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       FD\n",
       "1       DR\n",
       "2       FD\n",
       "3       FD\n",
       "4       NC\n",
       "        ..\n",
       "8518    FD\n",
       "8519    FD\n",
       "8520    NC\n",
       "8521    FD\n",
       "8522    DR\n",
       "Name: New_Item_Type, Length: 8523, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['New_Item_Type'] = df['Item_Identifier'].apply(lambda x: x[:2])\n",
    "df['New_Item_Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food              6125\n",
       "Non-Consumable    1599\n",
       "Drinks             799\n",
       "Name: New_Item_Type, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['New_Item_Type'] = df['New_Item_Type'].map({'FD':'Food', 'NC':'Non-Consumable', 'DR':'Drinks'})\n",
    "df['New_Item_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat       3918\n",
       "Regular       3006\n",
       "Non-Edible    1599\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['New_Item_Type']=='Non-Consumable', 'Item_Fat_Content'] = 'Non-Edible'\n",
    "df['Item_Fat_Content'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       14\n",
       "1        4\n",
       "2       14\n",
       "3       15\n",
       "4       26\n",
       "        ..\n",
       "8518    26\n",
       "8519    11\n",
       "8520     9\n",
       "8521     4\n",
       "8522    16\n",
       "Name: Outlet_Years, Length: 8523, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Outlet_Years'] = 2013 - df['Outlet_Establishment_Year']\n",
    "df['Outlet_Years']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Years</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",
       "      <td>Food</td>\n",
       "      <td>14</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",
       "      <td>Drinks</td>\n",
       "      <td>4</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",
       "      <td>Food</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>Food</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.066132</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",
       "      <td>Non-Consumable</td>\n",
       "      <td>26</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         0.066132   \n",
       "4           NCD19         8.93       Non-Edible         0.066132   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998       Small               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales   New_Item_Type  Outlet_Years  \n",
       "0  Supermarket Type1          3735.1380            Food            14  \n",
       "1  Supermarket Type2           443.4228          Drinks             4  \n",
       "2  Supermarket Type1          2097.2700            Food            14  \n",
       "3      Grocery Store           732.3800            Food            15  \n",
       "4  Supermarket Type1           994.7052  Non-Consumable            26  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Visibility', ylabel='Density'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7d/b26471xhly1053/yrw1aFun87qD3YwfnRmtu/3qijJZ0r5KPXsERmh4j0tnW9mXyLaBfNi4DfAH5NXVmbq6onQ1JL5wQ/Rbp064xcZmeIN/i8CDUSbZj4CPAL8n2QVlakamjtwGBnBX13GrgPt7DrQFnQpIpJg8Q7SFjGz3wO/d3dNzHoY9Qejo06ML8kPuJLhO6U6diPX1v1MPClzL1SLyBsdMfhjvW6+CnwcsNiiHuB/3P3rKagvo9Qf7CA7yxhbnNrgH2hY6GvPGN69D8dNHE1eThYvbt3HpSdNHNZniUh6OVpTz6eI9uY5zd3Huns5cAZwdmygNemj/mA7FcX5ZGdl/uBmeTlZnDS5VO38IiPQ0YL/fcA17r65d4G7bwLeG3tP+qhvbqdydOY38/SaN6WMV3YcoLM7EnQpIpJARwv+XHdv7L8w1s6fvKmlMlBHVw/7D3WNiAu7veZVj6GjO8LqXQeDLkVEEuhowd85xPdCp6GlA4DKEXBhF6LXDbY0tQLwswWbBryOICKZ6Wi9euaa2UCnewaMnFPbBGhojgZ/RYov7CZTWWEeowty2Lb3UHQaHhEZEY4Y/O6enapCMl1jSwdZBuXFeUGXklBTygvZuvdQ0GWISAJl7oAyaaahuYMxhXnkZI2sQzq1vJB9h7o40NYVdCkikiCDmWxdjqChpYOKNGrfT1Sb/LSKYgA2N7Ym5PNEJHgj6/Q0IBF3mlo60yr4E2ViaQEFuVlsbmwJuhQRSRAFfwLsP9RFd8RH1IXdXllm1IwtYlODzvhFRgoFfwI0NEfH6BmJZ/wA08cV0dTaye4D7UGXIiIJoOBPgJHYlbOv3nb+xZubAq5ERBJBwZ8ADS2dFOZlU5g/Mq+VTywtYFRuNk+ve8NN3CKSgQIJfjMrM7MHzWyNma02szcFUUeiNLV0MLZoZPXf7yvLjJnji5m/roFIZMgTr4lImgjqjP+HwF/d/VhgLhk+2XpTayfjRmgzT6/Z40tobOlg5U6N2yOS6VLeNmFmo4E3Ax8AcPdOMnjcn7bOHg60dTE2g+/YjafP/8zxJQA8tXYPJ1WVJrskEUmiIM74pxOdxvHnZvaimd1uZkX9VzKzm8xsqZktbWhI30m/eoczSPXkK6lWnJ/D3KpSnly7J+hSRGSYggj+HOAU4FZ3nwe0Ep3T93Xc/TZ3r3X32oqKilTXGLfeO1pHcht/r/NnV7J8234aYyORikhmCiL4twPb3X1x7PWDRL8IMlJdbOjikd7GD/D2kyYQcfjLit1BlyIiw5Dy4Hf33cA2M5sdW3QhsCrVdSRKXWMrRXnZFOSO/IFMZ48v4ZjKYv700s6gSxGRYQiqV8/NwK/M7GXgZOA/A6pj2DY3to749v1eZsZlcybxfN1e6g/qLl6RTBVI8Lv78lj7/Rx3v9Ld9wVRRyJsaTrEuAzu0TNY75w7EXf408u7gi5FRIZId+4OQ1tnD7sPtlNeFI4zfoAZFcXMqSrlgSXbcNfNXCKZSME/DK9d2A3PGT/AdWdUs7a+maVbMvYPNZFQU/APQ11vV86QtPH3umzuJEoKcvjFc1uCLkVEhkDBPwx1TbGbt0LQh7+vwrwc3nVKFX9ZsUsXeUUykIJ/GOoaWxlXnBeKrpz9fejsabjDrU9tDLoUERkkBf8wbG5qpWbsG0abCIXqsYW865Qq7n1+qyZoEckwCv5hqGtspWZcOIMf4ONvOYZIxPnR39cHXYqIDIKCf4haO7rZ09zBtBAH/5TyQq5/01Tue34ry7ftD7ocEYmTgn+ItsQu7Ia1qafXpy+eRWVJPl966BW6eyJBlyMicVDwD1FvH/6pYwsDriRYJQW5XHjseFbtOsiN9yzl3sVb4xrfX0SCo+Afot7hmMPcxt/rxMmlnDp1DPPXNrBhT0vQ5YjIUSj4h6iusZWKknyKR+gE64N12ZxJVJTkc9/zW9W3XyTNKbWGqK6pNXQXdo/UhJOXk8X73lTDT+Zv5K5n67jm9GomlBaksDoRiZfO+Idoc2Mr00MW/EdTXpTH+8+qoa2rhw/8/HkOtncFXZKIDEDBPwQH2rpobOkM3Rl/PCaXjeK606vZsKeFG+9eyqHO7qBLEpF+FPxD0Ds4m4J/YDPHl/C9q09mad1ePnTXEto6e4IuSUT6UPAPQW+PnukVCv7DaWnv5qpTq1i8aS/v+NEC7nqmLuiSRCQmsOA3s2wze9HM/hRUDUO1qbGVLIveuSqHd/KUMVx1ahWbG1u5Z1GdzvxF0kSQZ/yfBFYHuP8h29zYStWYQvJzwjcq52DNq46Ff0MrN96zhPYuhb9I0AIJfjOrAt4B3B7E/odrc2OL2vcHYV71GN51ahXPbmzixruXKvxFAhbUGf8PgM8Dhx3cxcxuMrOlZra0oaEhZYUdza8WbWFdfQudPRENTzAIp1SP4dtXzeWZjY3ccPcSmtXVUyQwKQ9+M3snsMfdXzjSeu5+m7vXunttRUVFiqo7uub2bjq7I4wL2XSLiXDVqVV856q5LNq0l6t/ukh3+IoEJIgz/rOBy82sDrgfeIuZ/TKAOoaksaUDCN8E64lw7+KtdHRHuP7MqWzY08Lbvv80P3h8XdBliYROyoPf3f/N3avcvQZ4D/B3d39vqusYqsaWTgCd8Q/DrPElfPjc6XRHnFvnb+SPL+0MuiSRUFE//kFqbOkgJ8soHZUbdCkZbfKYUXz0/BmMH13Azfe9yL/cu0xTOIqkSKCDtLn7U8BTQdYwWI0tHYwrzifLLOhSMt6Ywjw+fO509h3q5JYnN/DE6nquOb2aD5xVw9SQT3AjkkwanXOQGls6GT9azTyJkp1lfOLCmVx58mR+9Pf13PPcFn7+TB1zp5RxxdxJvGPORMaP1iifIomkpp5B6O6JsLe1Q+37SVA9tpDv/NNcFn7hAr506bF0dUf4+p9WceZ/PcFVtz7LHQs3s0e9gEQSQmf8g7B9XxsR14XdZJpYOoqb3jyDm948gw17mnnkld38ZcVu/v1Pq/jGn1dxwqRSLji2kgl9/gq49ozqACsWyTwK/kHoHZxNXTlT45jKEj5xYQmfuHAmGxta+OofVrKkbi8rdhzglOoxXHLiBIo0A5rIoOn/mkHY9Grw64w/keK9+/nSkyZy/uwK5q9t4JmNjazefZCrTqlKcnUiI4+CfxA2N7YwKjebwjwNzhaUwrwc3n7SROZVj+E3L2zjnkVbqGtq5eLjJ5Cd9VpPKzX/iByeLu4OwsY9rYwrzsPUlTNwE0oL+OfzZnB6TTlPr2/k9oWbNNWjSJwU/IOwfk+zuhamkdzsLK6cN5l3105h1/52/vfJDWzbeyjoskTSnoI/To0tHTS2dFKp4E87J08p4yPnTSc7y/jZgk0s27ov6JJE0pqCP07r6psBdPNWmppYOoqPnX8M1eWFPPjCdv79T6vo7jnsqN8ioabgj9P6+hYANfWksaL8HD549jTeNGMsdyzczAd+voT9hzqDLksk7ahXT5zW1jdTVphLifqNp7XsLOOyOZP4h3mT+T+/W8HltzzDT68/leMmjh6w26h6/0gY6Yw/Tut2NzOrskQ9ejLEu2uncP9HzqS9q4crf/wMDyzdFnRJImlDwR8Hd2ddfTOzJhQHXYoMwinVY/jzJ86ltmYMn3/wZR58YTud3Wr3F1Hwx6H+YAcH27uZPb4k6FJkkCpK8rnnQ2fwyQtn8uLWfdw6fwN7mjXYm4Sbgj8Oq3cfBKIzR0nmyc4y/vXiWXzgrBqa27v53yc3snzb/qDLEglMEJOtTzGzJ81stZmtNLNPprqGwVqx/QAAx08aHXAlMhwzx5dw81tmMrGsgAeWbuO3y7bTrLt9JYSC6KLSDXzG3ZeZWQnwgpk97u6rAqglLq/sOMD0cUWUFGi6xUxxuIHfSkflcuM503lidT3z1zVwyQ8W8J1/msubZoxNcYUiwUl58Lv7LmBX7Hmzma0GJgNpG/wrdhygtqY86DIkQbKzjLeeMIFjJ5Tw15W7ueZni7jujGo+89bZlBcdfshtdQeVkSLQNn4zqwHmAYsHeO8mM1tqZksbGhpSXluvppYOdh5o58TJauYZaarHFvHIJ8/lQ2dP4/4l2zjv20/y3cfW0tDcEXRpIkkVWPCbWTHwW+BT7n6w//vufpu717p7bUVFReoLjHllR7R9/8TJpYHVIMlTmJfDVy47nr9+8lzOOWYctzy5gTP+829cf8diHli6TXf+yogUyG2oZpZLNPR/5e4PBVFDvFYo+ENh5vgSbn3vqWxsaOF3y3bw8Es7+fyDL/MFgzlVZbx55jjauyJUlxe+btx/kUyU8uC36K2vdwCr3f17qd7/YL2y4wA1YwsZrQu7oTCjopjPvm02n3nrLF7efoAn1+5hwfpG/vepjfREnLycLKaPK2JmZTEzK9W9VzJTEGf8ZwPXA6+Y2fLYsi+5+yMB1HJE7s6yrfs5Sz0+RqzD9f659oxq5k4pY+6UMj510SwOtHXxrb+uYf2eFjbsaWHN7mZgF0+sqee6M6dy2ZxJjNLMbJIhgujVsxDIiL+V65oO0dDcwenT1KMn7EpH5XLCpFJOmBRt8mtq6WDN7mbW1jfz+Qdf5ht/Xs0N50zjhnOmUZSfox5AktY01OQRPL+5CYAzFPzSz9jifM4+Jp9brp3H4s17uWPhZr73+DrueW4Ln7jwGAzTtQBJWwr+I1i8eS/lRXnMqNDgbGFzuCag/u57Pjrq5wWzK5lRUcyjK3fzlT+sZFJpAe86tYqJpaOSWabIkGisniN4fvNeTq8p11DMEpfq8kJuPGca155ezYHYmEB/X7OHnogHXZrI6yj4D2PH/ja272tT+74Miplx4uRSPnXhTE6YPJq/ra7n9oWbOKgxgSSNKPgP45n1jQCcOV09emTwivJzeM9p1VxdO4Vd+9u55e8bWLypKeiyRAAF/2E9tqqeyWWjOG6i+mrL0M2dUsZHz59BQW4W196+mNsXbMJdTT8SLAX/AA51drNgfQMXHz9e7fsybONHF/Cx84/houMq+Y8/r+bTD7xEe1dP0GVJiCn4B7BgfSMd3REuPn580KXICFGQm82t153KZy6exe+X7+CqnzzLjv1tQZclIaXgH8BjK+sZXZCjC7uSUFlZxs0XzuRn19eypfEQl//PQhap3V8CoODvp7Wjm8dW7uai48eTm63DI4lz7+Kt3Lt4K3uaO7jh3GmYGe+9fTF3P1undn9JKSVbP797cQfNHd1cd8bUoEuREayypICPnT+D82ZV8NWHV/LpB15Sl09JGQV/H+7OLxdt4fiJozmluizocmSEK8jN5mfvq+VTF83kD8t38PYfLFCXT0kJBX8fz21qYs3uZt73pqnqzSMpcf+SbVSWFHDTm2fQ3tXDe25bxHU/W6RJ4CWpFPwxPRHnG39ezcTSAq44eXLQ5UjIVJcX8vG3HMPp08p5dmMTF3xnPvc8V0dHt7p9SuIp+GPuX7KVlTsP8qVLj9O46hKI/Jxsrjh5Mh89fwbTxxXxlT+s5OxvPsl3Hl3Lhj3NQZcnI4hG5wTW7D7INx9ZwxnTynnnnIlBlyMhVzWmkM+9bTbPbmzizoWb+fFTG7jlyQ1MHVtI7dRyjqksprq8kCnloxhTmEdJQQ7F+TnkqBeaxCn0wb9t7yE++PMlFOZn8/2rT1bbvqSF3uGeLzxuPLU15azadZD2rh7mr9vDb5dtH3Cb3GyjIDebipJ8SgpyKcnPefVLoaQgl+KCHEb3e11SkBNbL/p6VG625hEIgaAmW78E+CGQDdzu7t9MdQ2RiPPHl3fyf3+/Ane4/yNnMqlMY6dL+ikdlcubpo99dQav5vYutu1tY/u+Qzy6sp72rh7au3vo6IrQ3tVDR3eEQx3d7GvtfPV172M8sgxysrPIyTJy+zyOH51Pfk42+blZ5OdkkZ+bHX3MiT4W5WdTnJ8b/TLp+wUT+wIayl8m7k5nT4SO7gidsZ/XP++hsydCT8TpjjiRiNMTcSIefd0TccyMvOxozXk50cfol2AupaNyKczLDt0JXxCTrWcDPwYuBrYDS8zsYXdflax9ujsd3REaWzrY1NDK0rq9/OnlXWxqbGVuVSk/umYeU8cWJWv3Igkx0OQwp04dE/f2EXc6Y18C7d0ROrp6aO+KvO5LozsSobvH6eqJ0B3x6PPYsrLCPDq6e2jp6KapJRq6e1s76Yqt39UTIZ6pB0blZlNckENBbhbWbxbWrp5+Ad8T35fVcGRZtGvtqNxsRuVlM72imNJRuZSOyqFsVF70eWEuowtyKch97csjPyebvJws8rKzyM6KzriWk2VkZRnZFn3MiS3PstfeSwdBnPGfDmxw900AZnY/cAWQ8OD/77+u4b7nt9LS3k13n3+RWRb9H+ZTF8/i0hMnqG1UQiHLok1BBbnJ6bzg7nT1+Bv++og+9nDCpFJaOrppbu+ipaOb9q4ImxtbX/cZ2WZkZxu5WUZ2VhY52dHAzMkysrOzYsvt1b9I+gZrlkWHxbDYczMjC3B49S+C3i+2ju4I7Z09tHX10N4Vfex9vqmhhbbYe22dPST6nurs2BdD9qu1x5ZlZZGdxatfGllmmMG33jWHMxI8PHwQwT8Z2Nbn9XbgjP4rmdlNwE2xly1mtjaRRWwGHox/9XFAYyL3nwTpXmO61weqMVFUY2KMAxrP/PywPmPAIQiCCP6B/tZ5w5equ98G3Jb8co7OzJa6e23QdRxJuteY7vWBakwU1ZgYyawxiDaO7cCUPq+rgJ0B1CEiEkpBBP8SYKaZTTOzPOA9wMMB1CEiEkopb+px924z+zjwKNHunHe6+8pU1zFIadHkdBTpXmO61weqMVFUY2IkrUbTOOAiIuGifowiIiGj4BcRCZlQB7+ZXWJma81sg5l9cYD3zcx+FHv/ZTM7Jd5t06TGOjN7xcyWm9nSAGs81syeM7MOM/vsYLZNkxrT5TheF/tv/LKZPWtmc+PdNg3qS5djeEWsvuVmttTMzol32zSpMTHH0d1D+UP0wvJGYDqQB7wEHN9vnUuBvxC99+BMYHG82wZdY+y9OmBcGhzHSuA04BvAZwezbdA1ptlxPAsYE3v+9lT+exxOfWl2DIt57drmHGBNGv5bHLDGRB7HMJ/xvzp0hLt3Ar1DR/R1BXCPRy0CysxsYpzbBl1jqhy1Rnff4+5LgP7TSqXNcTxCjakST43Puvu+2MtFRO+BiWvbgOtLlXhqbPFYggJFvHbzaDr9WzxcjQkT5uAfaOiI/lNvHW6deLYNukaI/oN5zMxeiA2BkQzDORbpdByPJB2P4w1E/9IbyrZDMZz6II2OoZn9g5mtAf4MfGgw2wZcIyToOIZ5PP54ho443DpxDTuRAMOpEeBsd99pZpXA42a2xt2fTmiFwzsW6XQcjyStjqOZXUA0WHvbflNxHIdTH6TRMXT33wG/M7M3A/8OXBTvtgkwnBohQccxzGf88Qwdcbh1UjXsxHBqxN17H/cAvyP6Z2YQNSZj28EY1n7S6Tia2RzgduAKd28azLYB1pdWx7BPTU8DM8xs3GC3DajGxB3HRF+8yJQfon/tbAKm8dpFlhP6rfMOXn/h9Pl4t02DGouAkj7PnwUuCaLGPut+jddf3E2b43iEGtPmOALVwAbgrKH+fgHVl07H8Bheu3B6CrAj9v9O2vxbPEKNCTuOCf2lMu2HaI+YdUSvsn85tuyfgX+OPTeik8ZsBF4Bao+0bTrVSLTXwEuxn5UB1ziB6JnOQWB/7PnoNDuOA9aYZsfxdmAfsDz2szSV/x6HWl+aHcMvxGpYDjwHnJPKYzicGhN5HDVkg4hIyIS5jV9EJJQU/CIiIaPgFxEJGQW/iEjIKPhFREJGwS8iEjIKfslIZtYSe6wxs2uTuJ/zzey5fstyzKzezCaa2SNmVnaE7W83s+P71jzAOl83s4tiz58ys9rY80fMrCz287GE/VISegp+yXQ1QNKCH3gaqDKzmj7LLgJWuPsud7/U3fcfbmN3v9HdVx1pB+7+FXf/2wDLez+7DFDwS8Io+CXTfRM4NzYxxb+aWbaZfdvMlsQms/gIvHrmPt/MHjCzdWb2zdjEIc/HJraYMdCHu3sE+A1wdZ/F7wHui31unZmNM7MiM/uzmb1kZivM7OrY+6+ewcdef9fMlpnZE2ZWEVt2l5ld1X/fvZ8d+x1nxH7Hb5vZL8zsij7r/crMLh/mcZQQUfBLpvsisMDdT3b37xMdFfKAu59GdGKVD5vZtNi6c4FPAicB1wOz3P10okMN3HyEfdxHNOwxs3yit9z/tt86lwA73X2uu58I/HWAzykClrn7KcB84KuD+B03xn7Hz8Xq/WCsnlKiE6A8EudniSj4ZcR5K/A+M1sOLAbGAjNj7y2JNc90EB0n5bHY8leINhkNyKMTtBSb2WyiM0st8tcmHOn1CnCRmf23mZ3r7gcG+KgI8OvY81/y+mGL4+bu84FjYkPzXgP81t27h/JZEk5hHo9fRiYDbnb3R1+30Ox8oKPPokif1xGO/v/C/UTP+o8j1szTl7uvM7NTif418F9m9pi7f/0onzmcgbJ+AVwXq+lDR1lX5HV0xi+Zrhko6fP6UeCjZpYLYGazzKwoAfu5D3gv8Bbg4f5vmtkk4JC7/xL4DtHhdPvLAnrb8q8FFsa57/6/I8BdwKcA3H1lnJ8jAuiMXzLfy0C3mb1ENAx/SLTZZpmZGdAAXDncnbj7KjM7BLzg7q0DrHIS8G0zixCdt/ejA6zTCpxgZi8AB3j9BeMj7bvJzJ4xsxXAX9z9c+5eb2argd8P5feRcNOwzCIZyMwKiV5XOOUw1xNEDktNPSIZJnaz1xrgfxT6MhQ64xeJMbMvA//Ub/Fv3P0bQdQjkiwKfhGRkFFTj4hIyCj4RURCRsEvIhIyCn4RkZD5//L90/TaIzSLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_Visibility'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_MRP', ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_MRP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Outlet_Sales', ylabel='Density'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Outlet_Sales', ylabel='Density'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Item_Outlet_Sales'] = np.log(1+df['Item_Outlet_Sales'])\n",
    "sns.distplot(df['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Item_Fat_Content', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"Item_Fat_Content\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[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": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = list(df['Item_Type'].unique())\n",
    "chart = sns.countplot(df[\"Item_Type\"])\n",
    "chart.set_xticklabels(labels=l, rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outlet_Establishment_Year', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Outlet_Establishment_Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outlet_Size', ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Outlet_Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outlet_Location_Type', ylabel='count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Outlet_Location_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Outlet_Type', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Outlet_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "sns.heatmap(corr, annot=True, cmap='autumn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Years</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>8.225808</td>\n",
       "      <td>Food</td>\n",
       "      <td>14</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>6.096776</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>4</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>7.648868</td>\n",
       "      <td>Food</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>6.597664</td>\n",
       "      <td>Food</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.066132</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>6.903451</td>\n",
       "      <td>Non-Consumable</td>\n",
       "      <td>26</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         0.066132   \n",
       "4           NCD19         8.93       Non-Edible         0.066132   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998       Small               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales   New_Item_Type  Outlet_Years  \n",
       "0  Supermarket Type1           8.225808            Food            14  \n",
       "1  Supermarket Type2           6.096776          Drinks             4  \n",
       "2  Supermarket Type1           7.648868            Food            14  \n",
       "3      Grocery Store           6.597664            Food            15  \n",
       "4  Supermarket Type1           6.903451  Non-Consumable            26  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['Outlet'] = le.fit_transform(df['Outlet_Identifier'])\n",
    "cat_col = ['Item_Fat_Content', 'Item_Type', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'New_Item_Type']\n",
    "for col in cat_col:\n",
    "    df[col] = le.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Outlet_Years</th>\n",
       "      <th>Outlet</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Location_Type_0</th>\n",
       "      <th>Outlet_Location_Type_1</th>\n",
       "      <th>Outlet_Location_Type_2</th>\n",
       "      <th>Outlet_Type_0</th>\n",
       "      <th>Outlet_Type_1</th>\n",
       "      <th>Outlet_Type_2</th>\n",
       "      <th>Outlet_Type_3</th>\n",
       "      <th>New_Item_Type_0</th>\n",
       "      <th>New_Item_Type_1</th>\n",
       "      <th>New_Item_Type_2</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>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.225808</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>6.096776</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</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>1</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>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>7.648868</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>6.597664</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>6.903451</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight  Item_Visibility  Item_Type  Item_MRP  \\\n",
       "0           FDA15         9.30         0.016047          4  249.8092   \n",
       "1           DRC01         5.92         0.019278         14   48.2692   \n",
       "2           FDN15        17.50         0.016760         10  141.6180   \n",
       "3           FDX07        19.20         0.066132          6  182.0950   \n",
       "4           NCD19         8.93         0.066132          9   53.8614   \n",
       "\n",
       "  Outlet_Identifier  Outlet_Establishment_Year  Item_Outlet_Sales  \\\n",
       "0            OUT049                       1999           8.225808   \n",
       "1            OUT018                       2009           6.096776   \n",
       "2            OUT049                       1999           7.648868   \n",
       "3            OUT010                       1998           6.597664   \n",
       "4            OUT013                       1987           6.903451   \n",
       "\n",
       "   Outlet_Years  Outlet  ...  Outlet_Location_Type_0  Outlet_Location_Type_1  \\\n",
       "0            14       9  ...                       1                       0   \n",
       "1             4       3  ...                       0                       0   \n",
       "2            14       9  ...                       1                       0   \n",
       "3            15       0  ...                       0                       0   \n",
       "4            26       1  ...                       0                       0   \n",
       "\n",
       "   Outlet_Location_Type_2  Outlet_Type_0  Outlet_Type_1  Outlet_Type_2  \\\n",
       "0                       0              0              1              0   \n",
       "1                       1              0              0              1   \n",
       "2                       0              0              1              0   \n",
       "3                       1              1              0              0   \n",
       "4                       1              0              1              0   \n",
       "\n",
       "   Outlet_Type_3  New_Item_Type_0  New_Item_Type_1  New_Item_Type_2  \n",
       "0              0                0                1                0  \n",
       "1              0                1                0                0  \n",
       "2              0                0                1                0  \n",
       "3              0                0                1                0  \n",
       "4              0                0                0                1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=['Item_Fat_Content', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'New_Item_Type'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Outlet_Establishment_Year', 'Item_Identifier', 'Outlet_Identifier', 'Item_Outlet_Sales'])\n",
    "y = df['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "def train(model, X, y):\n",
    "    \n",
    "    model.fit(X, y)\n",
    "    \n",
    "    pred = model.predict(X)\n",
    "    \n",
    "    cv_score = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=5)\n",
    "    cv_score = np.abs(np.mean(cv_score))\n",
    "    \n",
    "    print(\"Model Report\")\n",
    "    print(\"MSE:\",mean_squared_error(y,pred))\n",
    "    print(\"CV Score:\", cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 0.28800650325017957\n",
      "CV Score: 0.28913252999364325\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Model Coefficients'}>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "model = LinearRegression(normalize=True)\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()\n",
    "coef.plot(kind='bar', title=\"Model Coefficients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 0.4280298560568348\n",
      "CV Score: 0.4289289251562642\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Model Coefficients'}>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Ridge(normalize=True)\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()\n",
    "coef.plot(kind='bar', title=\"Model Coefficients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 0.7628688679102086\n",
      "CV Score: 0.7630789166281843\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Model Coefficients'}>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Lasso()\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()\n",
    "coef.plot(kind='bar', title=\"Model Coefficients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 2.7767015319289398e-34\n",
      "CV Score: 0.5720127268644755\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature Importance'}>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "model = DecisionTreeRegressor()\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 0.04229961270609278\n",
      "CV Score: 0.31067327336235573\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature Importance'}>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 1.0398099340049763e-28\n",
      "CV Score: 0.33223164976074726\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature Importance'}>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}