big-data-mart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "922694f1",
   "metadata": {},
   "source": [
    "Big Data Mart Sales Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98a7335d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \ n",
    "import numpy as np \ n",
    "import sklearn \ n",
    "import seaborn as sns \ n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db39a272",
   "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')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv (\" bigdatamart_Train.csv \ ") \ n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c618ae09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / html": [
       "<div> \ n",
       "<style scoped> \ n",
       ".dataframe tbody tr th: only-of-type {\ n",
       "vertical-align: middle; \ n",
       "} \ n",
       "\ n",
       ".dataframe tbody tr th {\ n",
       "vertical-align: top; \ n",
       "} \ n",
       "\ n",
       ".dataframe thead th {\ n",
       "text-align: right; \ n",
       "} \ n",
       "</style> \ n",
       "<table border = \" 1 \ "class = \" dataframe \ "> \ n",
       "<thead> \ n",
       "<tr style = \" text-align: right; \ "> \ n",
       "<th> </th> \ n",
       "<th> Item_Weight </th> \ n",
       "<th> Item_Visibility </th> \ n",
       "<th> Item_MRP </th> \ n",
       "<th> Outlet_Establishment_Year </th> \ n",
       "<th> Item_Outlet_Sales </th> \ n",
       "</tr> \ n",
       "</thead> \ n",
       "<tbody> \ n",
       "<tr> \ n",
       "<th> Item_Weight </th> \ n",
       "<td> 1.000000 </td> \ n",
       "<td> -0.014048 </td> \ n",
       "<td> 0.027141 </td> \ n",
       "<td> -0.011588 </td> \ n",
       "<td> 0.014123 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> Item_Visibility </th> \ n",
       "<td> -0.014048 </td> \ n",
       "<td> 1.000000 </td> \ n",
       "<td> -0.001315 </td> \ n",
       "<td> -0.074834 </td> \ n",
       "<td> -0.128625 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> Item_MRP </th> \ n",
       "<td> 0.027141 </td> \ n",
       "<td> -0.001315 </td> \ n",
       "<td> 1.000000 </td> \ n",
       "<td> 0.005020 </td> \ n",
       "<td> 0.567574 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> Outlet_Establishment_Year </th> \ n",
       "<td> -0.011588 </td> \ n",
       "<td> -0.074834 </td> \ n",
       "<td> 0.005020 </td> \ n",
       "<td> 1.000000 </td> \ n",
       "<td> -0.049135 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> Item_Outlet_Sales </th> \ n",
       "<td> 0.014123 </td> \ n",
       "<td> -0.128625 </td> \ n",
       "<td> 0.567574 </td> \ n",
       "<td> -0.049135 </td> \ n",
       "<td> 1.000000 </td> \ n",
       "</tr> \ n",
       "</tbody> \ n",
       "</table> \ n",
       "</div>"
      ],
      "text / plain": [
       "Item_Weight Item_Visibility Item_MRP \\\ n",
       "Item_Weight 1.000000 -0.014048 0.027141 \ n",
       "Item_Visibility -0.014048 1.000000 -0.001315 \ n",
       "Item_MRP 0.027141 -0.001315 1.000000 \ n",
       "Outlet_Establishment_Year -0.011588 -0.074834 0.005020 \ n",
       "Item_Outlet_Sales 0.014123 -0.128625 0.567574 \ n",
       "\ n",
       "Outlet_Establishment_Year Item_Outlet_Sales \ n",
       "Item_Weight -0.011588 0.014123 \ n",
       "Item_Visibility -0.074834 -0.128625 \ n",
       "Item_MRP 0.005020 0.567574 \ n",
       "Outlet_Establishment_Year 1.000000 -0.049135 \ n",
       "Item_Outlet_Sales -0.049135 1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "08eb3189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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MwscESrDHGhElEDgm1XFWntncspuOzBGuMMWESkfeCnqYAY4G5qnq4RyGZDsyugzXGmDCp6onBz0WkN / APj8IxHZwNcjLGmJ23HhjqdRCmY7IarDHGhElEHqZuJicfMBJY6F1EpiOzBGuMMeFbCiTgJNlC4CVV / crbkExHZQnWGGNa4E4o8TfgYuBHnDmIewNPicgsVa3yMj7TMVkfrDHGtOweIAfoq6qjVHUk0A / IAu71MjDTcdllOsYY0wIRWQEM1AZfmCKSAHyvqgO8icx0ZFaDNcaYlmnD5Oou9FP / 9nXG1LIEa4wxLVsqIhc0XCgi5wHfexCPiQHWRGyMMS0QkV7Am0AZMBen1rov0Bk4VVU3eBie6aAswRpjTJhE5HCcW9YJsERVP / M4JNOBWYI1xhhjosD6YI0xxpgosARrjDHGRIElWGOMMSYKLMEaY4wxUfD / byOHQMD9yoAAAAAASUVORK5CYII = \ n ",
      "text / plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.corr () \ n",
    "sns.heatmap (corr, annot = True, cmap = 'coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "981a83a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb8675ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> \ n",
      "RangeIndex: 8523 entries, 0 to 8522 \ n",
      "Data columns (total 12 columns): \ n",
      "# Column Non-Null Count Dtype \ n",
      "--- ------ -------------- ----- \ n",
      "0 Item_Identifier 8523 non-null object \ n",
      "1 Item_Weight 7060 non-null float64 \ n",
      "2 Item_Fat_Content 8523 non-null object \ n",
      "3 Item_Visibility 8523 non-null float64 \ n",
      "4 Item_Type 8523 non-null object \ n",
      "5 Item_MRP 8523 non-null float64 \ n",
      "6 Outlet_Identifier 8523 non-null object \ n",
      "7 Outlet_Establishment_Year 8523 non-null int64 \ n",
      "8 Outlet_Size 6113 non-null object \ n",
      "9 Outlet_Location_Type 8523 non-null object \ n",
      "10 Outlet_Type 8523 non-null object \ n",
      "11 Item_Outlet_Sales 8523 non-null float64 \ n",
      "dtypes: float64 (4), int64 (1), object (7) \ n",
      "memory usage: 799.2+ KB \ n"
     ]
    }
   ],
   "source": [
    "data.info ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfa7f822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "Item_Identifier 1559 \ n",
       "Item_Weight 415 \ 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 3 \ n",
       "Outlet_Location_Type 3 \ n",
       "Outlet_Type 4 \ n",
       "Item_Outlet_Sales 3493 \ n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb6bba49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / html": [
       "<div> \ n",
       "<style scoped> \ n",
       ".dataframe tbody tr th: only-of-type {\ n",
       "vertical-align: middle; \ n",
       "} \ n",
       "\ n",
       ".dataframe tbody tr th {\ n",
       "vertical-align: top; \ n",
       "} \ n",
       "\ n",
       ".dataframe thead th {\ n",
       "text-align: right; \ n",
       "} \ n",
       "</style> \ n",
       "<table border = \" 1 \ "class = \" dataframe \ "> \ n",
       "<thead> \ n",
       "<tr style = \" text-align: right; \ "> \ n",
       "<th> </th> \ n",
       "<th> Item_Weight </th> \ n",
       "<th> Item_Visibility </th> \ n",
       "<th> Item_MRP </th> \ n",
       "<th> Outlet_Establishment_Year </th> \ n",
       "<th> Item_Outlet_Sales </th> \ n",
       "</tr> \ n",
       "</thead> \ n",
       "<tbody> \ n",
       "<tr> \ n",
       "<th> count </th> \ n",
       "<td> 7060.000000 </td> \ n",
       "<td> 8523.000000 </td> \ n",
       "<td> 8523.000000 </td> \ n",
       "<td> 8523.000000 </td> \ n",
       "<td> 8523.000000 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> mean </th> \ n",
       "<td> 12.857645 </td> \ n",
       "<td> 0.066132 </td> \ n",
       "<td> 140.992782 </td> \ n",
       "<td> 1997.831867 </td> \ n",
       "<td> 2181.288914 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> std </th> \ n",
       "<td> 4.643456 </td> \ n",
       "<td> 0.051598 </td> \ n",
       "<td> 62.275067 </td> \ n",
       "<td> 8.371760 </td> \ n",
       "<td> 1706.499616 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> min </th> \ n",
       "<td> 4.555000 </td> \ n",
       "<td> 0.000000 </td> \ n",
       "<td> 31.290000 </td> \ n",
       "<td> 1985.000000 </td> \ n",
       "<td> 33.290000 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 25% </th> \ n",
       "<td> 8.773750 </td> \ n",
       "<td> 0.026989 </td> \ n",
       "<td> 93.826500 </td> \ n",
       "<td> 1987.000000 </td> \ n",
       "<td> 834.247400 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 50% </th> \ n",
       "<td> 12.600000 </td> \ n",
       "<td> 0.053931 </td> \ n",
       "<td> 143.012800 </td> \ n",
       "<td> 1999.000000 </td> \ n",
       "<td> 1794.331000 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 75% </th> \ n",
       "<td> 16.850000 </td> \ n",
       "<td> 0.094585 </td> \ n",
       "<td> 185.643700 </td> \ n",
       "<td> 2004.000000 </td> \ n",
       "<td> 3101.296400 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> max </th> \ n",
       "<td> 21.350000 </td> \ n",
       "<td> 0.328391 </td> \ n",
       "<td> 266.888400 </td> \ n",
       "<td> 2009.000000 </td> \ n",
       "<td> 13086.964800 </td> \ n",
       "</tr> \ n",
       "</tbody> \ n",
       "</table> \ n",
       "</div>"
      ],
      "text / plain": [
       "Item_Weight Item_Visibility Item_MRP Outlet_Establishment_Year \\\ n",
       "count 7060.000000 8523.000000 8523.000000 8523.000000 \ n",
       "mean 12.857645 0.066132 140.992782 1997.831867 \ n",
       "std 4.643456 0.051598 62.275067 8.371760 \ n",
       "min 4.555000 0.000000 31.290000 1985.000000 \ n",
       "25% 8.773750 0.026989 93.826500 1987.000000 \ n",
       "50% 12.600000 0.053931 143.012800 1999.000000 \ n",
       "75% 16.850000 0.094585 185.643700 2004.000000 \ n",
       "max 21.350000 0.328391 266.888400 2009.000000 \ n",
       "\ n",
       "Item_Outlet_Sales \ n",
       "count 8523.000000 \ n",
       "mean 2181.288914 \ n",
       "std 1706.499616 \ n",
       "min 33.290000 \ n",
       "25% 834.247400 \ n",
       "50% 1794.331000 \ n",
       "75% 3101.296400 \ n",
       "max 13086.964800"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5e5a1ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "array (['Low Fat', 'Regular', 'low fat', 'LF', 'reg'], dtype = object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['Item_Fat_Content']. unique ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3530375b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "array (['Medium', nan, 'High', 'Small'], dtype = object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['Outlet_Size']. unique ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "97feaae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "array (['Tier 1', 'Tier 3', 'Tier 2'], dtype = object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['Outlet_Location_Type']. unique ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82c5c5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "array (['Supermarket Type1', 'Supermarket Type2', 'Grocery Store', \ n",
       "'Supermarket Type3'], dtype = object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['Outlet_Type']. unique ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d773176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "Counter ({'Low Fat': 5089, \ n",
       "'Regular': 2889, \ n",
       "'low fat': 112, \ n",
       "'LF': 316, \ n",
       "'reg': 117})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter \ n",
    "Counter (data ['Item_Fat_Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f43c4627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "Counter ({'Medium': 2793, at: 2410, 'High': 932, 'Small': 2388})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter \ n",
    "Counter (data ['Outlet_Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f73d7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "Counter ({'Tier 1': 2388, 'Tier 3': 3350, 'Tier 2': 2785})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter \ n",
    "Counter (data ['Outlet_Location_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "007bc4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "Counter ({'Supermarket Type1': 5577, \ n",
       "'Supermarket Type2': 928, \ n",
       "Grocery Store ': 1083, \ n",
       "'Supermarket Type3': 935})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter \ n",
    "Counter (data ['Outlet_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ee480634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Outlet_Type', ylabel = 'count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (x = 'Outlet_Type', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eca45ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as plt \ n",
    "import warnings \ n",
    "% matplotlib inline \ n",
    "warnings.filterwarnings ('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad33c641",
   "metadata": {},
   "source": [
    "# loding the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7f32829",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply (lambda x: len (x.unique ()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474f4a47",
   "metadata": {},
   "source": [
    "# preproccesing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d6dac03a",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull (). sum ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bdf789f9",
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col = [] \ n",
    "for x in data.dtypes.index: \ n",
    "if data.dtypes [x] == 'object': \ n",
    "cat_col.append (x) \ n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "71a34f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col.remove (\" Item_Identifier \ ") \ n",
    "cat_col.remove (\" Outlet_Identifier \ ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e530340d",
   "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 (data [col] .value_counts ()) \ n",
    "print ()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb67fb95",
   "metadata": {},
   "source": [
    "# fill missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bdb7f48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / html": [
       "<div> \ n",
       "<style scoped> \ n",
       ".dataframe tbody tr th: only-of-type {\ n",
       "vertical-align: middle; \ n",
       "} \ n",
       "\ n",
       ".dataframe tbody tr th {\ n",
       "vertical-align: top; \ n",
       "} \ n",
       "\ n",
       ".dataframe thead th {\ n",
       "text-align: right; \ n",
       "} \ n",
       "</style> \ n",
       "<table border = \" 1 \ "class = \" dataframe \ "> \ n",
       "<thead> \ n",
       "<tr style = \" text-align: right; \ "> \ n",
       "<th> </th> \ n",
       "<th> Item_Weight </th> \ n",
       "</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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_weight_mean = data.pivot_table (values ​​= \" Item_Weight \ ", index = \" Item_Identifier \ ") \ n",
    "item_weight_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2920ee13",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss_bool = data ['Item_Weight']. isnull () \ n",
    "miss_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c9df831b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, item in enumerate (data ['Item_Identifier']): \ n",
    "if miss_bool [i]: \ n",
    "if item in item_weight_mean: \ n",
    "data ['Item_Weight'] [i] = item_weight_mean.loc [item] ['Item_Weight'] \ n",
    "else: \ n",
    "data ['Item_Weight'] [i] = np.mean (data ['Item_Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5c3fc3d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['Item_Weight']. isnull (). sum ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "937cc29f",
   "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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlet_size_mode = data.pivot_table (values ​​= 'Outlet_Size', columns = 'Outlet_Type', aggfunc = lambda x: x.mode () [0]) \ n",
    "outlet_size_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3b091e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "miss_bool = data ['Outlet_Size']. isnull () \ n",
    "data.loc [miss_bool, 'Outlet_Size'] = data.loc [miss_bool, 'Outlet_Type']. apply (lambda x: outlet_size_mode [x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "55eb86f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['Outlet_Size']. isnull (). sum ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "442b93fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "526"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum (data ['Item_Visibility'] == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "49816409",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc [:, 'Item_Visibility']. replace ([0], [data ['Item_Visibility']. mean ()], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f06a34fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum (data ['Item_Visibility'] == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2820f754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / html": [
       "<div> \ n",
       "<style scoped> \ n",
       ".dataframe tbody tr th: only-of-type {\ n",
       "vertical-align: middle; \ n",
       "} \ n",
       "\ n",
       ".dataframe tbody tr th {\ n",
       "vertical-align: top; \ n",
       "} \ n",
       "\ n",
       ".dataframe thead th {\ n",
       "text-align: right; \ n",
       "} \ n",
       "</style> \ n",
       "<table border = \" 1 \ "class = \" dataframe \ "> \ n",
       "<thead> \ n",
       "<tr style = \" text-align: right; \ "> \ n",
       "<th> </th> \ n",
       "<th> Item_Identifier </th> \ n",
       "<th> Item_Weight </th> \ n",
       "<th> Item_Fat_Content </th> \ n",
       "<th> Item_Visibility </th> \ n",
       "<th> Item_Type </th> \ n",
       "<th> Item_MRP </th> \ n",
       "<th> Outlet_Identifier </th> \ n",
       "<th> Outlet_Establishment_Year </th> \ n",
       "<th> Outlet_Size </th> \ n",
       "<th> Outlet_Location_Type </th> \ n",
       "<th> Outlet_Type </th> \ n",
       "<th> Item_Outlet_Sales </th> \ n",
       "</tr> \ n",
       "</thead> \ n",
       "<tbody> \ n",
       "<tr> \ n",
       "<th> 0 </th> \ n",
       "<td> FDA15 </td> \ n",
       "<td> 9.300 </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> 5.920 </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> 17,500 </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> 19,200 </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",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 4 </th> \ n",
       "<td> NCD19 </td> \ n",
       "<td> 8.930 </td> \ n",
       "<td> Low Fat </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",
       "</tr> \ n",
       "<tr> \ n",
       "<th> ... </th> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "<td> ... </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 8518 </th> \ n",
       "<td> FDF22 </td> \ n",
       "<td> 6.865 </td> \ n",
       "<td> Low Fat </td> \ n",
       "<td> 0.056783 </td> \ n",
       "<td> Snack Foods </td> \ n",
       "<td> 214.5218 </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> 2778.3834 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 8519 </th> \ n",
       "<td> FDS36 </td> \ n",
       "<td> 8.380 </td> \ n",
       "<td> Regular </td> \ n",
       "<td> 0.046982 </td> \ n",
       "<td> Baking Goods </td> \ n",
       "<td> 108.1570 </td> \ n",
       "<td> OUT045 </td> \ n",
       "<td> 2002 </td> \ n",
       "<td> Small </td> \ n",
       "<td> Tier 2 </td> \ n",
       "<td> Supermarket Type1 </td> \ n",
       "<td> 549.2850 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 8520 </th> \ n",
       "<td> NCJ29 </td> \ n",
       "<td> 10,600 </td> \ n",
       "<td> Low Fat </td> \ n",
       "<td> 0.035186 </td> \ n",
       "<td> Health and Hygiene </td> \ n",
       "<td> 85.1224 </td> \ n",
       "<td> OUT035 </td> \ n",
       "<td> 2004 </td> \ n",
       "<td> Small </td> \ n",
       "<td> Tier 2 </td> \ n",
       "<td> Supermarket Type1 </td> \ n",
       "<td> 1193.1136 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 8521 </th> \ n",
       "<td> FDN46 </td> \ n",
       "<td> 7.210 </td> \ n",
       "<td> Regular </td> \ n",
       "<td> 0.145221 </td> \ n",
       "<td> Snack Foods </td> \ n",
       "<td> 103.1332 </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> 1845.5976 </td> \ n",
       "</tr> \ n",
       "<tr> \ n",
       "<th> 8522 </th> \ n",
       "<td> DRG01 </td> \ n",
       "<td> 14,800 </td> \ n",
       "<td> Low Fat </td> \ n",
       "<td> 0.044878 </td> \ n",
       "<td> Soft Drinks </td> \ n",
       "<td> 75.4670 </td> \ n",
       "<td> OUT046 </td> \ n",
       "<td> 1997 </td> \ n",
       "<td> Small </td> \ n",
       "<td> Tier 1 </td> \ n",
       "<td> Supermarket Type1 </td> \ n",
       "<td> 765.6700 </td> \ n",
       "</tr> \ n",
       "</tbody> \ n",
       "</table> \ n",
       "<p> 8523 rows × 12 columns </p> \ n",
       "</div>"
      ],
      "text / plain": [
       "Item_Identifier Item_Weight Item_Fat_Content Item_Visibility \\\ n",
       "0 FDA15 9,300 Low Fat 0.016047 \ n",
       "1 DRC01 5.920 Regular 0.019278 \ n",
       "2 FDN15 17,500 Low Fat 0.016760 \ n",
       "3 FDX07 19.200 Regular 0.066132 \ n",
       "4 NCD19 8,930 Low Fat 0.066132 \ n",
       "... ... ... ... ... \ n",
       "8518 FDF22 6.865 Low Fat 0.056783 \ n",
       "8519 FDS36 8.380 Regular 0.046982 \ n",
       "8520 NCJ29 10.600 Low Fat 0.035186 \ n",
       "8521 FDN46 7.210 Regular 0.145221 \ n",
       "8522 DRG01 14,800 Low Fat 0.044878 \ 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",
       "8518 Snack Foods 214.5218 OUT013 \ n",
       "8519 Baking Goods 108.1570 OUT045 \ n",
       "8520 Health and Hygiene 85.1224 OUT035 \ n",
       "8521 Snack Foods 103.1332 OUT018 \ n",
       "8522 Soft Drinks 75.4670 OUT046 \ 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",
       "8518 1987 High Tier 3 \ n",
       "8519 2002 Small Tier 2 \ n",
       "8520 2004 Small Tier 2 \ n",
       "8521 2009 Medium Tier 3 \ n",
       "8522 1997 Small Tier 1 \ 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 \ n",
       "... ... ... \ n",
       "8518 Supermarket Type1 2778.3834 \ n",
       "8519 Supermarket Type1 549.2850 \ n",
       "8520 Supermarket Type1 1193.1136 \ n",
       "8521 Supermarket Type2 1845.5976 \ n",
       "8522 Supermarket Type1 765.6700 \ n",
       "\ n",
       "[8523 rows x 12 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22161506",
   "metadata": {},
   "source": [
    "# combine item fat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dcab5c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "Low Fat 5517 \ n",
       "Regular 3006 \ n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['Item_Fat_Content'] = data ['Item_Fat_Content']. replace ({'LF': 'Low Fat', 'reg': 'Regular', 'low fat': 'Low Fat'}) \ n",
    "data ['Item_Fat_Content']. value_counts ()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cceae44",
   "metadata": {},
   "source": [
    "# CREATION OF NEW ATTRIBUTES \ n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "add32dfb",
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['New_Item_Type'] = data ['Item_Identifier']. apply (lambda x: x [: 2]) \ n",
    "data ['New_Item_Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3485dbde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "Food 6125 \ n",
       "Non-Consumable 1599 \ n",
       "Drinks 799 \ n",
       "Name: New_Item_Type, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['New_Item_Type'] = data ['New_Item_Type']. map ({'FD': 'Food', 'NC': 'Non-Consumable', 'DR': 'Drinks'}) \ n",
    "data ['New_Item_Type']. value_counts ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c9378676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "Low Fat 3918 \ n",
       "Regular 3006 \ n",
       "Non-Edible 1599 \ n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc [data ['New_Item_Type'] == 'Non-Consumable', 'Item_Fat_Content'] = 'Non-Edible' \ n",
    "data ['Item_Fat_Content']. value_counts ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8a7f82e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create small values ​​for establishments \ n",
    "date ['Outlet_Years'] = 2013-date ['Outlet_Establishment_Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c539f292",
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date ['Outlet_Years']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1133ef",
   "metadata": {},
   "source": [
    "# EXPLORATORY DASTA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "839d2261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Item_Weight', ylabel = 'Density'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot (data ['Item_Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "eead12af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Item_Visibility', ylabel = 'Density'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot (data ['Item_Visibility'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a2455482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Item_MRP', ylabel = 'Density'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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f6iv8u3Gum3ReSA76 // zwOIyG5fTeCXInJGRO4VkY / 7Nto5LiIr5rjOE / iW8ReR5UAP0DbDua8y86qmLwIrZ3hNqXnTxKGU / + 4GXvT9Rf89vEuH9BhjtgHbgL / xrfsDsAn4MrAB + CRQbozZDvwI + NIc1 kF6nyrm34MeHSWc + / + fgTTRvISIxeFdGPu7nvSnlN00cSs3fu4FbfHsg7Me7LlCZ77UDxrtR0Aje9YD 4Dt HG + + + T11wewdtc9QG8qxtP9RcRacW7mN3Dk44n uKpAGrxLuKnVFDFhDsApSKYAF8yxjzzloMiu4GRSYc8k3724N / v3W BbwMVxphe70Kub3E1MAD8BPifwN + / + 5jg8Z754MSllGaxxK a8P797UE54BvuDb0wERKfctY79gxpgh4B + Bf53jnK / grfVkBuO6SvlDE4dS / jsGjIvIURG5C29 / xUngkIhUAj8kiLV4Y8wjxrcn9iznNOFdtv2OYF1XqbnosupKKaUCojUOpZRSAdHOcaXCQETuAT4y5fCvjDEz9mkoZRfaVKWUUiog2lSllFIqIJo4lFJKBUQTh1JKqYBo4lBKKRWQ / wfg0kCG + lDUMgAAAABJRU5ErkJggg == \ n ",
      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot (data ['Item_MRP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6917425b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Item_Outlet_Sales', ylabel = 'Density'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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/ oqcYTP9iSceYo1YAbSLyvojcCTyM1wJZJiKrgAc4wd4BVZ0HPAK8LSIfAg8B14W76vCWGviViPwDLwGE / QX4XFcDCYBrgVtE5H1gNd5qoOAtcvddd5G / u4EE1 Nd01mOt7TBtarajpccfywib9H9NZl + / x + T2W + F + Pv / uyr8MrHLHHA881v1PxPQ3trSBMcaYhLGWjjHGmISxgQTGxIGI / AC4slPxs6r6 // kUz33AJzsV / 0pVH / UjHtN / WfeaMcaYhLHuNWOMMQljSccYY0zCWNIxxhiTMJZ0jDHGJMz / A9DobpXNmaisAAAAAElFTkSuQmCC \ n ",
      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot (data ['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dd20adf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#with log you can use standerdisation or in max \ n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "660ae475",
   "metadata": {},
   "outputs": [],
   "source": [
    "data ['Item_Outlet_Sales'] = np.log (1 + data ['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9e8484d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Item_Outlet_Sales', ylabel = 'Density'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot (data ['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "364aff1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Item_Fat_Content', ylabel = 'count'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (data ['Item_Fat_Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "05e3f8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Item_Type', ylabel = 'count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (data ['Item_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "772de08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3785d34d",
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text / plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure (figsize = (10,7)) \ n",
    "l = list (data ['Item_Type']. unique ()) \ n",
    "chart = sns.countplot (data ['Item_Type']) \ n",
    "chart.set_xticklabels (labels = l, rotation = 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5f03bc6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Outlet_Establishment_Year', ylabel = 'count'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (data ['Outlet_Establishment_Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c62d424a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: xlabel = 'Outlet_Size', ylabel = 'count'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (data ['Outlet_Size'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f976cb8",
   "metadata": {},
   "source": [
    "# correlation matrix \ n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bc75afe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AapzWcjvgGOBKVd0exPCMKXOWnI0xYUNEeuLcNlKAb1R1fpBDMqZcWHI2xhhjQoydczbGGGNCjCVnY4wxJsRYcjbGGGNCjCVnY4wxJsT8P8nb0Zlyju0hAAAAAElFTkSuQmCC \ n ",
      "text / plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.corr () \ n",
    "sns.heatmap (corr, annot = True, cmap = 'coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e877c9d8",
   "metadata": {},
   "source": [
    "# label incoding \ n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "64a058e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder \ n",
    "le = LabelEncoder () \ n",
    "data ['Outlet'] = le.fit_transform (data ['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",
    "data [col] = le.fit_transform (data [col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b208970d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c66f02b5",
   "metadata": {},
   "source": [
    "# one hot encoding #bst \ n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "07f78641",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies (data, columns = ['Item_Fat_Content', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'New_Item_Type']) \ n",
    "# data.head ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "de84b2ce",
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head ()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "308d00b3",
   "metadata": {},
   "source": [
    "# train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "480969b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop (columns = ['Outlet_Establishment_Year', 'Item_Identifier', 'Outlet_Identifier', 'Item_Outlet_Sales']) \ n",
    "y = data ['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10c879a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6dc71f23",
   "metadata": {},
   "source": [
    "#model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "17a03431",
   "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",
    "model.fit (X, y) \ n",
    "pred = model.predict (X) \ n",
    "cv_score = cross_val_score (model, X, y, scoring = 'mean_squared_error') \ n",
    "\ n",
    "print (\" model report \ ") \ n",
    "print (\" mse \ ", mean_squared_error (y, pred)) \ n",
    "print (\" CV score \ ", cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f1f43b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text / plain": [
       "['accuracy', \ n",
       "'adjusted_mutual_info_score', \ n",
       "'adjusted_rand_score', \ n",
       "'average_precision', \ n",
       "'balanced_accuracy', \ n",
       "'completeness_score', \ n",
       "'explained_variance', \ n",
       "'f1', \ n",
       "'f1_macro', \ n",
       "'f1_micro', \ n",
       "'f1_samples', \ n",
       "'f1_weighted', \ n",
       "'fowlkes_mallows_score', \ n",
       "'homogeneity_score', \ n",
       "'jaccard', \ n",
       "'jaccard_macro', \ n",
       "'jaccard_micro', \ n",
       "'jaccard_samples', \ n",
       "'jaccard_weighted', \ n",
       "'max_error', \ n",
       "'mutual_info_score', \ n",
       "'neg_brier_score', \ n",
       "'neg_log_loss', \ n",
       "'neg_mean_absolute_error', \ n",
       "'neg_mean_absolute_percentage_error', \ n",
       "'neg_mean_gamma_deviance', \ n",
       "'neg_mean_poisson_deviance', \ n",
       "'neg_mean_squared_error', \ n",
       "'neg_mean_squared_log_error', \ n",
       "'neg_median_absolute_error', \ n",
       "'neg_root_mean_squared_error', \ n",
       "'normalized_mutual_info_score', \ n",
       "'precision', \ n",
       "'precision_macro', \ n",
       "'precision_micro', \ n",
       "'precision_samples', \ n",
       "'precision_weighted', \ n",
       "'r2', \ n",
       "'rand_score', \ n",
       "'recall', \ n",
       "'recall_macro', \ n",
       "'recall_micro', \ n",
       "'recall_samples', \ n",
       "'recall_weighted', \ n",
       "'roc_auc', \ n",
       "'roc_auc_ovo', \ n",
       "'roc_auc_ovo_weighted', \ n",
       "'roc_auc_ovr', \ n",
       "'roc_auc_ovr_weighted', \ n",
       "'top_k_accuracy', \ n",
       "'v_measure_score']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn \ n",
    "sorted (sklearn.metrics.SCORERS.keys ()) \ n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "20fd4268",
   "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",
    "model.fit (X, y) \ n",
    "pred = model.predict (X) \ n",
    "cv_score = cross_val_score (model, X, y, scoring = 'neg_mean_squared_error', cv = 5) #this change \ n",
    "cv_score = np.abs (np.mean (cv_score)) \ n",
    "print (\" model report \ ") \ n",
    "print (\" mse \ ", mean_squared_error (y, pred)) \ n",
    "print (\" CV score \ ", cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2f9aaeb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model report \ n",
      "mse 0.28800650325017957 \ n",
      "CV score 0.28909740495315817 \ n"
     ]
    },
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: title = {'center': 'Model Coefficients'}>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "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": 63,
   "id": "5bbe0bd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model report \ n",
      "mse 0.42802985605683463 \ n",
      "CV score 0.4289289251562643 \ n"
     ]
    },
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: title = {'center': 'Model Coefficients'}>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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U2Sfu075S0F3DHJNQ1ZiLTeLtwk84QkbQucBbwN2AOqRbxImB5YLeIeHAy6RozkbFd2OGPC5K2J4VkFXBrRFw8mXWNmcg02S7s8I0xpiG4Dd8YYxqCHb4xxjQEO3xjjGkIdvjGGNMQ / j / 8O + Sab9ygpwAAAABJRU5ErkJggg == \ n ",
      "text / plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now copy paste for ridge \ n",
    "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": 64,
   "id": "a47711b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model report \ n",
      "mse 5.5534030638578795e-34 \ n",
      "CV score 0.575878558275867 \ n"
     ]
    },
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: title = {'center': 'feature_importances'}>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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_importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "36657d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model report \ n",
      "mse 0.04217273337840006 \ n",
      "CV score 0.3106383007472001 \ n"
     ]
    },
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: title = {'center': 'feature_importances'}>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "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_importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "577f5a44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model report \ n",
      "mse 1.0418489584965893e-28 \ n",
      "CV score 0.33187131435123474 \ n"
     ]
    },
    {
     "data": {
      "text / plain": [
       "<AxesSubplot: title = {'center': 'feature_importances'}>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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f0I2ZHDHsFIuVdzE6bnfX rbMKl2TiR26MZPjKkkvHF4paT + / + g6ino2zKrdE0mrnIxZkJI2ho4BfgtcDHp6fZRwLrA3hHxo0nqZ2W3mR126MZMGElPIoWiFXBlRJw9TX1bZpWuaY8dujHG9ATXoRtjTE wQzfGmJ5gh26MMT3BDt0YY3rC / wemBvab2PGYLAAAAABJRU5ErkJggg == \ 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_importances')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7fe553b",
   "metadata": {},
   "source": [
    "# we finalize linier regrassion beacuse cv score is 0.2892"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cca5f13b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}