Day 78/005-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big Data Mart Analysis\n",
    "\n",
    "## Business Understanding\n",
    "\n",
    "The Big Data Mart Dataset is used for predicting the future price trends of different items, and how each of the items contribute to the overall sales of the mart. We study the information related to different store outlets, different items present in the store, how the location of the stores influence the sales and what sort of products are sold most and contribute maximum to the overall sales of the Mart.\n",
    "\n",
    "## Data Understanding\n",
    "\n",
    " \n",
    "We can identify the target variable to be the Item_Outlet_Sales, and rest of the features are inputs to the model and are independent variables of the dataset.\n",
    "\n",
    "Based on each feature, we can describe them as follows:\n",
    "\n",
    "Product based features:\n",
    "- Item Identifier: Unique code for each item\n",
    "- Item Weight: The weight of the item selected in data\n",
    "- Item Fat Content: It is the amount of fats present in the item generally consumable edibles\n",
    "- Item Visibility: The amount of how much an item is being sold as a range of 0-1\n",
    "- Item Type: Dairy, Soft Drinks, Meat, Fruits and Vegetables and Household supplies\n",
    "\n",
    "Outlet based features:\n",
    "- Outlet Identifier: It is the unique code assigned to each outlet\n",
    "- Outlet Establishment Year: The founding year of outlet\n",
    "- Outlet Size: Bigger, Medium or Small\n",
    "- Outlet Location: It is based on rural to urban in form of Tiers\n",
    "- Outlet Type: Nature of the shop\n",
    "\n",
    "Target Feature:\n",
    "- Item Outlet Sales: Total sales done in the outlet\n",
    "\n",
    "## Hypotheses\n",
    "\n",
    "\n",
    "1. Cities have higher sales than rural areas, hence Tier 1 locations will have highest sales.\n",
    "2. Bigger stores will have higher sales, owing to convenience factor of finding products at single place.\n",
    "3. Higher Visibilty products will have potentially higher sales\n",
    "4. Higher marketed products and well-advertised products will have higher sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_desc(data):\n",
    "    print(\"Rows: \", data.shape[0])\n",
    "    print(\"Columns: \", data.shape[1])\n",
    "    print(\"First 5 Rows: \", data.head())\n",
    "    print(\"Last 5 Rows: \", data.tail())\n",
    "    print(\"/n Information of frames:/n\")\n",
    "    print(data.info())\n",
    "    print(\"/n 5-Point Summary: /n\")\n",
    "    print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:  8523\n",
      "Columns:  12\n",
      "First 5 Rows:    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  \n",
      "Last 5 Rows:       Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\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",
      "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",
      "8518                       1987        High               Tier 3   \n",
      "8519                       2002         NaN               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",
      "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 Information of frames:/n\n",
      "<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",
      "None\n",
      "/n 5-Point Summary: /n\n",
      "       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  \n"
     ]
    }
   ],
   "source": [
    "data_desc(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:  5681\n",
      "Columns:  11\n",
      "First 5 Rows:    Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
      "0           FDW58       20.750          Low Fat         0.007565  Snack Foods   \n",
      "1           FDW14        8.300              reg         0.038428        Dairy   \n",
      "2           NCN55       14.600          Low Fat         0.099575       Others   \n",
      "3           FDQ58        7.315          Low Fat         0.015388  Snack Foods   \n",
      "4           FDY38          NaN          Regular         0.118599        Dairy   \n",
      "\n",
      "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
      "0  107.8622            OUT049                       1999      Medium   \n",
      "1   87.3198            OUT017                       2007         NaN   \n",
      "2  241.7538            OUT010                       1998         NaN   \n",
      "3  155.0340            OUT017                       2007         NaN   \n",
      "4  234.2300            OUT027                       1985      Medium   \n",
      "\n",
      "  Outlet_Location_Type        Outlet_Type  \n",
      "0               Tier 1  Supermarket Type1  \n",
      "1               Tier 2  Supermarket Type1  \n",
      "2               Tier 3      Grocery Store  \n",
      "3               Tier 2  Supermarket Type1  \n",
      "4               Tier 3  Supermarket Type3  \n",
      "Last 5 Rows:       Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
      "5676           FDB58         10.5          Regular         0.013496   \n",
      "5677           FDD47          7.6          Regular         0.142991   \n",
      "5678           NCO17         10.0          Low Fat         0.073529   \n",
      "5679           FDJ26         15.3          Regular         0.000000   \n",
      "5680           FDU37          9.5          Regular         0.104720   \n",
      "\n",
      "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
      "5676         Snack Foods  141.3154            OUT046   \n",
      "5677       Starchy Foods  169.1448            OUT018   \n",
      "5678  Health and Hygiene  118.7440            OUT045   \n",
      "5679              Canned  214.6218            OUT017   \n",
      "5680              Canned   79.7960            OUT045   \n",
      "\n",
      "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
      "5676                       1997       Small               Tier 1   \n",
      "5677                       2009      Medium               Tier 3   \n",
      "5678                       2002         NaN               Tier 2   \n",
      "5679                       2007         NaN               Tier 2   \n",
      "5680                       2002         NaN               Tier 2   \n",
      "\n",
      "            Outlet_Type  \n",
      "5676  Supermarket Type1  \n",
      "5677  Supermarket Type2  \n",
      "5678  Supermarket Type1  \n",
      "5679  Supermarket Type1  \n",
      "5680  Supermarket Type1  \n",
      "/n Information of frames:/n\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5681 entries, 0 to 5680\n",
      "Data columns (total 11 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            5681 non-null   object \n",
      " 1   Item_Weight                4705 non-null   float64\n",
      " 2   Item_Fat_Content           5681 non-null   object \n",
      " 3   Item_Visibility            5681 non-null   float64\n",
      " 4   Item_Type                  5681 non-null   object \n",
      " 5   Item_MRP                   5681 non-null   float64\n",
      " 6   Outlet_Identifier          5681 non-null   object \n",
      " 7   Outlet_Establishment_Year  5681 non-null   int64  \n",
      " 8   Outlet_Size                4075 non-null   object \n",
      " 9   Outlet_Location_Type       5681 non-null   object \n",
      " 10  Outlet_Type                5681 non-null   object \n",
      "dtypes: float64(3), int64(1), object(7)\n",
      "memory usage: 488.3+ KB\n",
      "None\n",
      "/n 5-Point Summary: /n\n",
      "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year\n",
      "count  4705.000000      5681.000000  5681.000000                5681.000000\n",
      "mean     12.695633         0.065684   141.023273                1997.828903\n",
      "std       4.664849         0.051252    61.809091                   8.372256\n",
      "min       4.555000         0.000000    31.990000                1985.000000\n",
      "25%       8.645000         0.027047    94.412000                1987.000000\n",
      "50%      12.500000         0.054154   141.415400                1999.000000\n",
      "75%      16.700000         0.093463   186.026600                2004.000000\n",
      "max      21.350000         0.323637   266.588400                2009.000000\n"
     ]
    }
   ],
   "source": [
    "data_desc(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_null(data):\n",
    "    print(data.isna().sum())\n",
    "    print(\"Percentage of Null Values: \", data.isna().sum()/len(train)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n",
      "Percentage of Null Values:  Item_Identifier               0.000000\n",
      "Item_Weight                  17.165317\n",
      "Item_Fat_Content              0.000000\n",
      "Item_Visibility               0.000000\n",
      "Item_Type                     0.000000\n",
      "Item_MRP                      0.000000\n",
      "Outlet_Identifier             0.000000\n",
      "Outlet_Establishment_Year     0.000000\n",
      "Outlet_Size                  28.276428\n",
      "Outlet_Location_Type          0.000000\n",
      "Outlet_Type                   0.000000\n",
      "Item_Outlet_Sales             0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "count_null(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Identifier                 0\n",
      "Item_Weight                   976\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                  1606\n",
      "Outlet_Location_Type            0\n",
      "Outlet_Type                     0\n",
      "dtype: int64\n",
      "Percentage of Null Values:  Item_Identifier               0.000000\n",
      "Item_Weight                  11.451367\n",
      "Item_Fat_Content              0.000000\n",
      "Item_Visibility               0.000000\n",
      "Item_Type                     0.000000\n",
      "Item_MRP                      0.000000\n",
      "Outlet_Identifier             0.000000\n",
      "Outlet_Establishment_Year     0.000000\n",
      "Outlet_Size                  18.843130\n",
      "Outlet_Location_Type          0.000000\n",
      "Outlet_Type                   0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "count_null(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style  type=\"text/css\" >\n",
       "    #T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col8 {\n",
       "            background-color:  red;\n",
       "        }    #T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col1 {\n",
       "            background-color:  red;\n",
       "        }    #T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col8 {\n",
       "            background-color:  red;\n",
       "        }    #T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col8 {\n",
       "            background-color:  red;\n",
       "        }</style><table id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Item_Identifier</th>        <th class=\"col_heading level0 col1\" >Item_Weight</th>        <th class=\"col_heading level0 col2\" >Item_Fat_Content</th>        <th class=\"col_heading level0 col3\" >Item_Visibility</th>        <th class=\"col_heading level0 col4\" >Item_Type</th>        <th class=\"col_heading level0 col5\" >Item_MRP</th>        <th class=\"col_heading level0 col6\" >Outlet_Identifier</th>        <th class=\"col_heading level0 col7\" >Outlet_Establishment_Year</th>        <th class=\"col_heading level0 col8\" >Outlet_Size</th>        <th class=\"col_heading level0 col9\" >Outlet_Location_Type</th>        <th class=\"col_heading level0 col10\" >Outlet_Type</th>        <th class=\"col_heading level0 col11\" >Item_Outlet_Sales</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col0\" class=\"data row0 col0\" >FDA15</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col1\" class=\"data row0 col1\" >9.300000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col2\" class=\"data row0 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col3\" class=\"data row0 col3\" >0.016047</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col4\" class=\"data row0 col4\" >Dairy</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col5\" class=\"data row0 col5\" >249.809200</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col6\" class=\"data row0 col6\" >OUT049</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col7\" class=\"data row0 col7\" >1999</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col8\" class=\"data row0 col8\" >Medium</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col9\" class=\"data row0 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col10\" class=\"data row0 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row0_col11\" class=\"data row0 col11\" >3735.138000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col0\" class=\"data row1 col0\" >DRC01</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col1\" class=\"data row1 col1\" >5.920000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col2\" class=\"data row1 col2\" >Regular</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col3\" class=\"data row1 col3\" >0.019278</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col4\" class=\"data row1 col4\" >Soft Drinks</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col5\" class=\"data row1 col5\" >48.269200</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col6\" class=\"data row1 col6\" >OUT018</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col7\" class=\"data row1 col7\" >2009</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col8\" class=\"data row1 col8\" >Medium</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col9\" class=\"data row1 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col10\" class=\"data row1 col10\" >Supermarket Type2</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row1_col11\" class=\"data row1 col11\" >443.422800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col0\" class=\"data row2 col0\" >FDN15</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col1\" class=\"data row2 col1\" >17.500000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col2\" class=\"data row2 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col3\" class=\"data row2 col3\" >0.016760</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col4\" class=\"data row2 col4\" >Meat</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col5\" class=\"data row2 col5\" >141.618000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col6\" class=\"data row2 col6\" >OUT049</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col7\" class=\"data row2 col7\" >1999</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col8\" class=\"data row2 col8\" >Medium</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col9\" class=\"data row2 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col10\" class=\"data row2 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row2_col11\" class=\"data row2 col11\" >2097.270000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col0\" class=\"data row3 col0\" >FDX07</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col1\" class=\"data row3 col1\" >19.200000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col2\" class=\"data row3 col2\" >Regular</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col4\" class=\"data row3 col4\" >Fruits and Vegetables</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col5\" class=\"data row3 col5\" >182.095000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col6\" class=\"data row3 col6\" >OUT010</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col7\" class=\"data row3 col7\" >1998</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col8\" class=\"data row3 col8\" >nan</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col9\" class=\"data row3 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col10\" class=\"data row3 col10\" >Grocery Store</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row3_col11\" class=\"data row3 col11\" >732.380000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col0\" class=\"data row4 col0\" >NCD19</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col1\" class=\"data row4 col1\" >8.930000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col2\" class=\"data row4 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col4\" class=\"data row4 col4\" >Household</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col5\" class=\"data row4 col5\" >53.861400</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col6\" class=\"data row4 col6\" >OUT013</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col7\" class=\"data row4 col7\" >1987</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col8\" class=\"data row4 col8\" >High</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col9\" class=\"data row4 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col10\" class=\"data row4 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row4_col11\" class=\"data row4 col11\" >994.705200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col0\" class=\"data row5 col0\" >FDP36</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col1\" class=\"data row5 col1\" >10.395000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col2\" class=\"data row5 col2\" >Regular</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col4\" class=\"data row5 col4\" >Baking Goods</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col5\" class=\"data row5 col5\" >51.400800</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col6\" class=\"data row5 col6\" >OUT018</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col7\" class=\"data row5 col7\" >2009</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col8\" class=\"data row5 col8\" >Medium</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col9\" class=\"data row5 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col10\" class=\"data row5 col10\" >Supermarket Type2</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row5_col11\" class=\"data row5 col11\" >556.608800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col0\" class=\"data row6 col0\" >FDO10</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col1\" class=\"data row6 col1\" >13.650000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col2\" class=\"data row6 col2\" >Regular</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col3\" class=\"data row6 col3\" >0.012741</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col4\" class=\"data row6 col4\" >Snack Foods</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col5\" class=\"data row6 col5\" >57.658800</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col6\" class=\"data row6 col6\" >OUT013</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col7\" class=\"data row6 col7\" >1987</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col8\" class=\"data row6 col8\" >High</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col9\" class=\"data row6 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col10\" class=\"data row6 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row6_col11\" class=\"data row6 col11\" >343.552800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col0\" class=\"data row7 col0\" >FDP10</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col1\" class=\"data row7 col1\" >nan</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col2\" class=\"data row7 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col3\" class=\"data row7 col3\" >0.127470</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col4\" class=\"data row7 col4\" >Snack Foods</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col5\" class=\"data row7 col5\" >107.762200</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col6\" class=\"data row7 col6\" >OUT027</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col7\" class=\"data row7 col7\" >1985</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col8\" class=\"data row7 col8\" >Medium</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col9\" class=\"data row7 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col10\" class=\"data row7 col10\" >Supermarket Type3</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row7_col11\" class=\"data row7 col11\" >4022.763600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col0\" class=\"data row8 col0\" >FDH17</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col1\" class=\"data row8 col1\" >16.200000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col2\" class=\"data row8 col2\" >Regular</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col3\" class=\"data row8 col3\" >0.016687</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col4\" class=\"data row8 col4\" >Frozen Foods</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col5\" class=\"data row8 col5\" >96.972600</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col6\" class=\"data row8 col6\" >OUT045</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col7\" class=\"data row8 col7\" >2002</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col8\" class=\"data row8 col8\" >nan</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col9\" class=\"data row8 col9\" >Tier 2</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col10\" class=\"data row8 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row8_col11\" class=\"data row8 col11\" >1076.598600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col0\" class=\"data row9 col0\" >FDU28</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col1\" class=\"data row9 col1\" >19.200000</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col2\" class=\"data row9 col2\" >Regular</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col3\" class=\"data row9 col3\" >0.094450</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col4\" class=\"data row9 col4\" >Frozen Foods</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col5\" class=\"data row9 col5\" >187.821400</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col6\" class=\"data row9 col6\" >OUT017</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col7\" class=\"data row9 col7\" >2007</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col8\" class=\"data row9 col8\" >nan</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col9\" class=\"data row9 col9\" >Tier 2</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col10\" class=\"data row9 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_5d9d0950_dcce_11ea_bb0f_edeaf4098461row9_col11\" class=\"data row9 col11\" >4710.535000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7d9d9e3ac0>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clearly Item Weight and Outlet Size has many missing values\n",
    "train.head(10).style.highlight_null()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Identifier\n",
       "OUT027    3694.038558\n",
       "OUT035    2438.841866\n",
       "OUT049    2348.354635\n",
       "OUT017    2340.675263\n",
       "OUT013    2298.995256\n",
       "OUT046    2277.844267\n",
       "OUT045    2192.384798\n",
       "OUT018    1995.498739\n",
       "OUT019     340.329723\n",
       "OUT010     339.351662\n",
       "Name: Item_Outlet_Sales, dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Outlet_Identifier')['Item_Outlet_Sales'].agg('mean').sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlet Sales\n",
    "\n",
    "There are many outliers, in the Item Outlet Sales whose median is 1794.331, and there are many items at much higher price than the upper quadrant. This does not indicate need for removal of points with unusual behaviour as they can be products with higher prices such as electronics. However, we cannot see any such items in the Item Type list and thus it is indicative of many seasonal increase in the prices with a few exceptions. Also, only item outlet sales seems to have outliers and no other columns. \n",
    "\n",
    "The costliest product is a low fat household type item at $13000 approximately bought from the Supermarket\n",
    "The minimalist product cost is a low fat household item itself bought from the grocery store and a soft drink also bought from the grocery store.\n",
    "Hence, we cannot really comment directly based on item type and fat content whether it is priced higher or lower!\n",
    "\n",
    "The Item Outlet Sales is a one-tailed skewed distribution, which is indicating that the prices are more dense towards the left and has positive skewness with 1.177 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1794.3310000000001"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Outlet_Sales.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7d9ca1b280>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=train, kind='box', x='Item_Outlet_Sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7d9d9a7400>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.catplot(data=train, kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col5 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col1 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col7 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col1 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col3 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col3 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col3 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col7 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col11 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col3 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col7 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col1 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col11 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col3 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col5 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col7 {\n",
       "            background-color:  red;\n",
       "            : ;\n",
       "        }    #T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col7 {\n",
       "            : ;\n",
       "            background-color:  green;\n",
       "        }</style><table id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Item_Identifier</th>        <th class=\"col_heading level0 col1\" >Item_Weight</th>        <th class=\"col_heading level0 col2\" >Item_Fat_Content</th>        <th class=\"col_heading level0 col3\" >Item_Visibility</th>        <th class=\"col_heading level0 col4\" >Item_Type</th>        <th class=\"col_heading level0 col5\" >Item_MRP</th>        <th class=\"col_heading level0 col6\" >Outlet_Identifier</th>        <th class=\"col_heading level0 col7\" >Outlet_Establishment_Year</th>        <th class=\"col_heading level0 col8\" >Outlet_Size</th>        <th class=\"col_heading level0 col9\" >Outlet_Location_Type</th>        <th class=\"col_heading level0 col10\" >Outlet_Type</th>        <th class=\"col_heading level0 col11\" >Item_Outlet_Sales</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col0\" class=\"data row0 col0\" >FDA15</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col1\" class=\"data row0 col1\" >9.300000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col2\" class=\"data row0 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col3\" class=\"data row0 col3\" >0.016047</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col4\" class=\"data row0 col4\" >Dairy</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col5\" class=\"data row0 col5\" >249.809200</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col6\" class=\"data row0 col6\" >OUT049</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col7\" class=\"data row0 col7\" >1999</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col8\" class=\"data row0 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col9\" class=\"data row0 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col10\" class=\"data row0 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row0_col11\" class=\"data row0 col11\" >3735.138000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col0\" class=\"data row1 col0\" >DRC01</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col1\" class=\"data row1 col1\" >5.920000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col2\" class=\"data row1 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col3\" class=\"data row1 col3\" >0.019278</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col4\" class=\"data row1 col4\" >Soft Drinks</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col5\" class=\"data row1 col5\" >48.269200</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col6\" class=\"data row1 col6\" >OUT018</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col7\" class=\"data row1 col7\" >2009</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col8\" class=\"data row1 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col9\" class=\"data row1 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col10\" class=\"data row1 col10\" >Supermarket Type2</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row1_col11\" class=\"data row1 col11\" >443.422800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col0\" class=\"data row2 col0\" >FDN15</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col1\" class=\"data row2 col1\" >17.500000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col2\" class=\"data row2 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col3\" class=\"data row2 col3\" >0.016760</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col4\" class=\"data row2 col4\" >Meat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col5\" class=\"data row2 col5\" >141.618000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col6\" class=\"data row2 col6\" >OUT049</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col7\" class=\"data row2 col7\" >1999</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col8\" class=\"data row2 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col9\" class=\"data row2 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col10\" class=\"data row2 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row2_col11\" class=\"data row2 col11\" >2097.270000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col0\" class=\"data row3 col0\" >FDX07</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col1\" class=\"data row3 col1\" >19.200000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col2\" class=\"data row3 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col4\" class=\"data row3 col4\" >Fruits and Vegetables</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col5\" class=\"data row3 col5\" >182.095000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col6\" class=\"data row3 col6\" >OUT010</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col7\" class=\"data row3 col7\" >1998</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col8\" class=\"data row3 col8\" >nan</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col9\" class=\"data row3 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col10\" class=\"data row3 col10\" >Grocery Store</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row3_col11\" class=\"data row3 col11\" >732.380000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col0\" class=\"data row4 col0\" >NCD19</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col1\" class=\"data row4 col1\" >8.930000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col2\" class=\"data row4 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col4\" class=\"data row4 col4\" >Household</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col5\" class=\"data row4 col5\" >53.861400</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col6\" class=\"data row4 col6\" >OUT013</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col7\" class=\"data row4 col7\" >1987</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col8\" class=\"data row4 col8\" >High</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col9\" class=\"data row4 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col10\" class=\"data row4 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row4_col11\" class=\"data row4 col11\" >994.705200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col0\" class=\"data row5 col0\" >FDP36</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col1\" class=\"data row5 col1\" >10.395000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col2\" class=\"data row5 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col4\" class=\"data row5 col4\" >Baking Goods</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col5\" class=\"data row5 col5\" >51.400800</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col6\" class=\"data row5 col6\" >OUT018</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col7\" class=\"data row5 col7\" >2009</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col8\" class=\"data row5 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col9\" class=\"data row5 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col10\" class=\"data row5 col10\" >Supermarket Type2</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row5_col11\" class=\"data row5 col11\" >556.608800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col0\" class=\"data row6 col0\" >FDO10</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col1\" class=\"data row6 col1\" >13.650000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col2\" class=\"data row6 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col3\" class=\"data row6 col3\" >0.012741</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col4\" class=\"data row6 col4\" >Snack Foods</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col5\" class=\"data row6 col5\" >57.658800</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col6\" class=\"data row6 col6\" >OUT013</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col7\" class=\"data row6 col7\" >1987</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col8\" class=\"data row6 col8\" >High</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col9\" class=\"data row6 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col10\" class=\"data row6 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row6_col11\" class=\"data row6 col11\" >343.552800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col0\" class=\"data row7 col0\" >FDP10</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col1\" class=\"data row7 col1\" >nan</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col2\" class=\"data row7 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col3\" class=\"data row7 col3\" >0.127470</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col4\" class=\"data row7 col4\" >Snack Foods</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col5\" class=\"data row7 col5\" >107.762200</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col6\" class=\"data row7 col6\" >OUT027</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col7\" class=\"data row7 col7\" >1985</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col8\" class=\"data row7 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col9\" class=\"data row7 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col10\" class=\"data row7 col10\" >Supermarket Type3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row7_col11\" class=\"data row7 col11\" >4022.763600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col0\" class=\"data row8 col0\" >FDH17</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col1\" class=\"data row8 col1\" >16.200000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col2\" class=\"data row8 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col3\" class=\"data row8 col3\" >0.016687</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col4\" class=\"data row8 col4\" >Frozen Foods</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col5\" class=\"data row8 col5\" >96.972600</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col6\" class=\"data row8 col6\" >OUT045</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col7\" class=\"data row8 col7\" >2002</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col8\" class=\"data row8 col8\" >nan</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col9\" class=\"data row8 col9\" >Tier 2</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col10\" class=\"data row8 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row8_col11\" class=\"data row8 col11\" >1076.598600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col0\" class=\"data row9 col0\" >FDU28</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col1\" class=\"data row9 col1\" >19.200000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col2\" class=\"data row9 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col3\" class=\"data row9 col3\" >0.094450</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col4\" class=\"data row9 col4\" >Frozen Foods</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col5\" class=\"data row9 col5\" >187.821400</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col6\" class=\"data row9 col6\" >OUT017</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col7\" class=\"data row9 col7\" >2007</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col8\" class=\"data row9 col8\" >nan</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col9\" class=\"data row9 col9\" >Tier 2</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col10\" class=\"data row9 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row9_col11\" class=\"data row9 col11\" >4710.535000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col0\" class=\"data row10 col0\" >FDY07</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col1\" class=\"data row10 col1\" >11.800000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col2\" class=\"data row10 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col3\" class=\"data row10 col3\" >0.000000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col4\" class=\"data row10 col4\" >Fruits and Vegetables</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col5\" class=\"data row10 col5\" >45.540200</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col6\" class=\"data row10 col6\" >OUT049</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col7\" class=\"data row10 col7\" >1999</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col8\" class=\"data row10 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col9\" class=\"data row10 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col10\" class=\"data row10 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row10_col11\" class=\"data row10 col11\" >1516.026600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col0\" class=\"data row11 col0\" >FDA03</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col1\" class=\"data row11 col1\" >18.500000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col2\" class=\"data row11 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col3\" class=\"data row11 col3\" >0.045464</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col4\" class=\"data row11 col4\" >Dairy</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col5\" class=\"data row11 col5\" >144.110200</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col6\" class=\"data row11 col6\" >OUT046</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col7\" class=\"data row11 col7\" >1997</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col8\" class=\"data row11 col8\" >Small</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col9\" class=\"data row11 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col10\" class=\"data row11 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row11_col11\" class=\"data row11 col11\" >2187.153000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col0\" class=\"data row12 col0\" >FDX32</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col1\" class=\"data row12 col1\" >15.100000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col2\" class=\"data row12 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col3\" class=\"data row12 col3\" >0.100014</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col4\" class=\"data row12 col4\" >Fruits and Vegetables</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col5\" class=\"data row12 col5\" >145.478600</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col6\" class=\"data row12 col6\" >OUT049</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col7\" class=\"data row12 col7\" >1999</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col8\" class=\"data row12 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col9\" class=\"data row12 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col10\" class=\"data row12 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row12_col11\" class=\"data row12 col11\" >1589.264600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col0\" class=\"data row13 col0\" >FDS46</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col1\" class=\"data row13 col1\" >17.600000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col2\" class=\"data row13 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col3\" class=\"data row13 col3\" >0.047257</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col4\" class=\"data row13 col4\" >Snack Foods</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col5\" class=\"data row13 col5\" >119.678200</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col6\" class=\"data row13 col6\" >OUT046</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col7\" class=\"data row13 col7\" >1997</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col8\" class=\"data row13 col8\" >Small</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col9\" class=\"data row13 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col10\" class=\"data row13 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row13_col11\" class=\"data row13 col11\" >2145.207600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col0\" class=\"data row14 col0\" >FDF32</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col1\" class=\"data row14 col1\" >16.350000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col2\" class=\"data row14 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col3\" class=\"data row14 col3\" >0.068024</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col4\" class=\"data row14 col4\" >Fruits and Vegetables</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col5\" class=\"data row14 col5\" >196.442600</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col6\" class=\"data row14 col6\" >OUT013</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col7\" class=\"data row14 col7\" >1987</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col8\" class=\"data row14 col8\" >High</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col9\" class=\"data row14 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col10\" class=\"data row14 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row14_col11\" class=\"data row14 col11\" >1977.426000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col0\" class=\"data row15 col0\" >FDP49</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col1\" class=\"data row15 col1\" >9.000000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col2\" class=\"data row15 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col3\" class=\"data row15 col3\" >0.069089</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col4\" class=\"data row15 col4\" >Breakfast</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col5\" class=\"data row15 col5\" >56.361400</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col6\" class=\"data row15 col6\" >OUT046</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col7\" class=\"data row15 col7\" >1997</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col8\" class=\"data row15 col8\" >Small</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col9\" class=\"data row15 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col10\" class=\"data row15 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row15_col11\" class=\"data row15 col11\" >1547.319200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col0\" class=\"data row16 col0\" >NCB42</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col1\" class=\"data row16 col1\" >11.800000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col2\" class=\"data row16 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col3\" class=\"data row16 col3\" >0.008596</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col4\" class=\"data row16 col4\" >Health and Hygiene</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col5\" class=\"data row16 col5\" >115.349200</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col6\" class=\"data row16 col6\" >OUT018</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col7\" class=\"data row16 col7\" >2009</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col8\" class=\"data row16 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col9\" class=\"data row16 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col10\" class=\"data row16 col10\" >Supermarket Type2</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row16_col11\" class=\"data row16 col11\" >1621.888800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col0\" class=\"data row17 col0\" >FDP49</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col1\" class=\"data row17 col1\" >9.000000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col2\" class=\"data row17 col2\" >Regular</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col3\" class=\"data row17 col3\" >0.069196</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col4\" class=\"data row17 col4\" >Breakfast</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col5\" class=\"data row17 col5\" >54.361400</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col6\" class=\"data row17 col6\" >OUT049</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col7\" class=\"data row17 col7\" >1999</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col8\" class=\"data row17 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col9\" class=\"data row17 col9\" >Tier 1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col10\" class=\"data row17 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row17_col11\" class=\"data row17 col11\" >718.398200</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col0\" class=\"data row18 col0\" >DRI11</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col1\" class=\"data row18 col1\" >nan</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col2\" class=\"data row18 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col3\" class=\"data row18 col3\" >0.034238</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col4\" class=\"data row18 col4\" >Hard Drinks</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col5\" class=\"data row18 col5\" >113.283400</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col6\" class=\"data row18 col6\" >OUT027</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col7\" class=\"data row18 col7\" >1985</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col8\" class=\"data row18 col8\" >Medium</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col9\" class=\"data row18 col9\" >Tier 3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col10\" class=\"data row18 col10\" >Supermarket Type3</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row18_col11\" class=\"data row18 col11\" >2303.668000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col0\" class=\"data row19 col0\" >FDU02</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col1\" class=\"data row19 col1\" >13.350000</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col2\" class=\"data row19 col2\" >Low Fat</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col3\" class=\"data row19 col3\" >0.102492</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col4\" class=\"data row19 col4\" >Dairy</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col5\" class=\"data row19 col5\" >230.535200</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col6\" class=\"data row19 col6\" >OUT035</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col7\" class=\"data row19 col7\" >2004</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col8\" class=\"data row19 col8\" >Small</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col9\" class=\"data row19 col9\" >Tier 2</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col10\" class=\"data row19 col10\" >Supermarket Type1</td>\n",
       "                        <td id=\"T_6170a028_dcce_11ea_bb0f_edeaf4098461row19_col11\" class=\"data row19 col11\" >2748.422400</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7d9b0a2b20>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(20).style.highlight_max(color='red').highlight_min(color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>7188</th>\n",
       "      <td>NCE42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.010551</td>\n",
       "      <td>Household</td>\n",
       "      <td>234.9958</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>13086.9648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "7188           NCE42          NaN          Low Fat         0.010551   \n",
       "\n",
       "      Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "7188  Household  234.9958            OUT027                       1985   \n",
       "\n",
       "     Outlet_Size Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "7188      Medium               Tier 3  Supermarket Type3         13086.9648  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['Item_Outlet_Sales'] == train['Item_Outlet_Sales'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>906</th>\n",
       "      <td>DRK12</td>\n",
       "      <td>9.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>32.89</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>33.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6950</th>\n",
       "      <td>NCR42</td>\n",
       "      <td>9.105</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.064411</td>\n",
       "      <td>Household</td>\n",
       "      <td>33.39</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>33.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "906            DRK12        9.500          Low Fat         0.000000   \n",
       "6950           NCR42        9.105          Low Fat         0.064411   \n",
       "\n",
       "        Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "906   Soft Drinks     32.89            OUT010                       1998   \n",
       "6950    Household     33.39            OUT010                       1998   \n",
       "\n",
       "     Outlet_Size Outlet_Location_Type    Outlet_Type  Item_Outlet_Sales  \n",
       "906          NaN               Tier 3  Grocery Store              33.29  \n",
       "6950         NaN               Tier 3  Grocery Store              33.29  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['Item_Outlet_Sales'] == train['Item_Outlet_Sales'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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_Fat_Content</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8523</td>\n",
       "      <td>8523</td>\n",
       "      <td>8523</td>\n",
       "      <td>8523</td>\n",
       "      <td>6113</td>\n",
       "      <td>8523</td>\n",
       "      <td>8523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1559</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>FDW13</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10</td>\n",
       "      <td>5089</td>\n",
       "      <td>1232</td>\n",
       "      <td>935</td>\n",
       "      <td>2793</td>\n",
       "      <td>3350</td>\n",
       "      <td>5577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Identifier Item_Fat_Content              Item_Type  \\\n",
       "count             8523             8523                   8523   \n",
       "unique            1559                5                     16   \n",
       "top              FDW13          Low Fat  Fruits and Vegetables   \n",
       "freq                10             5089                   1232   \n",
       "\n",
       "       Outlet_Identifier Outlet_Size Outlet_Location_Type        Outlet_Type  \n",
       "count               8523        6113                 8523               8523  \n",
       "unique                10           3                    3                  4  \n",
       "top               OUT027      Medium               Tier 3  Supermarket Type1  \n",
       "freq                 935        2793                 3350               5577  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe(exclude='number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.177531\n"
     ]
    },
    {
     "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(train['Item_Outlet_Sales'])\n",
    "print('Skewness: %f' % train['Item_Outlet_Sales'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d995cdb50>"
      ]
     },
     "execution_count": 293,
     "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": [
    "Q1 = train['Item_Outlet_Sales'].quantile(0.25)\n",
    "Q3 = train['Item_Outlet_Sales'].quantile(0.75)\n",
    "interquantile_range = Q3 - Q1\n",
    "low = Q1 - 1.5*interquantile_range\n",
    "high = Q3 + 1.5*interquantile_range\n",
    "\n",
    "new_train = train.copy()\n",
    "new_train['Item_Outlet_Sales']=np.where(new_train['Item_Outlet_Sales'] > high, high, new_train['Item_Outlet_Sales'])\n",
    "\n",
    "new_train['Item_Outlet_Sales']=np.where(new_train['Item_Outlet_Sales'] < low, low, new_train['Item_Outlet_Sales'])\n",
    "\n",
    "sns.boxplot(y=new_train[\"Item_Outlet_Sales\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Low:  -2566.3261\n",
      "High:  6501.8699\n"
     ]
    }
   ],
   "source": [
    "print(\"Low: \", low)\n",
    "print(\"High: \", high)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fruits and Vegetables, and snacks as well as household items are the most frequently bought items, which indicates a correct trend that daily needs have higher consumption and sales than other items such as soft drinks and hard drinks which are a luxury based item.\n",
    "\n",
    "- Starchy Food generated maximum sales, and seafood closely follows.\n",
    "- The lower priced items have more sales\n",
    "- In terms of visibility, the breakfast, seafood and dairy types of food are maximum\n",
    "- Tier 2 cities have maximum sales, though the MRP remains the same so we can rule out any malpractice in selling\n",
    "- Medium sized outlets have highest sales\n",
    "- 1985 and 2004 had maximum sales\n",
    "- Fat Content does not seem likely to affect the Item Outlet Sales, but when combined we have low fat products much higher sold than the regular fat content\n",
    "- Lesser visibility relates to higher prices, and higher visibility are products with lower prices that are sold more.\n",
    "- Weight does not seem to have a much of an effect on Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Item Type')"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "items = train['Item_Type'].value_counts()\n",
    "sns.barplot(x=items,y=items.index,palette='coolwarm')\n",
    "plt.title(\"Frequent Bought Products\")\n",
    "plt.xlabel(\"Item Frequency\")\n",
    "plt.ylabel(\"Item Type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9ac13610>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JVKRpxmmwR70/YaE9lF7hME5gDFvfDblZu4xyH4PlLPRqn1m569i9BbPZ42AYQ9WT1u3cvX94a9mSau8NmGRvwaFgNh1GuY/BKCcUBt9g1z3L6555ZZMyNDRG6C04FMymh3sMI6gyFKps/PtdqrqQxtqhYDa73GOYEkWDo6xG2I25WXtVGgySLpP0sKStubIVkq6XdHf6fnAql6SLJG2TtEXScVXWrWpN6S30M6jh9xCSWbtV3WP4B+ClXWXnATdExFrghvQe4GXA2vS1Abi44rpNlXFPQpd9x7NDwWz2VRoMEfGvzJ9P6VTg8vT6cuAVufJPRuY7wHJJq6qsX1VGuQppmnoLDgWzdqjjHMOhEfFAev0gcGh6vRr4UW69+1PZTKjivoXuxrf7fa/ewkKGkMatl5lNl1pPPkdEMMbjQSVtkLRJ0qZHduyooGbNUvW9DNlnlHNewaFgNv3qCIaH5oaI0veHU/l2YE1uvcNS2TwRsTEi1kfE+kNWrqy0snl1PBGtXyiU2Vso82SzmU2/OoLhavY/9Ocs4Kpc+evS1UknADtzQ05To6lTXUwiFNxbMJsNld7gJunTwInASkn3A+8G3gdcKelssum8X51WvxY4GdgG7AbeUGXdhlm2ZFHtjXyZvYUi+5m33KFg1kqVBkNEnNFn0Yt7rBvAOVXWpwzLly4q9XGZy5Ysnndl0qBzCmUNITkUzKyf1k+JsWzp4n3PZCijlzDOPoqeXB6nAR5rG4eCWat5Sowx1HMSevhf/d31Gue8gkPBzBwMAzSl4WtaKCxbsqgxx8bMyudgGNOovYZxG9JJhMKypYt9n4KZ7eNgqMC4l4YWWb+KUFhIfcxs9jgYugy76idvUK9hUAM9bFqKXuv0KptUKHjoyKxdWn9V0kKNe/nqKA1tr3WLhEJZ5xPMrF0cDAWMexlrWZe/5pVxn0KRUHAgmLVXK4eS5g3TLHAuoHGGlIYpMnTUb/8OBTNbiFYGwzDj3Ck8bH9Ft++3rkPBzCbFQ0klKXKuYZxGt4xZUh0IZjYKB0ODuZdgZnVwMDRQv3MWDgUzmwQHQ0nKmnG1rAfsOBTMbFwOhoIGXXZaRiiM0ksYWO77E8xsgRwMPXSHQJNCwTetmVnVWhkM8xr+XXv6rNlflUNH4FAws/r4PoYuRXoLZT7BrReHgpnVqZU9hqK6Q6FfIBSZ9qKqRtmhYGZla32PIT+MlG/gi4TCzt17C8+FVHS9UXoLDgUzq0LrgmGcSe26Q2GUQFjoZ/fjUDCzqngoKenXW8iHQq+GfVhj390w79y9t6Ps0V17R56Ez6FgZlVqXY8hr9fVSEVDoWivodd6ZfYc+nEomNm43GPoo18o9O41zA+YZUsWd62zd2KNtUPBzBaiVcHQ7y/1ufJBQ0Xz/+offO/D3PJ8QJQRDgt9doSZ2TCtHUoadFPbXG+hX+9gWCh0r9+7vJpLXN1bMLOFalWPoYheobC/17Cn57q95E8o79y9Z1/PYSG9hmG9BYeCmZWhtT2GOYOGkTqX7w+FR3ftHXr3c9V3R3dzKJhZWWoLBkn3SrpV0mZJm1LZCknXS7o7fT+4jroNCotRbnTrPIE9+nxMZmZ1qLvH8NsRcUxErE/vzwNuiIi1wA3pfSmKjOn3n/Jiz7zlc2HQPeRUdBqNUQ0aRnJvwczKVHcwdDsVuDy9vhx4RRUfMnfiedgwUrasdygM3H8J9ykUbewdCmZWtjqDIYAvS7pZ0oZUdmhEPJBePwgc2mtDSRskbZK06ZEdOyqp3CRuQuvmRt7MmqDOq5JeEBHbJT0NuF7SnfmFERGSoteGEbER2Ahw9LHH9VxnXJPoDYyq3zCSg8TMqlBbjyEitqfvDwNfBI4HHpK0CiB9f7iu+tWpSIPvUDCzqtQSDJKWSnrq3GvgJGArcDVwVlrtLOCqOupXpbkGfe773P0Oo0yt7VAwsyrVNZR0KPBFSXN1+FREfEnSd4ErJZ0N3Ae8uqb69bVsyaKRhpP2N/yeysLMpkMtwRAR9wBH9yh/BHjx5GtUrqJ/0feabju/rXsLZlaHpl2uWruFjO8vW7KoY1l3b6F7GGmUzzQzm5RWB8OgBnl/I5416t1/3c+FQP4rb1gouLdgZk3VymDobnSHNbj5cBj0tLXudYqcV+jXi+i3nplZ1Vozu2qRk8bLly7i0V17962b32bZksX77oIeFg5z6+c/O/991CuRHApmNkmt7DH00q/R7hjeWbJ4aC8gv05+iGlQKAwbQjIzm6TW9Bj6GdST6NVzyMqLDxHlX/u8gplNg9YGw7Kli+c9xW0uAOaGlPJl3Q10rzAZdLXRsJ5Czzo6FMysBq0Khn69g85zCb3DAejqNQy/ogk6ewmDQsHnFcysKVoVDN3yvYZ+4QDMC4giel3e2uv1XD3MzJqidcEw73zBkHCA+Y18v4fvDDuH0P3es6aaWRO1LhhgeDgA884r5Ncvdrnq4LubHQpm1lStDIZeuk9Gz78SabwGu9d2vvrIzJqstcHQ60R0r3CYM8qMqqNMoT1ofTOzOrQ2GKB/OAA9L2Ud+3MGnFx2KJhZ07Q6GGDAJay5xrw7JArtd8iVRg4EM2uq1gcD9L5PoWN5iZeTOhDMrOkcDDnDAqKMfZuZNZ2DoYeyAsJhYGbTyMEwgBt2M2sjT7ttZmYdHAxmZtbBwWBmZh0cDGZm1sHBYGZmHRwMZmbWwcFgZmYdHAxmZtahccEg6aWS7pK0TdJ5ddfHzKxtGhUMkg4A/hZ4GbAOOEPSunprZWbWLo0KBuB4YFtE3BMRe4HPAKfWXCczs1ZpWjCsBn6Ue39/KusgaYOkTZI2PbJjx8QqZ2bWBlM5iV5EbAQ2Akj68eoVS+8radcrgWlLmmmsM0xnvV3nyZjGOsP01ftX+y1oWjBsB9bk3h+WyvqKiF8p68MlbYqI9WXtbxKmsc4wnfV2nSdjGusM01vvXpo2lPRdYK2kIyUtAk4Hrq65TmZmrdKoHkNEPC7prcB1wAHAZRFxW83VMjNrlUYFA0BEXAtcW9PHb6zpcxdiGusM01lv13kyprHOML31nkcRUXcdzMysQZp2jsHMzGrmYDAzsw4OhqRJczRJWiPpa5Jul3SbpLel8hWSrpd0d/p+cCqXpItS3bdIOi63r7PS+ndLOmsCdT9A0vckXZPeHynpxlS3f0xXmyFpcXq/LS0/IreP81P5XZJeUnF9l0v6nKQ7Jd0h6blNP86S3pH+XWyV9GlJBzXxOEu6TNLDkrbmyko7tpKeI+nWtM1FklRRnT+Q/n1skfRFSctzy3oew37tSb/fU+NEROu/yK6A+gFwFLAI+D6wrsb6rAKOS6+fCvw72dxRfwWcl8rPA96fXp8M/Asg4ATgxlS+ArgnfT84vT644rr/EfAp4Jr0/krg9PT6Y8Bb0us/BD6WXp8O/GN6vS4d/8XAken3ckCF9b0c+IP0ehGwvMnHmWwmgB8CT84d39c38TgD/w04DtiaKyvt2AI3pXWVtn1ZRXU+CTgwvX5/rs49jyED2pN+v6emfdVegSZ8Ac8Frsu9Px84v+565epzFfC7wF3AqlS2Crgrvf44cEZu/bvS8jOAj+fKO9aroJ6HATcALwKuSf9hd+T+U+07zmSXJD83vT4wrafuY59fr4L6LiNrZNVV3tjjzP5pY1ak43YN8JKmHmfgiK5GtpRjm5bdmSvvWK/MOncteyVwRXrd8xjSpz0Z9P+haV8eSsoUmqOpDqnrfyxwI3BoRDyQFj0IHJpe96v/pH+uvwb+J/CL9P4Q4NGIeLzH5++rW1q+M60/yTofCfwY+Ps0/PUJSUtp8HGOiO3AB4H/AzxAdtxuptnHOa+sY7s6ve4ur9obyXonDKlbr/JB/x8axcHQYJKeAnweeHtEPJZfFtmfHI251ljSy4GHI+LmuusyggPJhg0ujohjgV1kwxv7NPA4H0w24/CRwNOBpcBLa63UmJp2bIeRdAHwOHBF3XWpmoMhM/IcTVWT9CSyULgiIr6Qih+StCotXwU8nMr71X+SP9fzgVMk3Us2XfqLgI8CyyXN3UiZ//x9dUvLlwGPTLjO9wP3R8SN6f3nyIKiycf5d4AfRsSPI+LnwBfIjn2Tj3NeWcd2e3rdXV4JSa8HXg6cmQKNIXXrVf4I/X9PjeJgyDRqjqZ0dcWlwB0R8eHcoquBuasyziI79zBX/rp0ZccJwM7UXb8OOEnSwekvzZNSWeki4vyIOCwijiA7fl+NiDOBrwGv6lPnuZ/lVWn9SOWnp6tpjgTWkp1krKLODwI/kvSfU9GLgdtp8HEmG0I6QdKS9O9krs6NPc5dSjm2adljkk5Ix+F1uX2VStJLyYZIT4mI3V0/S69j2LM9Sce93++pWeo+ydGUL7KrIv6d7GqCC2quywvIuthbgM3p62SyMcobgLuBrwAr0voie/LdD4BbgfW5fb0R2Ja+3jCh+p/I/quSjiL7z7IN+CywOJUflN5vS8uPym1/QfpZ7qKEK02G1PUYYFM61v9EduVLo48z8B7gTmAr8L/Iropp3HEGPk12HuTnZL2zs8s8tsD6dAx+APwNXRcRlFjnbWTnDOb+L35s2DGkT3vS7/fUtC9PiWFmZh08lGRmZh0cDGZm1sHBYGZmHRwMZmbWwcFgZmYdHAxmZtbBwWBTRdLP0vcjJL1mAp+3IU25fKekmyS9oMA2J0p6Xu79hZL+eMg2r5C0bsg6J6QpmzcrmyL8wgL1uGZYfc26ORhsWh0BVBoMaf6nNwEviIhfB94MfErSfxqy6YnA84as0+0VZNM4D3I5sCEijgF+k2wKZ7PSORhsWr0PeGH66/kdyh4Q9AFJ300PVHkT7Pur+RuSrpJ0j6T3SToz/fV/q6RnDPiMPwX+JCJ2AETELWSN8zlp3/dKWpler5f09TQb7puBd6S6vTC/Q0nPkPQlSTdL+jdJv556F6cAH0jb9KvT08juyiUinoiI29M+j5f07TRD7LdyU3zkP3epsof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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train[\"Item_Outlet_Sales\"], train[\"Item_MRP\"], color=\"steelblue\", shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'item cost')"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "sns.barplot(x=train.Item_Type, y=train.Item_Outlet_Sales, data=train)\n",
    "plt.xlabel('item type')\n",
    "plt.ylabel('item cost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.barplot(x=train.Item_Visibility, y=train.Item_Type, data=train, palette='viridis')\n",
    "plt.xlabel('Item Visibility')\n",
    "plt.ylabel('Item Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Establishment_Year\n",
       "1985    2483.677474\n",
       "2004    2438.841866\n",
       "1999    2348.354635\n",
       "2007    2340.675263\n",
       "1987    2298.995256\n",
       "1997    2277.844267\n",
       "2002    2192.384798\n",
       "2009    1995.498739\n",
       "1998     339.351662\n",
       "Name: Item_Outlet_Sales, dtype: float64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Outlet_Establishment_Year')['Item_Outlet_Sales'].agg('mean').sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7d9a6c0a60>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"Item_MRP\", y=\"Item_Outlet_Sales\", data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7d9a833c10>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(\"Outlet_Location_Type\", data=train, kind=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9a40f760>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w7xCy52Hcn06pvbvXabWjpnE4ql3voTUsVq1b3zMsus3Xenpu93X0ni/PKbdVnm464TEd3zrErPaK9DBKv+6hDno1dO3OiGoOgE4Ndesv/lXr1m/R22isM8+v3Ty/jPP2NHqtp1+j1tMYlbqa1UWRK70fJrv+4Smywe7GayzlGdju9qu+3QV9nXok/Z6aOxFFejiD4l6GWb0VudL7Q8CvyK6y/uf0urqietVOa+8i71XXVYRGke31UnZo1C2EuhmluprVQZFDUicDz4+IJ6qqzDC1G7/o1PhuGQKbv2891NF6qKiMw1NlHk7xQLCZ5VFk0PsXQOvzMCaVToPXecrMzEZdzx6GpD9Nkw8CN0n6Zza/0vvctguOiW6Nf6/P+vnFXkbvIc/gd1XbNrPxleeQ1E7p73+m15T0si76bXjdYJtZXfUMjIj4ZGuZpK2AHSNidSW1GhHTd5hSy8NP/fQuYPhhNYirvc2sf0XOkvq6pJ0lTQPuAZZJOq26qtXDsBvRdqqoUx2/p5nVS5FB7/1Tj+Jo4Fpgb7L7RE0a7c5sam1op+8wZYuy1l/87dbTbl2DUuZ2+z4U596FWe0VCYxt063KjwYWRUTjAr6x0K7B6nRoZ8sAmNI2KAptP8eyebZR5HDUROvcbn19LTeksHCvyqyYIoFxIfAQMA24WdJzgbEbw+jUiDTKG72DPA3zjGlTcvUu8oZFnu3lUXZQNNbZ13LuWZiNjNwX7kXE+cD5TUUPS3pt+VWqh+YB7U6D240GuvlCvk6NdpVB0W27/a6viFEMC/cuzIrLcx3GuyLi75uux2g10tdh9DrTaca0KZsCoTFvo/FvXMjXrbHuJyiKNGYOij6277Aw60ueHsa09HenrnONgenTpmYP89kUDFv2MjYv2zw4Nq2nw6B2Yz1FyjsZxaAAh4XZKMtzHcaF6e8W12NMFo1eRnNoAFsERyfdGqkqehNF1zuI9TkozEZfnkNS53f7PCI+XF51hq9TL6M1NKD6q7n7uQCvjIax1DOnhhAUDgezauQ5JHVH5bUYsk7jGN1CoyHPld5lnuHU7/rLXC7XuisMCgeC2XDkOSR1afN7STtnxbGmsloNWaOXsVlZU2gAmw2E92PcAmLTNioKCoeE2fDlPq1W0nzgErLBb0laCZwYEWPRA+nVy2idbm7wez20aFj3dhpUI+vehNnkUOQBShcDH4yIfwWQ9GqyAHlpFRUbhs3CoamX0W7cojlchtFbqHpdXbdT8biEQ8KsnooExsZGWABExI8kPV1BnWqjNTSg89PpOo1lVNX4Vd2oDnKw2gFhNhrynCU1L03+UNKFwBVk95D6X8BN1VVtOFoPTbWOZ7TrYTSXl1mPqvgUVzPrR54exl+3vD+rabrrzQclXQy8BVgRES9OZQcAFwA7kt2b6riIWC1pCtn9quYDzwAnR8RNaZmbgJnAf6dVHxYRK3LUvS/tQgNoGxwNeZ+LMejGctjh0OCQMBt9ec6Smsj9ov4O+Bvgsqayi4CPRMQPJZ0InAZ8HPijtL2XSNoduFbSyyLimbTccRGxeAJ1KaTdIHi74Gief5jqEgzNhr1PzKxcRcYwkHQE8CJgu0ZZRHyq0/wRcbOkvVqK9wNuTtPXA9eRBcb+wI1puRXpLKz5wG1F6limjmdONTXO7cKj9HrUMAzacUCYjbcip9VeAOwAvJasl/AH9NeYLwWOAv4ROAaYk8p/Chwp6YpUdlD629jGJZI2AlcCfxERbQ+HSVoALACYNXtOu1kK6XVzwlFpzMvkYDCbnIr0MF4ZES+VdFdEfFLSX5M9ea+oE4HzJX0cWAQ0WuOLgRcCi4GHgVuAjemz4yLiUUk7kQXG8Wx+mGuTiFgILAQ4YO68Uh7w1Gmgexy48TezvIoERmPAeZ2k3wGeIBuILiQi7gMOA5C0H3BEKn8aOLUxn6RbgJ+lzx5Nf9dI+jpwMB0Co0qjFhwOAzMrU5HAuFrSDOBzwJ1kZ0hdVHSDknZPYxRbAX9OdsYUknYAFBFrJb0BeDoilknaBpgREY+nR8S+Bfh+0e2Wqd8zpKqsg5lZ1YoExmcjYj1wpaSryQa+/6fbAmk84lBgN0mPkJ2Su6Okk9IsV5FdLQ6wO3CdpGeAR8kOOwFMTeXbAluThcVXCtS7cm68zWwyKBIYPwbmAaTgWC/pzkZZOxHxzg4fnddm3oeA57cpX0s2AG5mZkOU50rv3wZmAdtLmgsofbQz2VlTZmY2CeTpYbwReA8wm+yq70ZgrAE+Vk21zMysbvI+D+NSSW+LiCsHUCczM6uhrQrMO1vSzspcJOlOSYdVVjMzM6uVIoFxYkSsJruG4jlkZzGdU0mtzMysdooERmPs4s3AZRGxtKnMzMzGXJHAuEPSv5AFxnXpNh3P9FjGzMzGRJHrMN4HHAg8GBHrJD0HeG811TIzs7rp2cOQ9C6A9FyK7SNiZXr/BPC71VbPzMzqIs8hqT9tmv5iy2cnllgXMzOrsTyBoQ7T7d6bmdmYyhMY0WG63XszMxtTeQa9XyDpLrLexPPSNOn9PpXVzMzMaiVPYLyw8lqYmVnt5bmX1MN5ViTpxxHxiolXyczM6qjIhXu9bFfiuszMrGbKDAwPgJuZjbEyA8PMzMZYmYHhazLMzMZY7sCQ9JkeZceXUiMzM6ulIj2MN7Qpe1NjIiLumXh1zMysrnqeVivpA8AHgX2aLtoD2An4t6oqZmZm9ZLnwr2vA9cCZwOnN5WviYgnK6mVmZnVTs9DUhGxKiIeioh3AnOA16WL+baStHflNTQzs1ooMuh9FvBR4IxUNAX4+x7LXCxphaR7msoOkPRjSXdL+idJO6fyKZIuSeU/lXRo0zIHpfLlks6X5DOyzMwGrMig91uBI4G1ABHx/8jGMbr5O+DwlrKLgNMj4iXAd4DTUvkfpfW+hGyA/a8lNer35fT5vunVuk4zM6tYkcDYEBFBuqJb0rReC0TEzUDrOMd+wM1p+nrgbWl6f+DGtNwKYCUwX9JMYOeI+Ena/mXA0QXqbWZmJSgSGN+UdCEwQ9IfAd8HvtLHNpcCR6XpY8jGRQB+ChwpaZs0NnJQ+mwW8EjT8o+kMjMzG6A8Z0kBEBGfl/QGYDXwfOD/RMT1fWzzROB8SR8HFgEbUvnFZLdSXww8DNwCbCy6ckkLgAUAs2bP6TG3mZnllTswAFJA9BMSzeu4DzgMQNJ+wBGp/Gng1MZ8km4Bfgb8FzC7aRWzgUe7rH8hsBDggLnzfENEM7OS5Llwbw3ZuIXY/I60AiIidi6yQUm7R8SKNKD958AFqXwHQBGxNvVkno6IZemz1ZIOAW4F3g18scg2zcxs4vI8QKnXmVAdSboCOBTYTdIjwFnAjpJOSrNcBVySpncHrpP0DFkPovneVB8kO+Nqe7KLCK/tt05mZtaf3IekJH0tIo7vVdYsXezXznlt5n2IbGyk3XoWAy/OW9dhW7VuQ++ZBmj6DlOGXQUzGwNFxjBe1PxG0jZkZzJNenULiFZF6+eAMbN28oxhnAF8DNhe0upGMdnZTQsrrFvt1T0o+uWAMbN28oxhnA2cLensiDij1/yTwbgGRb967Q8Hitl4KHJI6lpJv9tamK7mnjT6DYtVa9dvmp4+bWpZ1RkJrfvMAWI2mooExmlN09sBBwN3AK8rtUY1lScomkOhjPnKUMdwat6XDg+z0VHkSu/fb34vaQ7whdJrVEPdwmKQjX8/etVv2IHi8DAbHYWu9G7xCNmtPMZap7Coe1DkVeR7VB0uDg+zeityHcYXefZK762AucCdVVSqLvoJi3E+w6jb9y47TBweZvVTpIexDNg6Ta8EroiISfVM704N5kTOmuq0bLdGso5nJbXbN2WFiAfNzeohz3UY2wB/RXaX2f9MxXsCF0u6LSKeqrB+Q9PaSLVrEKs8vbaKEILBNrZVnRnmADEbjjw9jM+RPVlv74hYA5Aeq/r59Dq5uuoNR6+w6HioKmcjP8wGbliNbZWnFfvwldlg5AmMtwD7pafdARARqyV9ALiPMQyMZr3Cop+eQJ0auH4OiU14m2mfVjGIXqd9azZu8gRGNIdFU+FGSWP3vImup9A2fVbW4ai6NnCDGCepMjigvvvWbFTlCYxlkt4dEZc1F0p6F1kPY2w19y66hcXKtb3DY8a03g3WKDVwZR7aWrV2/UBP2YX671+zOsoTGCcBV0k6kezKboD5ZM+meGtVFRuGzUIhR1jkCYp28xYNj4kY2BjFBMOu6t7GFtur4ZlmZnWX5+aDjwIvl/Q6nr3F+TURcUOlNRuwPAPZjeluQdGYp1uD01g+T3BMVJ76VLXNfrY76ODoWA/3SMy2UOTWIDcCN1ZYl9rodgpta1jkCZpOjc3KtRsGEhrN9Rl0w7dq3Ya+exzDDo1mo3S40KwqWw27AnXWaCTahcWqdRtyHzbqNl+Rw1plGMat2Sdyh9863oKlyL+92ThxYNB57CLP/I33ra9eywzTKIUGODjM6sKB0UGn3kXrmEaRi/g6zTvoXsawTLRxrXNwmE0GDowcWsMi7y9LNyRbKmOf1DE43NuwycCB0UZr76L3/OtZtS7fjQndqJSnrsFhNq4m8jyMsZPntuWbH5JqvW1IOiV0h/qc3dOOz/Ixs35M+h5Gr1+EncYXOvUoen02TNN3mDKWYeFehtlguIdRA1VfizGOIWFmg1dpYEi6mOxutysi4sWp7ADgAmBH4CHguHT3222Bi4B5qV6XRcTZaZmHgDXARuDpiJhfZb2h+1lOE+lB9Nt4j0ujPy7fo5d+L1g0q7OqD0n9HXB4S9lFwOkR8RLgO8BpqfwYYGoqPwh4v6S9mpZ7bUQcOIiwGKRuvYvGISQ3PGZWB5UGRkTcDDzZUrwfcHOavh54W2N2YFp6wt/2wAZgdZX1G7ZeYTFOqgy+Ot1CpGHc/v3MYDiD3kuBo9L0McCcNP1tYC3wGNmjYD8fEY2wCeBfJN0haUG3lUtaIGmxpMVPPP54+bXPofksqeaGI08jMo49iiq/Tx3DwmxcDWPQ+0TgfEkfBxaR9SQADiYbo/gdYBfgXyV9PyIeBF4dEY9K2h24XtJ9qfeyhYhYCCwEOGDuvL4f8DR9hylt71iaHZue2nEcI+8pte16F8MOil7bL3L2T9XfxUFhNngDD4yIuA84DEDSfsAR6aNjge9FxFPACkn/RvbcjQfTLdaJiBWSvkMWLm0DY1AawdAcHK1h0al3MeywGNWB91EJiWHvJ7OqDDwwJO2eGv6tgD8nO2MKssNQrwO+JmkacAjwhTS9VUSsSdOHAZ8aVH1nTJuy2bUYrT2PTj2KvGGR9zDVZDMq4dBqMv5b2eRR9Wm1VwCHArtJegQ4C9hR0klplquAS9L0l4BLJC0FBFwSEXdJ2gf4jqRGfb8eEd8rq47tDj1NZL7GvO2mi4TFZGx4RjUkGibjv5lNLpUGRkS8s8NH57WZ9zdkg+Ct5Q8CB5RctbamT5uaPbhn01hFa29iyhbn13d7sE4/YTHZGp1RDwmYfP9mNnn5Su8cmg9LtQuRdjqFxWQPinEICJg8/15mzRwYHbT2MlpDA3o/9zlPr2JYDc9EGu68924ah3BwMJg9y4HB5r2GxmGpdp+3GwDvpFdY5G2I6tjo1rFORTgEzPrjwOii3UB3Iwja3cU2z+myvRqrUW+M68TBYFYuB0Ybzb2M1oHuRoD0usNs0cNPDoqJcTiYVc+BkWwxmN0mNBrT0P6q56ID2g6JzhwAZvXjwGjS7VqLvGdHtS7TttxBATgUzEaNA6OLRsPe3NNo6BYsvdY34XrVqKHtZz+Y2WhyYLRoe9PBDmdO5V5nwaAYpcZ2lOpqZhPjwGijU2hANdcguNE1s1HgwOig03jGOB5WMjPLYxgPUBoZVTTq4/iAJDObHNzD6KHbabRF12FmNsocGDkVDQ6HhJmNGwdGQQ4CM5usPIZhZma5ODDMzCwXB4aZmeXiwDAzs1wcGGZmlosDw8zMcnFgmJlZLg4MMzPLRREx7DpURtKvgYdLWt1uwOMlrWtQXOfBGMU6w2jW23Wu3nMj4rfafTDWgVEmSYsjYv6w61GE6zwYo1hnGM16u87D5UNSZmaWiwPDzMxycWDkt3DYFeiD6/Ym4n4AAAiSSURBVDwYo1hnGM16u85D5DEMMzPLxT0MMzPLxYFhZma5ODB6kHS4pPslLZd0+pDrMkfSDyQtk7RU0smpfFdJ10t6IP3dJZVL0vmp7ndJmte0rhPS/A9IOmEAdd9a0r9Lujq931vSralu/yBpSiqfmt4vT5/v1bSOM1L5/ZLeOIA6z5D0bUn3SbpX0ivqvq8lnZr+27hH0hWStqvbvpZ0saQVku5pKittv0o6SNLdaZnzJamiOn8u/bdxl6TvSJrR9Fnb/depPen0b1Q7EeFXhxewNfBzYB9gCvBTYP8h1mcmMC9N7wT8DNgf+Cxweio/HfhMmn4zcC0g4BDg1lS+K/Bg+rtLmt6l4rr/KfB14Or0/pvAO9L0BcAH0vQHgQvS9DuAf0jT+6f9PxXYO/27bF1xnS8F/jBNTwFm1HlfA7OA/wC2b9rH76nbvgZ+F5gH3NNUVtp+BW5L8yot+6aK6nwYsE2a/kxTndvuP7q0J53+jer2GnoF6vwCXgFc1/T+DOCMYderqT7fBd4A3A/MTGUzgfvT9IXAO5vmvz99/k7gwqbyzearoJ6zgRuA1wFXp/+RH2/6n23TfgauA16RprdJ86l13zfPV1Gdp5M1vmopr+2+JguMX6RGdJu0r99Yx30N7NXS+JayX9Nn9zWVbzZfmXVu+eytwOVpuu3+o0N70u3/h7q9fEiqu8b/gA2PpLKhS4cP5gK3AntExGPpo18Ce6TpTvUf9Pf6AvC/gWfS++cAKyPi6Tbb31S39PmqNP+g67w38GvgknQo7SJJ06jxvo6IR4HPA/8JPEa27+6g/vsaytuvs9J0a3nVTiTrzdCjbu3Ku/3/UCsOjBEkaUfgSuCUiFjd/FlkP1Fqc660pLcAKyLijmHXpaBtyA5BfDki5gJryQ6VbFLDfb0LcBRZ2P0OMA04fKiV6kPd9msvks4EngYuH3ZdqubA6O5RYE7T+9mpbGgkbUsWFpdHxFWp+FeSZqbPZwIrUnmn+g/ye70KOFLSQ8A3yA5LnQfMkLRNm+1vqlv6fDrwxIDrDNmvvEci4tb0/ttkAVLnff17wH9ExK8j4ingKrL9X/d9DeXt10fTdGt5JSS9B3gLcFwKOnrUrV35E3T+N6oVB0Z3twP7pjMYppANDC4aVmXS2R5fBe6NiHObPloENM4SOYFsbKNR/u50pskhwKrU7b8OOEzSLulX6WGprHQRcUZEzI6Ivcj2340RcRzwA+APOtS58V3+IM0fqfwd6cyevYF9yQY3KxERvwR+Ien5qej1wDJqvK/JDkUdImmH9N9Ko8613tdt6tL3fk2frZZ0SNoH725aV6kkHU52qPXIiFjX8l3a7b+27Una553+jepl2IModX+RnaXxM7KzG84ccl1eTdZVvwtYkl5vJjsGegPwAPB9YNc0v4AvpbrfDcxvWteJwPL0eu+A6n8oz54ltQ/Z/0TLgW8BU1P5dun98vT5Pk3Ln5m+y/2UcOZLjvoeCCxO+/sfyc7GqfW+Bj4J3AfcA3yN7EydWu1r4AqyMZanyHpy7ytzvwLz0/f/OfA3tJy4UGKdl5ONSTT+X7yg1/6jQ3vS6d+obi/fGsTMzHLxISkzM8vFgWFmZrk4MMzMLBcHhpmZ5eLAMDOzXBwYZmaWiwPDxoKk36S/e0k6dgDbW5BubX2fpNskvTrHModKemXT+09I+kiPZY6WtH+PeQ5Jt8Zeouw27J/IUY+re9XXrJUDw8bNXkClgZHuj/V+4NUR8QLgj4GvS/rtHoseCryyxzytjia7XXY3lwILIuJA4MVkt8o2K50Dw8bNOcBr0q/tU5U9uOlzkm5PD7p5P2z6lf1DSd+V9KCkcyQdl3oLd0t6XpdtfBQ4LSIeB4iIO8ka7ZPSuh+StFuani/ppnR34T8GTk11e03zCiU9T9L3JN0h6V8lvSD1Ro4EPpeW6VSn3cmuQiYiNkbEsrTOgyX9ON1t95am25w0b3easocD3ZbmOyqVvyiVLUn7bd8c+97G3Da9ZzEbKacDH4mIt0B26Ijs/kMvkzQV+DdJ/5LmPQB4IfAk2QN4LoqIg5U9yfBDwCkdtvEistuGN1vMs/dC2kJEPCTpAuA3EfH5VLfXN82yEPjjiHhA0suBv42I10laRHY7lW93+c7/F7hf0k3A94BLI+J/yG4R8pqIeFrS7wF/BbytZdkzye4hdaKyJ8bdJun7ZOF2XkRcnu57tHWX7dsk4cCwcXcY8FJJjRu7TSe7GdwG4PZIz2CQ9HOgESR3A68dVAWV3a7+lcC39OzTRKfmXT4iPiXpcrLveizZQ4MOJfuul6beQQDbtln8MLK7CTfGUrYD9gR+DJwpaTZwVUQ8UPR72fhxYNi4E/ChiNjsDrGSDgXWNxU90/T+Gbr/v7EMOAi4sansIGBpmn6aZw/3bpejjluRPUDnwBzzthURPwe+LOkrwK8lPQf4NPCDiHhrOiR2U5tFBbwtIu5vKb9X0q3AEcA1kt4fETduubhNJh7DsHGzhux55w3XAR9Q9hwRJO2n7Ml5E/FZ4DOpUUbSgWTPzv7b9PlDZAECmx8Caq0bAJE9BOs/JB2T1idJB3RbppmkI/Rs12RfYCOwkqyH0Xiuwns6LH4d8KHG8pLmpr/7AA9GxPlkt9p+abc62OTgwLBxcxewUdJPJZ0KXETWI7hT0j1kz36eUM86IhYBFwO3SLoP+Arwrnj2EaOfBM6TtJis8W74J+Ct7Qa9geOA90n6KVlP5ahU/g3gtDQg3WnQ+3iyMYwlZLc0Py4iNpIF29mS/r3Ld/402aGquyQtTe8B3g7ck9b5YuCybvvEJgff3tzMzHJxD8PMzHLxoLdZB5LOBI5pKf5WRPzlkOrzJbJndDc7LyIuGUZ9bPLxISkzM8vFh6TMzCwXB4aZmeXiwDAzs1wcGGZmlsv/B1sHsvQZ0Y/EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(train[\"Item_Outlet_Sales\"], train[\"Outlet_Establishment_Year\"], color=\"steelblue\", shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sales of Item in Different Locations')"
      ]
     },
     "execution_count": 209,
     "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.barplot(x='Outlet_Location_Type',y='Item_Outlet_Sales',data = train)\n",
    "plt.title(\"Sales of Item in Different Locations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Size\n",
       "Medium    2793\n",
       "Small     2388\n",
       "High       932\n",
       "Name: Item_Outlet_Sales, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Outlet_Size')['Item_Outlet_Sales'].agg('count').sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outlet_Identifier  Item_Type            \n",
       "OUT019             Snack Foods               75\n",
       "OUT010             Fruits and Vegetables     79\n",
       "OUT046             Fruits and Vegetables    126\n",
       "OUT017             Snack Foods              128\n",
       "OUT018             Fruits and Vegetables    135\n",
       "OUT049             Snack Foods              139\n",
       "OUT035             Snack Foods              140\n",
       "OUT027             Fruits and Vegetables    140\n",
       "OUT013             Fruits and Vegetables    142\n",
       "OUT045             Fruits and Vegetables    143\n",
       "Name: Item_Outlet_Sales, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['Outlet_Identifier', 'Item_Type'])['Item_Outlet_Sales'].agg('count').sort_values().groupby('Outlet_Identifier').tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d98767ee0>"
      ]
     },
     "execution_count": 92,
     "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.barplot(x=train.Outlet_Location_Type, y=train.Item_MRP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d985b6610>"
      ]
     },
     "execution_count": 93,
     "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.barplot(x=train.Outlet_Size, y=train.Item_Outlet_Sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Years of Establishmeent and there Sales Margin')"
      ]
     },
     "execution_count": 98,
     "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": [
    "train.groupby('Outlet_Establishment_Year')['Item_Outlet_Sales'].mean().plot.bar()\n",
    "plt.title(\"Years of Establishmeent and there Sales Margin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d97a4ef40>"
      ]
     },
     "execution_count": 99,
     "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.barplot(x=train.Item_Fat_Content, y=train.Item_Outlet_Sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Item_Fat_Content'].replace({'reg':'Regular','low fat':'Low Fat','LF':'Low Fat'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7d97422340>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot('Item_Fat_Content',kind = 'count',data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d96061d90>"
      ]
     },
     "execution_count": 114,
     "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.scatterplot(x='Item_Outlet_Sales',y='Item_Visibility', data=train, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9d61f190>"
      ]
     },
     "execution_count": 116,
     "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.scatterplot(x='Item_Outlet_Sales',y='Item_MRP',data=train, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9646d0a0>"
      ]
     },
     "execution_count": 112,
     "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.scatterplot(x = \"Item_Outlet_Sales\", y=\"Item_Weight\", data=train, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9d52c040>"
      ]
     },
     "execution_count": 118,
     "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": [
    "sns.heatmap(train.corr(), annot=True, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7d9c51ab50>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Item_Type',y = 'Item_Outlet_Sales',kind = 'box',data = new_train,aspect=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9c1c7430>"
      ]
     },
     "execution_count": 124,
     "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.violinplot(x='Item_Fat_Content', y='Item_Outlet_Sales', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9bf783d0>"
      ]
     },
     "execution_count": 127,
     "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.violinplot(x='Outlet_Type', y='Item_Outlet_Sales', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d9bde4700>"
      ]
     },
     "execution_count": 128,
     "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.violinplot(x='Outlet_Location_Type', y='Item_Outlet_Sales', data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "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": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train = new_train.drop(['Item_Identifier'], axis=1)\n",
    "new_train['Item_Weight'].fillna(values.mean(),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To balance the minority class\n",
    "new_train['Outlet_Size'].fillna('Small',inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>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>19.200</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>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>8.930</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",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8518</th>\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>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>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>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>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 × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Weight Item_Fat_Content  Item_Visibility              Item_Type  \\\n",
       "0           9.300          Low Fat         0.016047                  Dairy   \n",
       "1           5.920          Regular         0.019278            Soft Drinks   \n",
       "2          17.500          Low Fat         0.016760                   Meat   \n",
       "3          19.200          Regular         0.000000  Fruits and Vegetables   \n",
       "4           8.930          Low Fat         0.000000              Household   \n",
       "...           ...              ...              ...                    ...   \n",
       "8518        6.865          Low Fat         0.056783            Snack Foods   \n",
       "8519        8.380          Regular         0.046982           Baking Goods   \n",
       "8520       10.600          Low Fat         0.035186     Health and Hygiene   \n",
       "8521        7.210          Regular         0.145221            Snack Foods   \n",
       "8522       14.800          Low Fat         0.044878            Soft Drinks   \n",
       "\n",
       "      Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0     249.8092            OUT049                       1999      Medium   \n",
       "1      48.2692            OUT018                       2009      Medium   \n",
       "2     141.6180            OUT049                       1999      Medium   \n",
       "3     182.0950            OUT010                       1998       Small   \n",
       "4      53.8614            OUT013                       1987        High   \n",
       "...        ...               ...                        ...         ...   \n",
       "8518  214.5218            OUT013                       1987        High   \n",
       "8519  108.1570            OUT045                       2002       Small   \n",
       "8520   85.1224            OUT035                       2004       Small   \n",
       "8521  103.1332            OUT018                       2009      Medium   \n",
       "8522   75.4670            OUT046                       1997       Small   \n",
       "\n",
       "     Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "0                  Tier 1  Supermarket Type1          3735.1380  \n",
       "1                  Tier 3  Supermarket Type2           443.4228  \n",
       "2                  Tier 1  Supermarket Type1          2097.2700  \n",
       "3                  Tier 3      Grocery Store           732.3800  \n",
       "4                  Tier 3  Supermarket Type1           994.7052  \n",
       "...                   ...                ...                ...  \n",
       "8518               Tier 3  Supermarket Type1          2778.3834  \n",
       "8519               Tier 2  Supermarket Type1           549.2850  \n",
       "8520               Tier 2  Supermarket Type1          1193.1136  \n",
       "8521               Tier 3  Supermarket Type2          1845.5976  \n",
       "8522               Tier 1  Supermarket Type1           765.6700  \n",
       "\n",
       "[8523 rows x 11 columns]"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = new_train.columns[new_train.dtypes == 'object'].tolist()\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "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_Fat_Content_Low Fat</th>\n",
       "      <th>Item_Fat_Content_Regular</th>\n",
       "      <th>Item_Fat_Content_low fat</th>\n",
       "      <th>Item_Fat_Content_reg</th>\n",
       "      <th>Item_Type_Breads</th>\n",
       "      <th>Item_Type_Breakfast</th>\n",
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       "      <th>Item_Type_Dairy</th>\n",
       "      <th>Item_Type_Frozen Foods</th>\n",
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       "      <th>Outlet_Identifier_OUT046</th>\n",
       "      <th>Outlet_Identifier_OUT049</th>\n",
       "      <th>Outlet_Size_Medium</th>\n",
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       "      <th>Outlet_Location_Type_Tier 2</th>\n",
       "      <th>Outlet_Location_Type_Tier 3</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Fat_Content_Low Fat  Item_Fat_Content_Regular  \\\n",
       "0                         1                         0   \n",
       "1                         0                         1   \n",
       "2                         1                         0   \n",
       "3                         0                         1   \n",
       "4                         1                         0   \n",
       "\n",
       "   Item_Fat_Content_low fat  Item_Fat_Content_reg  Item_Type_Breads  \\\n",
       "0                         0                     0                 0   \n",
       "1                         0                     0                 0   \n",
       "2                         0                     0                 0   \n",
       "3                         0                     0                 0   \n",
       "4                         0                     0                 0   \n",
       "\n",
       "   Item_Type_Breakfast  Item_Type_Canned  Item_Type_Dairy  \\\n",
       "0                    0                 0                1   \n",
       "1                    0                 0                0   \n",
       "2                    0                 0                0   \n",
       "3                    0                 0                0   \n",
       "4                    0                 0                0   \n",
       "\n",
       "   Item_Type_Frozen Foods  Item_Type_Fruits and Vegetables  ...  \\\n",
       "0                       0                                0  ...   \n",
       "1                       0                                0  ...   \n",
       "2                       0                                0  ...   \n",
       "3                       0                                1  ...   \n",
       "4                       0                                0  ...   \n",
       "\n",
       "   Outlet_Identifier_OUT045  Outlet_Identifier_OUT046  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT049  Outlet_Size_Medium  Outlet_Size_Small  \\\n",
       "0                         1                   1                  0   \n",
       "1                         0                   1                  0   \n",
       "2                         1                   1                  0   \n",
       "3                         0                   0                  1   \n",
       "4                         0                   0                  0   \n",
       "\n",
       "   Outlet_Location_Type_Tier 2  Outlet_Location_Type_Tier 3  \\\n",
       "0                            0                            0   \n",
       "1                            0                            1   \n",
       "2                            0                            0   \n",
       "3                            0                            1   \n",
       "4                            0                            1   \n",
       "\n",
       "   Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "0                              1                              0   \n",
       "1                              0                              1   \n",
       "2                              1                              0   \n",
       "3                              0                              0   \n",
       "4                              1                              0   \n",
       "\n",
       "   Outlet_Type_Supermarket Type3  \n",
       "0                              0  \n",
       "1                              0  \n",
       "2                              0  \n",
       "3                              0  \n",
       "4                              0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df= pd.get_dummies(new_train[cat_cols], drop_first=True)\n",
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Identifier_OUT045</th>\n",
       "      <th>Outlet_Identifier_OUT046</th>\n",
       "      <th>Outlet_Identifier_OUT049</th>\n",
       "      <th>Outlet_Size_Medium</th>\n",
       "      <th>Outlet_Size_Small</th>\n",
       "      <th>Outlet_Location_Type_Tier 2</th>\n",
       "      <th>Outlet_Location_Type_Tier 3</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
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       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
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       "      <td>Medium</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight Item_Fat_Content  Item_Visibility              Item_Type  \\\n",
       "0         9.30          Low Fat         0.016047                  Dairy   \n",
       "1         5.92          Regular         0.019278            Soft Drinks   \n",
       "2        17.50          Low Fat         0.016760                   Meat   \n",
       "3        19.20          Regular         0.000000  Fruits and Vegetables   \n",
       "4         8.93          Low Fat         0.000000              Household   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  249.8092            OUT049                       1999      Medium   \n",
       "1   48.2692            OUT018                       2009      Medium   \n",
       "2  141.6180            OUT049                       1999      Medium   \n",
       "3  182.0950            OUT010                       1998       Small   \n",
       "4   53.8614            OUT013                       1987        High   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  ...  Outlet_Identifier_OUT045  \\\n",
       "0               Tier 1  Supermarket Type1  ...                         0   \n",
       "1               Tier 3  Supermarket Type2  ...                         0   \n",
       "2               Tier 1  Supermarket Type1  ...                         0   \n",
       "3               Tier 3      Grocery Store  ...                         0   \n",
       "4               Tier 3  Supermarket Type1  ...                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT046  Outlet_Identifier_OUT049  Outlet_Size_Medium  \\\n",
       "0                         0                         1                   1   \n",
       "1                         0                         0                   1   \n",
       "2                         0                         1                   1   \n",
       "3                         0                         0                   0   \n",
       "4                         0                         0                   0   \n",
       "\n",
       "   Outlet_Size_Small  Outlet_Location_Type_Tier 2  \\\n",
       "0                  0                            0   \n",
       "1                  0                            0   \n",
       "2                  0                            0   \n",
       "3                  1                            0   \n",
       "4                  0                            0   \n",
       "\n",
       "   Outlet_Location_Type_Tier 3  Outlet_Type_Supermarket Type1  \\\n",
       "0                            0                              1   \n",
       "1                            1                              0   \n",
       "2                            0                              1   \n",
       "3                            1                              0   \n",
       "4                            1                              1   \n",
       "\n",
       "   Outlet_Type_Supermarket Type2  Outlet_Type_Supermarket Type3  \n",
       "0                              0                              0  \n",
       "1                              1                              0  \n",
       "2                              0                              0  \n",
       "3                              0                              0  \n",
       "4                              0                              0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train = pd.concat([new_train, encoded_df], axis=1)\n",
    "new_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d99428bb0>"
      ]
     },
     "execution_count": 304,
     "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": [
    "new_train['Item_Visibility'].plot(kind = 'hist',bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.057792342999999996"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = new_train[new_train['Item_Visibility']!=0]['Item_Visibility'].median()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train['Item_Visibility'] = new_train['Item_Visibility'].replace(0.00,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.fit(new_train[['Item_MRP']])\n",
    "sc.fit(new_train[['Item_Weight']])\n",
    "sc.fit(new_train[['Item_Visibility']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train['MRP_Scaled'] = sc.transform(new_train[['Item_MRP']])\n",
    "new_train['Item_Weight_Scaled'] = sc.transform(new_train[['Item_Weight']])\n",
    "new_train['Item_Visibility_Scaled'] = sc.transform(new_train[['Item_Visibility']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Weight', 'Item_Fat_Content', 'Item_Visibility', 'Item_Type',\n",
       "       'Item_MRP', 'Outlet_Identifier', 'Outlet_Establishment_Year',\n",
       "       'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type',\n",
       "       'Item_Outlet_Sales', 'Item_Fat_Content_Low Fat',\n",
       "       'Item_Fat_Content_Regular', 'Item_Fat_Content_low fat',\n",
       "       'Item_Fat_Content_reg', 'Item_Type_Breads', 'Item_Type_Breakfast',\n",
       "       'Item_Type_Canned', 'Item_Type_Dairy', 'Item_Type_Frozen Foods',\n",
       "       'Item_Type_Fruits and Vegetables', 'Item_Type_Hard Drinks',\n",
       "       'Item_Type_Health and Hygiene', 'Item_Type_Household', 'Item_Type_Meat',\n",
       "       'Item_Type_Others', 'Item_Type_Seafood', 'Item_Type_Snack Foods',\n",
       "       'Item_Type_Soft Drinks', 'Item_Type_Starchy Foods',\n",
       "       'Outlet_Identifier_OUT013', 'Outlet_Identifier_OUT017',\n",
       "       'Outlet_Identifier_OUT018', 'Outlet_Identifier_OUT019',\n",
       "       'Outlet_Identifier_OUT027', 'Outlet_Identifier_OUT035',\n",
       "       'Outlet_Identifier_OUT045', 'Outlet_Identifier_OUT046',\n",
       "       'Outlet_Identifier_OUT049', 'Outlet_Size_Medium', 'Outlet_Size_Small',\n",
       "       'Outlet_Location_Type_Tier 2', 'Outlet_Location_Type_Tier 3',\n",
       "       'Outlet_Type_Supermarket Type1', 'Outlet_Type_Supermarket Type2',\n",
       "       'Outlet_Type_Supermarket Type3', 'MRP_Scaled', 'Item_Weight_Scaled',\n",
       "       'Item_Visibility_Scaled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train = new_train.drop(['Item_MRP',\n",
    "                            'Item_Weight', 'Item_Visibility',\n",
    "                            'Item_Type', 'Item_Fat_Content',\n",
    "                            'Outlet_Identifier', 'Outlet_Location_Type',\n",
    "                           'Outlet_Type', 'Outlet_Size'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "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>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Fat_Content_Low Fat</th>\n",
       "      <th>Item_Fat_Content_Regular</th>\n",
       "      <th>Item_Fat_Content_low fat</th>\n",
       "      <th>Item_Fat_Content_reg</th>\n",
       "      <th>Item_Type_Breads</th>\n",
       "      <th>Item_Type_Breakfast</th>\n",
       "      <th>Item_Type_Canned</th>\n",
       "      <th>Item_Type_Dairy</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Size_Medium</th>\n",
       "      <th>Outlet_Size_Small</th>\n",
       "      <th>Outlet_Location_Type_Tier 2</th>\n",
       "      <th>Outlet_Location_Type_Tier 3</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "      <th>MRP_Scaled</th>\n",
       "      <th>Item_Weight_Scaled</th>\n",
       "      <th>Item_Visibility_Scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>5115.201897</td>\n",
       "      <td>189.056415</td>\n",
       "      <td>-1.098896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>987.229410</td>\n",
       "      <td>119.826748</td>\n",
       "      <td>-1.032720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>2899.213523</td>\n",
       "      <td>357.010044</td>\n",
       "      <td>-1.084297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1998</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>3728.269504</td>\n",
       "      <td>391.829698</td>\n",
       "      <td>-0.243868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1101.769688</td>\n",
       "      <td>181.478019</td>\n",
       "      <td>-0.243868</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",
       "      <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>1987</td>\n",
       "      <td>2778.3834</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4392.440081</td>\n",
       "      <td>139.182380</td>\n",
       "      <td>-0.264533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>2002</td>\n",
       "      <td>549.2850</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2213.860306</td>\n",
       "      <td>170.212837</td>\n",
       "      <td>-0.465278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8520</th>\n",
       "      <td>2004</td>\n",
       "      <td>1193.1136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1742.062176</td>\n",
       "      <td>215.683209</td>\n",
       "      <td>-0.706889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8521</th>\n",
       "      <td>2009</td>\n",
       "      <td>1845.5976</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>2110.962081</td>\n",
       "      <td>146.248721</td>\n",
       "      <td>1.546852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>1997</td>\n",
       "      <td>765.6700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1544.298826</td>\n",
       "      <td>301.708239</td>\n",
       "      <td>-0.508376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8523 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Outlet_Establishment_Year  Item_Outlet_Sales  Item_Fat_Content_Low Fat  \\\n",
       "0                          1999          3735.1380                         1   \n",
       "1                          2009           443.4228                         0   \n",
       "2                          1999          2097.2700                         1   \n",
       "3                          1998           732.3800                         0   \n",
       "4                          1987           994.7052                         1   \n",
       "...                         ...                ...                       ...   \n",
       "8518                       1987          2778.3834                         1   \n",
       "8519                       2002           549.2850                         0   \n",
       "8520                       2004          1193.1136                         1   \n",
       "8521                       2009          1845.5976                         0   \n",
       "8522                       1997           765.6700                         1   \n",
       "\n",
       "      Item_Fat_Content_Regular  Item_Fat_Content_low fat  \\\n",
       "0                            0                         0   \n",
       "1                            1                         0   \n",
       "2                            0                         0   \n",
       "3                            1                         0   \n",
       "4                            0                         0   \n",
       "...                        ...                       ...   \n",
       "8518                         0                         0   \n",
       "8519                         1                         0   \n",
       "8520                         0                         0   \n",
       "8521                         1                         0   \n",
       "8522                         0                         0   \n",
       "\n",
       "      Item_Fat_Content_reg  Item_Type_Breads  Item_Type_Breakfast  \\\n",
       "0                        0                 0                    0   \n",
       "1                        0                 0                    0   \n",
       "2                        0                 0                    0   \n",
       "3                        0                 0                    0   \n",
       "4                        0                 0                    0   \n",
       "...                    ...               ...                  ...   \n",
       "8518                     0                 0                    0   \n",
       "8519                     0                 0                    0   \n",
       "8520                     0                 0                    0   \n",
       "8521                     0                 0                    0   \n",
       "8522                     0                 0                    0   \n",
       "\n",
       "      Item_Type_Canned  Item_Type_Dairy  ...  Outlet_Size_Medium  \\\n",
       "0                    0                1  ...                   1   \n",
       "1                    0                0  ...                   1   \n",
       "2                    0                0  ...                   1   \n",
       "3                    0                0  ...                   0   \n",
       "4                    0                0  ...                   0   \n",
       "...                ...              ...  ...                 ...   \n",
       "8518                 0                0  ...                   0   \n",
       "8519                 0                0  ...                   0   \n",
       "8520                 0                0  ...                   0   \n",
       "8521                 0                0  ...                   1   \n",
       "8522                 0                0  ...                   0   \n",
       "\n",
       "      Outlet_Size_Small  Outlet_Location_Type_Tier 2  \\\n",
       "0                     0                            0   \n",
       "1                     0                            0   \n",
       "2                     0                            0   \n",
       "3                     1                            0   \n",
       "4                     0                            0   \n",
       "...                 ...                          ...   \n",
       "8518                  0                            0   \n",
       "8519                  1                            1   \n",
       "8520                  1                            1   \n",
       "8521                  0                            0   \n",
       "8522                  1                            0   \n",
       "\n",
       "      Outlet_Location_Type_Tier 3  Outlet_Type_Supermarket Type1  \\\n",
       "0                               0                              1   \n",
       "1                               1                              0   \n",
       "2                               0                              1   \n",
       "3                               1                              0   \n",
       "4                               1                              1   \n",
       "...                           ...                            ...   \n",
       "8518                            1                              1   \n",
       "8519                            0                              1   \n",
       "8520                            0                              1   \n",
       "8521                            1                              0   \n",
       "8522                            0                              1   \n",
       "\n",
       "      Outlet_Type_Supermarket Type2  Outlet_Type_Supermarket Type3  \\\n",
       "0                                 0                              0   \n",
       "1                                 1                              0   \n",
       "2                                 0                              0   \n",
       "3                                 0                              0   \n",
       "4                                 0                              0   \n",
       "...                             ...                            ...   \n",
       "8518                              0                              0   \n",
       "8519                              0                              0   \n",
       "8520                              0                              0   \n",
       "8521                              1                              0   \n",
       "8522                              0                              0   \n",
       "\n",
       "       MRP_Scaled  Item_Weight_Scaled  Item_Visibility_Scaled  \n",
       "0     5115.201897          189.056415               -1.098896  \n",
       "1      987.229410          119.826748               -1.032720  \n",
       "2     2899.213523          357.010044               -1.084297  \n",
       "3     3728.269504          391.829698               -0.243868  \n",
       "4     1101.769688          181.478019               -0.243868  \n",
       "...           ...                 ...                     ...  \n",
       "8518  4392.440081          139.182380               -0.264533  \n",
       "8519  2213.860306          170.212837               -0.465278  \n",
       "8520  1742.062176          215.683209               -0.706889  \n",
       "8521  2110.962081          146.248721                1.546852  \n",
       "8522  1544.298826          301.708239               -0.508376  \n",
       "\n",
       "[8523 rows x 40 columns]"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = new_train['Item_Outlet_Sales']\n",
    "X = new_train.drop('Item_Outlet_Sales', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_model = LinearRegression(normalize=True,fit_intercept= True)\n",
    "lr_model.fit(X,y)\n",
    "y_pred = lr_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1038.181478672767"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "XGB = XGBRegressor()\n",
    "xgb_fit = XGB.fit(X,y)\n",
    "y_pred = xgb_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "662.4332709408842"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforming Unseen Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                   976\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                  1606\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.drop(['Item_Identifier'], axis=1)\n",
    "test['Item_Weight'].fillna(values.mean(),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Outlet_Size'].fillna('Small',inplace  = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Weight                  0\n",
       "Item_Fat_Content             0\n",
       "Item_Visibility              0\n",
       "Item_Type                    0\n",
       "Item_MRP                     0\n",
       "Outlet_Identifier            0\n",
       "Outlet_Establishment_Year    0\n",
       "Outlet_Size                  0\n",
       "Outlet_Location_Type         0\n",
       "Outlet_Type                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = test.columns[test.dtypes == 'object'].tolist()\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "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_Fat_Content_Low Fat</th>\n",
       "      <th>Item_Fat_Content_Regular</th>\n",
       "      <th>Item_Fat_Content_low fat</th>\n",
       "      <th>Item_Fat_Content_reg</th>\n",
       "      <th>Item_Type_Breads</th>\n",
       "      <th>Item_Type_Breakfast</th>\n",
       "      <th>Item_Type_Canned</th>\n",
       "      <th>Item_Type_Dairy</th>\n",
       "      <th>Item_Type_Frozen Foods</th>\n",
       "      <th>Item_Type_Fruits and Vegetables</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Identifier_OUT045</th>\n",
       "      <th>Outlet_Identifier_OUT046</th>\n",
       "      <th>Outlet_Identifier_OUT049</th>\n",
       "      <th>Outlet_Size_Medium</th>\n",
       "      <th>Outlet_Size_Small</th>\n",
       "      <th>Outlet_Location_Type_Tier 2</th>\n",
       "      <th>Outlet_Location_Type_Tier 3</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Fat_Content_Low Fat  Item_Fat_Content_Regular  \\\n",
       "0                         1                         0   \n",
       "1                         0                         0   \n",
       "2                         1                         0   \n",
       "3                         1                         0   \n",
       "4                         0                         1   \n",
       "\n",
       "   Item_Fat_Content_low fat  Item_Fat_Content_reg  Item_Type_Breads  \\\n",
       "0                         0                     0                 0   \n",
       "1                         0                     1                 0   \n",
       "2                         0                     0                 0   \n",
       "3                         0                     0                 0   \n",
       "4                         0                     0                 0   \n",
       "\n",
       "   Item_Type_Breakfast  Item_Type_Canned  Item_Type_Dairy  \\\n",
       "0                    0                 0                0   \n",
       "1                    0                 0                1   \n",
       "2                    0                 0                0   \n",
       "3                    0                 0                0   \n",
       "4                    0                 0                1   \n",
       "\n",
       "   Item_Type_Frozen Foods  Item_Type_Fruits and Vegetables  ...  \\\n",
       "0                       0                                0  ...   \n",
       "1                       0                                0  ...   \n",
       "2                       0                                0  ...   \n",
       "3                       0                                0  ...   \n",
       "4                       0                                0  ...   \n",
       "\n",
       "   Outlet_Identifier_OUT045  Outlet_Identifier_OUT046  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT049  Outlet_Size_Medium  Outlet_Size_Small  \\\n",
       "0                         1                   1                  0   \n",
       "1                         0                   0                  1   \n",
       "2                         0                   0                  1   \n",
       "3                         0                   0                  1   \n",
       "4                         0                   1                  0   \n",
       "\n",
       "   Outlet_Location_Type_Tier 2  Outlet_Location_Type_Tier 3  \\\n",
       "0                            0                            0   \n",
       "1                            1                            0   \n",
       "2                            0                            1   \n",
       "3                            1                            0   \n",
       "4                            0                            1   \n",
       "\n",
       "   Outlet_Type_Supermarket Type1  Outlet_Type_Supermarket Type2  \\\n",
       "0                              1                              0   \n",
       "1                              1                              0   \n",
       "2                              0                              0   \n",
       "3                              1                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   Outlet_Type_Supermarket Type3  \n",
       "0                              0  \n",
       "1                              0  \n",
       "2                              0  \n",
       "3                              0  \n",
       "4                              1  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_test = pd.get_dummies(test[cat_cols], drop_first=True)\n",
    "encoded_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Identifier_OUT045</th>\n",
       "      <th>Outlet_Identifier_OUT046</th>\n",
       "      <th>Outlet_Identifier_OUT049</th>\n",
       "      <th>Outlet_Size_Medium</th>\n",
       "      <th>Outlet_Size_Small</th>\n",
       "      <th>Outlet_Location_Type_Tier 2</th>\n",
       "      <th>Outlet_Location_Type_Tier 3</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.750000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.300000</td>\n",
       "      <td>reg</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.600000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>Others</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.315000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>773.386376</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  Item_MRP  \\\n",
       "0    20.750000          Low Fat         0.007565  Snack Foods  107.8622   \n",
       "1     8.300000              reg         0.038428        Dairy   87.3198   \n",
       "2    14.600000          Low Fat         0.099575       Others  241.7538   \n",
       "3     7.315000          Low Fat         0.015388  Snack Foods  155.0340   \n",
       "4   773.386376          Regular         0.118599        Dairy  234.2300   \n",
       "\n",
       "  Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0            OUT049                       1999      Medium   \n",
       "1            OUT017                       2007       Small   \n",
       "2            OUT010                       1998       Small   \n",
       "3            OUT017                       2007       Small   \n",
       "4            OUT027                       1985      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  ...  Outlet_Identifier_OUT045  \\\n",
       "0               Tier 1  Supermarket Type1  ...                         0   \n",
       "1               Tier 2  Supermarket Type1  ...                         0   \n",
       "2               Tier 3      Grocery Store  ...                         0   \n",
       "3               Tier 2  Supermarket Type1  ...                         0   \n",
       "4               Tier 3  Supermarket Type3  ...                         0   \n",
       "\n",
       "   Outlet_Identifier_OUT046  Outlet_Identifier_OUT049  Outlet_Size_Medium  \\\n",
       "0                         0                         1                   1   \n",
       "1                         0                         0                   0   \n",
       "2                         0                         0                   0   \n",
       "3                         0                         0                   0   \n",
       "4                         0                         0                   1   \n",
       "\n",
       "   Outlet_Size_Small  Outlet_Location_Type_Tier 2  \\\n",
       "0                  0                            0   \n",
       "1                  1                            1   \n",
       "2                  1                            0   \n",
       "3                  1                            1   \n",
       "4                  0                            0   \n",
       "\n",
       "   Outlet_Location_Type_Tier 3  Outlet_Type_Supermarket Type1  \\\n",
       "0                            0                              1   \n",
       "1                            0                              1   \n",
       "2                            1                              0   \n",
       "3                            0                              1   \n",
       "4                            1                              0   \n",
       "\n",
       "   Outlet_Type_Supermarket Type2  Outlet_Type_Supermarket Type3  \n",
       "0                              0                              0  \n",
       "1                              0                              0  \n",
       "2                              0                              0  \n",
       "3                              0                              0  \n",
       "4                              0                              1  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test = pd.concat([test, encoded_test], axis=1)\n",
    "new_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d98eb2880>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD4CAYAAAAD6PrjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAU40lEQVR4nO3dfbRldX3f8fdHQB5MIhBu6GQGvGDHWEgUyAVNUxuFWBCrg42h2CYSysqYiqt1JatLUFckWWUVuxIpaSx2DEawMUA0xqliWkA0y7UKeMGRR6nDU5lxhAlBHsRCwW//OHu2h8t9OPfO3efcc+f9Wuusu/dv733O9+6Z2Z/Z+/fb+6SqkCQJ4EWjLkCStHIYCpKklqEgSWoZCpKklqEgSWrtPeoCdschhxxSk5OToy5DksbKzTff/LdVNTHbsrEOhcnJSaanp0ddhiSNlSQPzLXMy0eSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpFZnoZBkvyQ3JflmkjuS/F7T/skk9yXZ0ryOadqT5I+SbE1ya5LjuqpNkjS7Lm9eexo4saqeTLIP8LUkX2qW/buq+syM9d8ErG9erwEuaX5Kkoaks1Co3rf3PNnM7tO85vtGnw3A5c12NyQ5MMmaqtrRRX2T536xnb7/wjd38RGSNHY67VNIsleSLcDDwDVVdWOz6ILmEtFFSfZt2tYCD/Ztvq1pm/meG5NMJ5neuXNnl+VL0h6n01Coqueq6hhgHXBCkp8FzgNeCRwPHAy8b5HvuamqpqpqamJi1uc5SZKWaCijj6rqe8D1wClVtaN6ngb+FDihWW07cFjfZuuaNknSkHQ5+mgiyYHN9P7AG4FvJVnTtAU4Dbi92WQz8M5mFNJrgce66k+QJM2uy9FHa4DLkuxFL3yuqqovJPlykgkgwBbgt5r1rwZOBbYCTwFndVibJGkWXY4+uhU4dpb2E+dYv4BzuqpHkrQw72iWJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLU6C4Uk+yW5Kck3k9yR5Pea9iOS3Jhka5Irk7y4ad+3md/aLJ/sqjZJ0uy6PFN4Gjixql4NHAOckuS1wIeBi6rq7wOPAmc3658NPNq0X9SsJ0kaos5CoXqebGb3aV4FnAh8pmm/DDitmd7QzNMsPylJuqpPkvRCnfYpJNkryRbgYeAa4B7ge1X1bLPKNmBtM70WeBCgWf4Y8JOzvOfGJNNJpnfu3Nll+ZK0x+k0FKrquao6BlgHnAC8chnec1NVTVXV1MTExG7XKEn6kaGMPqqq7wHXA78AHJhk72bROmB7M70dOAygWf5S4JFh1CdJ6uly9NFEkgOb6f2BNwJ30QuHtzernQl8vpne3MzTLP9yVVVX9UmSXmjvhVdZsjXAZUn2ohc+V1XVF5LcCVyR5N8D3wAubda/FPhUkq3A3wFndFibJGkWnYVCVd0KHDtL+730+hdmtv9f4Fe7qkeStDDvaJYktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVKrs1BIcliS65PcmeSOJP+2aT8/yfYkW5rXqX3bnJdka5K7k5zcVW2SpNnt3eF7Pwv8TlXdkuTHgZuTXNMsu6iq/qB/5SRHAWcARwM/DVyb5BVV9VyHNUqS+nR2plBVO6rqlmb6CeAuYO08m2wArqiqp6vqPmArcEJX9UmSXmgofQpJJoFjgRubpvckuTXJJ5Ic1LStBR7s22wbs4RIko1JppNM79y5s8OqJWnP03koJPkx4LPAe6vqceAS4OXAMcAO4A8X835VtamqpqpqamJiYtnrlaQ9WaehkGQfeoHwZ1X1lwBV9VBVPVdVPwQ+zo8uEW0HDuvbfF3TJkkaki5HHwW4FLirqj7S176mb7W3Abc305uBM5Lsm+QIYD1wU1f1SZJeqMvRR78I/DpwW5ItTdv7gXckOQYo4H7gXQBVdUeSq4A76Y1cOseRR5I0XJ2FQlV9Dcgsi66eZ5sLgAu6qkmSND/vaJYktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktbq8eW2PN3nuF9vp+y988wgrkaTBeKYgSWoNFApJfq7rQiRJozfomcJ/SXJTkncneWmnFUmSRmagUKiq1wH/kt6jrW9O8ukkb+y0MknS0A3cp1BV3wY+CLwP+CXgj5J8K8k/66o4SdJwDdqn8KokF9H7nuUTgbdU1T9opi/qsD5J0hANOiT1PwN/Ary/qn6wq7GqvpPkg51UJkkaukFD4c3AD3Z96U2SFwH7VdVTVfWpzqqTJA3VoH0K1wL7980f0LRJklaRQUNhv6p6ctdMM31ANyVJkkZl0FD4fpLjds0k+XngB/OsL0kaQ4P2KbwX+Isk36H3vct/D/jnnVUlSRqJgUKhqr6e5JXAzzRNd1fV/5tvmySHAZcDhwIFbKqqi5McDFwJTAL3A6dX1aNJAlwMnAo8BfxGVd2y+F9JkrRUi3kg3vHAq4DjgHckeecC6z8L/E5VHQW8FjgnyVHAucB1VbUeuK6ZB3gTsL55bQQuWURtkqRlMNCZQpJPAS8HtgDPNc1F70xgVlW1A9jRTD+R5C5gLbABeH2z2mXAV+jdJb0BuLyqCrghyYFJ1jTvI0kagkH7FKaAo5oD9qIlmQSOBW4EDu070H+X3uUl6AXGg32bbWvanhcKSTbSO5Pg8MMPX0o5ner/DgVJGjeDXj66nV7n8qIl+THgs8B7q+rx/mVNyCwqaKpqU1VNVdXUxMTEUkqSJM1h0DOFQ4A7k9wEPL2rsareOt9GSfahFwh/VlV/2TQ/tOuyUJI1wMNN+3Z6T2HdZV3TJkkakkFD4fzFvnEzmuhS4K6q+kjfos3AmcCFzc/P97W/J8kVwGuAx8alP2F3Lhn5lZ2SVpJBh6R+NcnLgPVVdW2SA4C9FtjsF4FfB25LsqVpez+9MLgqydnAA8DpzbKr6Q1H3UpvSOpZi/pNJEm7bdDRR79Jr3P3YHqjkNYCHwNOmmubqvoavRvdZvOC7Zr+hXMGqUeS1I1BO5rPofc//8eh/cKdn+qqKEnSaAwaCk9X1TO7ZpLszSJHDUmSVr5BO5q/muT9wP7NdzO/G/jv3ZW1+tihLGkcDHqmcC6wE7gNeBe9TmG/cU2SVplBRx/9EPh485IkrVKDjj66j1n6EKrqyGWvSJI0Mot59tEu+wG/Sm94qpbA5yNJWqkGvXz0yIym/5TkZuB3l7+klcvOYkmr3aCXj47rm30RvTOHQc8yJEljYtAD+x/2TT9L841py16NWp6VSBqFQS8fvaHrQiRJozfo5aPfnm/5jKegapl51iBpWBYz+uh4eo+3BngLcBPw7S6KkiSNxqChsA44rqqeAEhyPvDFqvq1rgqTJA3foKFwKPBM3/wz/Oi7lbVMvH9B0qgNGgqXAzcl+VwzfxpwWTclSZJGZdDRRxck+RLwuqbprKr6RndlSZJGYdCnpAIcADxeVRcD25Ic0VFNkqQRGXRI6ofojUD6GeBPgX2A/0bv29i0AjhsVdJyGPRM4W3AW4HvA1TVd4Af76ooSdJoDBoKz1RV0Tw+O8lLuitJkjQqg4bCVUn+K3Bgkt8ErmWBL9xJ8okkDye5va/t/CTbk2xpXqf2LTsvydYkdyc5eSm/jCRp9yzYp5AkwJXAK4HH6fUr/G5VXbPApp8E/pjecNZ+F1XVH8z4jKOAM4CjgZ8Grk3yiqp6bpBfQpK0PBYMhaqqJFdX1c8BCwVB/3Z/k2RywNU3AFdU1dPAfUm2AicA/2vQz5Mk7b5BLx/dkuT4ZfrM9yS5tbm8dFDTthZ4sG+dbU3bCyTZmGQ6yfTOnTuXqSRJEgweCq8BbkhyT3NAvy3JrUv4vEuAlwPHADt4/vc0DKSqNlXVVFVNTUxMLKEESdJc5r18lOTwqvo/wLJ0/FbVQ33v/XHgC83sduCwvlXXNW2SpCFaqE/hr+g9HfWBJJ+tql/ZnQ9LsqaqdjSzbwN2jUzaDHw6yUfodTSvp/dobs3DB+hJWm4LhUL6po9czBsn+XPg9cAhSbYBHwJen+QYevc73A+8C6Cq7khyFXAnva/7PGeljDwaxwOvdzdLWqqFQqHmmF5QVb1jluZL51n/AuCCxXyGJGl5LRQKr07yOL0zhv2baZr5qqqf6LQ6SdJQzRsKVbXXsAqRJI3eYh6dLUla5Qb95jWtAnZAS1qIZwqSpJahIElqGQqSpJahIElqGQqSpJahIElqOSR1lRvHZzdJGh1DQXOaGSje2yCtfl4+kiS1PFMYM14OktQlQ2GJPDhLWo0MBflMJEkt+xQkSS1DQZLU8vLRHso+EUmzMRT0PPOFhX0P0urXWSgk+QTwT4GHq+pnm7aDgSuBSeB+4PSqejRJgIuBU4GngN+oqlu6qk3La64gMTik8dNln8IngVNmtJ0LXFdV64HrmnmANwHrm9dG4JIO65IkzaGzM4Wq+pskkzOaNwCvb6YvA74CvK9pv7yqCrghyYFJ1lTVjq7q03B56UkaD8MefXRo34H+u8ChzfRa4MG+9bY1bS+QZGOS6STTO3fu7K5SSdoDjWxIanNWUEvYblNVTVXV1MTERAeVSdKea9ih8FCSNQDNz4eb9u3AYX3rrWvaJElDNOxQ2Ayc2UyfCXy+r/2d6Xkt8Jj9CZI0fF0OSf1zep3KhyTZBnwIuBC4KsnZwAPA6c3qV9MbjrqV3pDUs7qqS5I0ty5HH71jjkUnzbJuAed0VctiebevpD2Vzz6SJLV8zIVGyvsXpJXFMwVJUstQkCS1DAVJUstQkCS17GjWkjhsV1qdPFOQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSyyGpGls+N0lafoaCOuO9DNL4MRS04nlGIA2PoaBVxxCRls6OZklSyzMFrUhz9Ucstl3S4hgKGjoP4NLKNZJQSHI/8ATwHPBsVU0lORi4EpgE7gdOr6pHR1GfRsOwkEZvlH0Kb6iqY6pqqpk/F7iuqtYD1zXzkqQhWkkdzRuAy5rpy4DTRliLJO2RRhUKBfzPJDcn2di0HVpVO5rp7wKHzrZhko1JppNM79y5cxi1StIeY1Qdzf+oqrYn+SngmiTf6l9YVZWkZtuwqjYBmwCmpqZmXUeStDQjCYWq2t78fDjJ54ATgIeSrKmqHUnWAA+PojbtebzZTfqRoYdCkpcAL6qqJ5rpfwL8PrAZOBO4sPn5+WHXptXHA760OKM4UzgU+FySXZ//6ar66yRfB65KcjbwAHD6CGrTHsLhr9Lshh4KVXUv8OpZ2h8BThp2PdpzGATSwryjWVpGXq7SuFtJ9ylIkkbMUJAktbx8JM3BS0HaExkK2AGp0TF4tNJ4+UiS1PJMQRoCz0Y1LgwFqc8gB++Z63jZR6uJl48kSS3PFKTd5KUhrSaGgjQAD/zaUxgKUkeWK0gctqphsk9BktQyFCRJLS8fSSuEl4m0EnimIElqGQqSpJaXj6QVyCGwGhVDQdpDLLbPwj6OPZOhII0RD9TqmqEgackMqdVnxYVCklOAi4G9gD+pqgtHXJI0Vro+UM/V3zHo02Pn2n6Q9Rf7+4zqktk4h+WKCoUkewEfBd4IbAO+nmRzVd052sqklWecDjyLfSR5FwHRhZVWz3JIVY26hlaSXwDOr6qTm/nzAKrqP8y2/tTUVE1PTy/psxzdIWkYZobFYo89/dsvVwglubmqpmZbtqLOFIC1wIN989uA1/SvkGQjsLGZfTLJ3Uv8rEOAv13itqNm7aMxzrXDeNc/trXnw7tXez68uPYBvWyuBSstFBZUVZuATbv7Pkmm50rKlc7aR2Oca4fxrt/ah2el3dG8HTisb35d0yZJGoKVFgpfB9YnOSLJi4EzgM0jrkmS9hgr6vJRVT2b5D3A/6A3JPUTVXVHRx+325egRsjaR2Oca4fxrt/ah2RFjT6SJI3WSrt8JEkaIUNBktRalaGQ5JQkdyfZmuTcWZbvm+TKZvmNSSb7lp3XtN+d5ORh1t18/pJqTzKZ5AdJtjSvj63A2v9xkluSPJvk7TOWnZnk283rzOFV3X7+7tT+XN9+H/rAiAFq/+0kdya5Ncl1SV7Wt2yl7/f5al/p+/23ktzW1Pe1JEf1LRvpcWZeVbWqXvQ6qO8BjgReDHwTOGrGOu8GPtZMnwFc2Uwf1ay/L3BE8z57jUntk8DtK3y/TwKvAi4H3t7XfjBwb/PzoGb6oHGovVn25Arf728ADmim/3Xf35lx2O+z1j4m+/0n+qbfCvx1Mz3S48xCr9V4pnACsLWq7q2qZ4ArgA0z1tkAXNZMfwY4KUma9iuq6umqug/Y2rzfsOxO7aO2YO1VdX9V3Qr8cMa2JwPXVNXfVdWjwDXAKcMourE7tY/aILVfX1VPNbM30Lv/B8Zjv89V+6gNUvvjfbMvAXaN6hn1cWZeqzEUZntUxtq51qmqZ4HHgJ8ccNsu7U7tAEck+UaSryZ5XdfFzlVXYzH7bhz2+3z2SzKd5IYkpy1vaQtabO1nA19a4rbLbXdqhzHY70nOSXIP8B+Bf7OYbUdlRd2noN2yAzi8qh5J8vPAXyU5esb/VtSNl1XV9iRHAl9OcltV3TPqomZK8mvAFPBLo65lseaofcXv96r6KPDRJP8C+CAw9H6bxVqNZwqDPCqjXSfJ3sBLgUcG3LZLS669ORV9BKCqbqZ3nfIVnVc8S12Nxey7cdjvc6qq7c3Pe4GvAMcuZ3ELGKj2JL8MfAB4a1U9vZhtO7Q7tY/Ffu9zBbDrbGbU+31+o+7UWO4XvbOfe+l14OzqADp6xjrn8PzO2qua6aN5fgfQvQy3o3l3ap/YVSu9zq/twMErqfa+dT/JCzua76PX2XlQMz0utR8E7NtMHwJ8mxkdjqOund7B8h5g/Yz2Fb/f56l9HPb7+r7ptwDTzfRIjzML/m6jLqCjP7BTgf/d/GX6QNP2+/T+pwGwH/AX9Dp4bgKO7Nv2A812dwNvGpfagV8B7gC2ALcAb1mBtR9P7/rp9+mdmd3Rt+2/an6nrcBZ41I78A+B25p/5LcBZ6/A2q8FHmr+bmwBNo/Rfp+19jHZ7xf3/Zu8nr7QGPVxZr6Xj7mQJLVWY5+CJGmJDAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1/j/lAAwRmi5yHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_test['Item_Visibility'].plot(kind = 'hist',bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05795674"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = new_test[new_test['Item_Visibility']!=0]['Item_Visibility'].median()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test['Item_Visibility'] = new_test['Item_Visibility'].replace(0.00,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.fit(new_test[['Item_MRP']])\n",
    "sc.fit(new_test[['Item_Weight']])\n",
    "sc.fit(new_test[['Item_Visibility']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test['MRP_Scaled'] = sc.transform(new_test[['Item_MRP']])\n",
    "new_test['Item_Weight_Scaled'] = sc.transform(new_test[['Item_Weight']])\n",
    "new_test['Item_Visibility_Scaled'] = sc.transform(new_test[['Item_Visibility']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_test = new_test.drop([ 'Item_MRP',\n",
    "                            'Item_Weight', 'Item_Visibility',\n",
    "                            'Item_Type', 'Item_Fat_Content',\n",
    "                            'Outlet_Identifier', 'Outlet_Location_Type',\n",
    "                           'Outlet_Type', 'Outlet_Size'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Outlet_Establishment_Year', 'Item_Fat_Content_Low Fat',\n",
       "       'Item_Fat_Content_Regular', 'Item_Fat_Content_low fat',\n",
       "       'Item_Fat_Content_reg', 'Item_Type_Breads', 'Item_Type_Breakfast',\n",
       "       'Item_Type_Canned', 'Item_Type_Dairy', 'Item_Type_Frozen Foods',\n",
       "       'Item_Type_Fruits and Vegetables', 'Item_Type_Hard Drinks',\n",
       "       'Item_Type_Health and Hygiene', 'Item_Type_Household', 'Item_Type_Meat',\n",
       "       'Item_Type_Others', 'Item_Type_Seafood', 'Item_Type_Snack Foods',\n",
       "       'Item_Type_Soft Drinks', 'Item_Type_Starchy Foods',\n",
       "       'Outlet_Identifier_OUT013', 'Outlet_Identifier_OUT017',\n",
       "       'Outlet_Identifier_OUT018', 'Outlet_Identifier_OUT019',\n",
       "       'Outlet_Identifier_OUT027', 'Outlet_Identifier_OUT035',\n",
       "       'Outlet_Identifier_OUT045', 'Outlet_Identifier_OUT046',\n",
       "       'Outlet_Identifier_OUT049', 'Outlet_Size_Medium', 'Outlet_Size_Small',\n",
       "       'Outlet_Location_Type_Tier 2', 'Outlet_Location_Type_Tier 3',\n",
       "       'Outlet_Type_Supermarket Type1', 'Outlet_Type_Supermarket Type2',\n",
       "       'Outlet_Type_Supermarket Type3', 'MRP_Scaled', 'Item_Weight_Scaled',\n",
       "       'Item_Visibility_Scaled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Outlet_Establishment_Year', 'Item_Fat_Content_Low Fat',\n",
       "       'Item_Fat_Content_Regular', 'Item_Fat_Content_low fat',\n",
       "       'Item_Fat_Content_reg', 'Item_Type_Breads', 'Item_Type_Breakfast',\n",
       "       'Item_Type_Canned', 'Item_Type_Dairy', 'Item_Type_Frozen Foods',\n",
       "       'Item_Type_Fruits and Vegetables', 'Item_Type_Hard Drinks',\n",
       "       'Item_Type_Health and Hygiene', 'Item_Type_Household', 'Item_Type_Meat',\n",
       "       'Item_Type_Others', 'Item_Type_Seafood', 'Item_Type_Snack Foods',\n",
       "       'Item_Type_Soft Drinks', 'Item_Type_Starchy Foods',\n",
       "       'Outlet_Identifier_OUT013', 'Outlet_Identifier_OUT017',\n",
       "       'Outlet_Identifier_OUT018', 'Outlet_Identifier_OUT019',\n",
       "       'Outlet_Identifier_OUT027', 'Outlet_Identifier_OUT035',\n",
       "       'Outlet_Identifier_OUT045', 'Outlet_Identifier_OUT046',\n",
       "       'Outlet_Identifier_OUT049', 'Outlet_Size_Medium', 'Outlet_Size_Small',\n",
       "       'Outlet_Location_Type_Tier 2', 'Outlet_Location_Type_Tier 3',\n",
       "       'Outlet_Type_Supermarket Type1', 'Outlet_Type_Supermarket Type2',\n",
       "       'Outlet_Type_Supermarket Type3', 'MRP_Scaled', 'Item_Weight_Scaled',\n",
       "       'Item_Visibility_Scaled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = xgb_fit.predict(new_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = abs(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({'Item_Identifier':test['Item_Identifier'], 'Outlet_Identifier':test['Outlet_Identifier'], 'Item_Outlet_Sales':preds})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"Submit2.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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