Sales Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UNDERSTANDING OF THE PROBLEM STATEMENT:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the quote, \"Success in sales is the sum of small efforts, repeated day in & day out\"\n",
    "\n",
    "Let us consider a supermarket has several outlets or several stores around the world & they want us to predict the sales which they can expect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### APPLICATION OF PREDICTING THE SALES:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can tell the company what are all the challenges they may face\n",
    "\n",
    "What are the brands or products which is sold the most & other such kind of things\n",
    "\n",
    "This helps sales team to understand which product to sell & which product to promote & other such kind of things\n",
    "\n",
    "They can also make several marketing plans(let's say that a particular product in a particular store is getting sold the most & we may find some insights from it - as of why this product is getting sold the most & this helps the company to make better marketing decisions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable - Description\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Item_Identifier - Unique product ID\n",
    "\n",
    "Item_Weight - Weight of product\n",
    "\n",
    "Item_Fat_Content - Whether the product is low fat or not\n",
    "\n",
    "Item_Visibility - The % of total display area of all products in a store allocated to the particular product\n",
    "\n",
    "Item_Type - The category to which the product belongs\n",
    "\n",
    "Item_MRP - Maximum Retail Price (list price) of the product\n",
    "\n",
    "Outlet_Identifier - Unique store ID\n",
    "\n",
    "Outlet_Establishment_Year - The year in which store was established\n",
    "\n",
    "Outlet_Size - The size of the store in terms of ground area covered\n",
    "\n",
    "Outlet_Location_Type - The type of city in which the store is located\n",
    "\n",
    "Outlet_Type - Whether the outlet is just a grocery store or some sort of supermarket\n",
    "\n",
    "Item_Outlet_Sales - Sales of the product in the particular store. This is the outcome variable to be predicted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "import warnings\n",
    "np.random.seed(0)\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Collection and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data = pd.read_csv('Downloads/archive (2)/Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.920</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.200</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.930</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>5</th>\n",
       "      <td>FDP36</td>\n",
       "      <td>10.395</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>51.4008</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>556.6088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FDO10</td>\n",
       "      <td>13.650</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.012741</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>57.6588</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>343.5528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FDP10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.127470</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.7622</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>4022.7636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FDH17</td>\n",
       "      <td>16.200</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.016687</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>96.9726</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>1076.5986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FDU28</td>\n",
       "      <td>19.200</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.094450</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>187.8214</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4710.5350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  ...  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales\n",
       "0           FDA15        9.300          Low Fat  ...                Tier 1  Supermarket Type1          3735.1380\n",
       "1           DRC01        5.920          Regular  ...                Tier 3  Supermarket Type2           443.4228\n",
       "2           FDN15       17.500          Low Fat  ...                Tier 1  Supermarket Type1          2097.2700\n",
       "3           FDX07       19.200          Regular  ...                Tier 3      Grocery Store           732.3800\n",
       "4           NCD19        8.930          Low Fat  ...                Tier 3  Supermarket Type1           994.7052\n",
       "5           FDP36       10.395          Regular  ...                Tier 3  Supermarket Type2           556.6088\n",
       "6           FDO10       13.650          Regular  ...                Tier 3  Supermarket Type1           343.5528\n",
       "7           FDP10          NaN          Low Fat  ...                Tier 3  Supermarket Type3          4022.7636\n",
       "8           FDH17       16.200          Regular  ...                Tier 2  Supermarket Type1          1076.5986\n",
       "9           FDU28       19.200          Regular  ...                Tier 2  Supermarket Type1          4710.5350\n",
       "\n",
       "[10 rows x 12 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    }
   ],
   "source": [
    "bigmart_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = bigmart_data.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent= (bigmart_data.isnull().sum()/bigmart_data.isnull().count()  * 100).sort_values(ascending=False)  # calculating percentage of NAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_data = pd.concat([total,percent],axis=1,keys=['Total','Percent']) # concatenating total and percent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Percent</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>2410</td>\n",
       "      <td>28.276428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Weight</th>\n",
       "      <td>1463</td>\n",
       "      <td>17.165317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_MRP</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Visibility</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Total    Percent\n",
       "Outlet_Size                 2410  28.276428\n",
       "Item_Weight                 1463  17.165317\n",
       "Item_Outlet_Sales              0   0.000000\n",
       "Outlet_Type                    0   0.000000\n",
       "Outlet_Location_Type           0   0.000000\n",
       "Outlet_Establishment_Year      0   0.000000\n",
       "Outlet_Identifier              0   0.000000\n",
       "Item_MRP                       0   0.000000\n",
       "Item_Type                      0   0.000000\n",
       "Item_Visibility                0   0.000000\n",
       "Item_Fat_Content               0   0.000000\n",
       "Item_Identifier                0   0.000000"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data.Item_Weight.fillna(bigmart_data.Item_Weight.mean(),inplace=True) #filling the missing values with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "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                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Identifier',\n",
       " 'Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_list = bigmart_data.select_dtypes(include=['object']).columns.to_list()\n",
    "col_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Table for Outlet size and Outlet Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing missing values with the mode in Outlet type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>LF</th>\n",
       "      <th>Low Fat</th>\n",
       "      <th>Regular</th>\n",
       "      <th>low fat</th>\n",
       "      <th>reg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <td>FDQ16</td>\n",
       "      <td>DRN47</td>\n",
       "      <td>FDG33</td>\n",
       "      <td>DRH03</td>\n",
       "      <td>FDB26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Item_Fat_Content     LF Low Fat Regular low fat    reg\n",
       "Item_Identifier   FDQ16   DRN47   FDG33   DRH03  FDB26"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.pivot_table(values='Item_Identifier', columns = 'Item_Fat_Content', aggfunc=(lambda x:x.mode()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data['Outlet_Size'].fillna(bigmart_data['Outlet_Size'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mode_of_outlet_size = bigmart_data.pivot_table(values='Outlet_Size', columns = 'Outlet_Type', aggfunc=(lambda x:x.mode()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <th>Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>Small</td>\n",
       "      <td>Small</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2 Supermarket Type3\n",
       "Outlet_Size         Small             Small            Medium            Medium"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mode_of_outlet_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values = bigmart_data.Outlet_Size.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "8518    False\n",
       "8519    False\n",
       "8520    False\n",
       "8521    False\n",
       "8522    False\n",
       "Name: Outlet_Size, Length: 8523, dtype: bool"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bigmart_data.loc[missing_values,'Outlet_Size'] = bigmart_data.loc[missing_values,'Outlet_Type'].apply(lambda x : mode_of_outlet_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              0\n",
       "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",
       "Item_Outlet_Sales            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.857645    1463\n",
       "12.150000      86\n",
       "17.600000      82\n",
       "13.650000      77\n",
       "11.800000      76\n",
       "             ... \n",
       "7.560000        2\n",
       "9.420000        1\n",
       "5.400000        1\n",
       "6.520000        1\n",
       "7.685000        1\n",
       "Name: Item_Weight, Length: 416, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.Item_Weight.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>140.992782</td>\n",
       "      <td>1997.831867</td>\n",
       "      <td>2181.288914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.226124</td>\n",
       "      <td>0.051598</td>\n",
       "      <td>62.275067</td>\n",
       "      <td>8.371760</td>\n",
       "      <td>1706.499616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.290000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>33.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.310000</td>\n",
       "      <td>0.026989</td>\n",
       "      <td>93.826500</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>834.247400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>143.012800</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1794.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.094585</td>\n",
       "      <td>185.643700</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3101.296400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>266.888400</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>13086.964800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  Item_Outlet_Sales\n",
       "count  8523.000000      8523.000000  8523.000000                8523.000000        8523.000000\n",
       "mean     12.857645         0.066132   140.992782                1997.831867        2181.288914\n",
       "std       4.226124         0.051598    62.275067                   8.371760        1706.499616\n",
       "min       4.555000         0.000000    31.290000                1985.000000          33.290000\n",
       "25%       9.310000         0.026989    93.826500                1987.000000         834.247400\n",
       "50%      12.857645         0.053931   143.012800                1999.000000        1794.331000\n",
       "75%      16.000000         0.094585   185.643700                2004.000000        3101.296400\n",
       "max      21.350000         0.328391   266.888400                2009.000000       13086.964800"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saurabh.tiwari08\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(bigmart_data['Item_Weight'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saurabh.tiwari08\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(bigmart_data['Item_Visibility'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saurabh.tiwari08\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(bigmart_data['Item_MRP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.distplot(bigmart_data['Item_Outlet_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is skewed towards right means positively skewed.\n",
    "Show peakness.\n",
    "Deviate from normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness  1.1775306028542798\n",
      "Kurtosis  1.6158766814287264\n"
     ]
    }
   ],
   "source": [
    "# Let's check for skewness and kurtosis\n",
    "print(\"Skewness \", bigmart_data['Item_Outlet_Sales'].skew())\n",
    "print(\"Kurtosis \", bigmart_data['Item_Outlet_Sales'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(x='Outlet_Establishment_Year', data=bigmart_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.countplot(x='Item_Fat_Content', data=bigmart_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NuvXxwcqqXMcAAAAAAD7ntqnEvuWWW+LOO++MNm3a1C3Ly8uLxx9/vNGCNZX85oVR8fN7ch0jc4rPGxQRSmwAAAAAILe2qcR+6KGH4ve//33ss88+jZ0HAAAAAADqNNuWlfbdd18FNgAAAAAATW6bvondpUuXuP766+O73/1utGjRom75rnBNbAAAAAAAsmubSuzp06dHRERZWVndsl3lmtgAAAAAAGTXNpXYTzzxRGPnAAAAAACAzWxTiT116tQtLv/3f//3Bg0DAAAAAAAb26YS+69//Wvd39etWxfz5s2LLl26NFooAAAAAACI2MYSe/z48ZvcLi8vj9GjRzdKIABg+/3LXs2jqLAo1zEyp3p9dfxjxbpcxwCAnNtzr92jeWF+rmNkzrr1NbFyxdpcxwDgU2xTif1J++yzTyxdurShswAAO6iosCguv79PrmNkzsQTyyJCiQ0AzQvzY+KD7+U6RuZcfsK+uY4AwDbY7mtip5Ri4cKF0aZNm0YLBQAAAAAAETtwTeyIiH333Tcuu+yyRgkEAAAAAAAf265rYi9dujQ2bNgQ+++/f6OGAgAAAACAiG0ssRcvXhw//OEPY9myZVFbWxutW7eOX/ziF/GVr3ylsfMBAAAAAPA51mxbVrr66qvjrLPOinnz5sX8+fPjvPPOi3HjxjV2NgAAAAAAPue2qcRevnx5nHDCCXW3Bw4cGB9++GGjhQIAAAAAgIhtLLFrampixYoVdbc/+OCDndroE088EaWlpXHsscfGNddcExERc+bMiX79+kWvXr1i8uTJdesuWrQoSktLo3fv3jF69OjYsGHDTm0bAAAAAIDPjm0qsQcNGhQnnXRS3HTTTXHzzTfHKaecEqeccsoObfCdd96JK6+8MqZMmRIzZ86M1157LZ5++ukYNWpUTJkyJWbNmhULFy6Mp59+OiIihg8fHmPHjo3Zs2dHSimmTZu2Q9sFAAAAAOCzZ5tK7O7du0dExPr16+PNN9+M8vLyOOaYY3Zog4899lgcd9xx0bZt2ygsLIzJkyfHbrvtFvvvv3+0b98+CgoKol+/flFWVhZLly6Nqqqq6Ny5c0RElJaWRllZ2Q5tFwAAAACAz56CbVlpxIgRcdppp8WQIUOiuro67rvvvhg1alTcfvvt273BxYsXR2FhYZx77rnx3nvvxdFHHx0HHHBAFBcX161TUlIS5eXlsWzZsk2WFxcXR3l5+XZvEwAAAACAz6ZtKrE//PDDGDJkSEREFBUVxRlnnBEPPfTQDm2wpqYmXnrppbj77rtj9913j/POOy9atGgReXl5deuklCIvLy9qa2u3uHx7tGnTcodyElFc3CrXEYAMMSc0HmPbuIxv4zG2jcfYAjQdcy5A9m1TiV1TUxPl5eWxzz77REREZWVlpJR2aINf+MIXokuXLrH33ntHRMT3vve9KCsri/z8/Lp1KioqoqSkJNq2bRsVFRV1yysrK6OkpGS7trd8+eqora0/q4NV/SoqVuU6AjQ5c0L9GmJOML5bZmwb186Or7Gtn7FtPM7DgIZmzq2fORcg95o1y9vql5G36ZrYZ5xxRnz/+9+Pyy67LC6//PI44YQT4qyzztqhQD169Ijnnnsu/vGPf0RNTU08++yz0adPn3jrrbdi8eLFUVNTE4888kh069Yt2rVrF0VFRTF//vyIiJgxY0Z069Zth7YLAAAAAMBnzzZ9E/vEE0+Mgw46KF544YXIz8+PM888Mzp06LBDGzz00EPjrLPOilNPPTXWr18fXbt2jVNOOSW+/OUvx7Bhw6K6ujq6d+8effr0iYiISZMmxZgxY2L16tXRqVOnusuaAAAAAACw69umEjsiomPHjtGxY8cG2eiJJ54YJ5544ibLunTpEjNnztzidu+///4G2S4AAAAAAJ8t23Q5EQAAAAAAyAUlNgAAAAAAmaXEBgAAAAAgs5TYAAAAAABklhIbAAAAAIDMUmIDAAAAAJBZSmwAAAAAADJLiQ0AAAAAQGYpsQEAAAAAyCwlNgAAAAAAmaXEBgAAAAAgs5TYAAAAAABklhIbAAAAAIDMUmIDAAAAAJBZSmwAAAAAADKrINcBAAAAPkta7bVbtCj0UWpjVes3xKoVH+U6BgCwi3LmBQAAsB1aFBbEgPsfzXWMTJlx4rGxKtchAIBdlsuJAAAAAACQWUpsAAAAAAAyS4kNAAAAAEBmKbEBAAAAAMgsJTYAAAAAAJmlxAYAAAAAILOU2AAAAAAAZJYSGwAAAACAzFJiAwAAAACQWUpsAAAAAAAyS4kNAAAAAEBmKbEBAAAAAMgsJTYAAAAAAJmlxAYAAAAAILOU2AAAAAAAZJYSGwAAAACAzFJiAwAAAACQWQW5DsCua+89iyK/efNcx8icmnXr4oOV1bmOAQAAAACfCUpsGk1+8+bx3pTLcx0jc/b94cSIUGIDAAAAwLZwOREAAAAAADJLiQ0AAAAAQGYpsQEAAAAAyCzXxIbPoNZ7No+C5kW5jpE5G9ZVx4cr1+U6BgAAAAANSIkNn0EFzYti3i/65TpG5nz7nIcjQokNAAAAsCtxOREAAAAAADIrpyX2xIkTY8SIERERMWfOnOjXr1/06tUrJk+eXLfOokWLorS0NHr37h2jR4+ODRs25CouAAAAAABNLGcl9vPPPx8PPvhgRERUVVXFqFGjYsqUKTFr1qxYuHBhPP300xERMXz48Bg7dmzMnj07Ukoxbdq0XEUGAAAAAKCJ5aTEXrFiRUyePDnOPffciIhYsGBB7L///tG+ffsoKCiIfv36RVlZWSxdujSqqqqic+fOERFRWloaZWVluYgMAAAAAEAO5OR/7Dh27Ni45JJL4r333ouIiGXLlkVxcXHdz0tKSqK8vHyz5cXFxVFeXr5d22rTpmXDhP4cKi5ulesIuyxj23iMbeMxto3H2DYu49t4jG3jMbZ8Ftlv+ayy7wJkX5OX2L/97W9j3333jS5dusT06dMjIqK2tjby8vLq1kkpRV5eXr3Lt8fy5aujtjbV+3MHq/pVVKzaqfsb2/oZ28ZjbBvPzo5thPGtj7FtXOaFxmNsG09DzAs0Hvvultlvs81+Wz/7LkDuNWuWt9UvIzd5iT1r1qyoqKiIAQMGxMqVK2Pt2rWxdOnSyM/Pr1unoqIiSkpKom3btlFRUVG3vLKyMkpKSpo6MgAAAAAAOdLkJfbUqVPr/j59+vSYO3dujBs3Lnr16hWLFy+O/fbbLx555JEYOHBgtGvXLoqKimL+/PnxzW9+M2bMmBHdunVr6sgAAAAAAORITq6J/UlFRUUxYcKEGDZsWFRXV0f37t2jT58+ERExadKkGDNmTKxevTo6deoUQ4YMyXFaAAAAAACaSk5L7NLS0igtLY2IiC5dusTMmTM3W6djx45x//33N3U0AAAAAAAyoFmuAwAAAAAAQH2U2AAAAAAAZJYSGwAAAACAzFJiAwAAAACQWUpsAAAAAAAyS4kNAAAAAEBmKbEBAAAAAMgsJTYAAAAAAJmlxAYAAAAAILOU2AAAAAAAZJYSGwAAAACAzFJiAwAAAACQWUpsAAAAAAAyS4kNAAAAAEBmKbEBAAAAAMgsJTYAAAAAAJmlxAYAAAAAILOU2AAAAAAAZJYSGwAAAACAzFJiAwAAAACQWUpsAAAAAAAyS4kNAAAAAEBmKbEBAAAAAMgsJTYAAAAAAJmlxAYAAAAAILOU2AAAAAAAZJYSGwAAAACAzFJiAwAAAACQWUpsAAAAAAAyS4kNAAAAAEBmKbEBAAAAAMgsJTYAAAAAAJlVkOsAAAAAAJ9Ve+21RxQW+o7gJ61fXxsrVqzJdQxgF6HEBgAAANhBhYXN4tH/rsx1jMw59qQv5DoCsAvxq0IAAAAAADJLiQ0AAAAAQGYpsQEAAAAAyCwlNgAAAAAAmaXEBgAAAAAgs5TYAAAAAABklhIbAAAAAIDMKsjFRm+77bZ49NFHIyKie/fucdlll8WcOXNi/PjxUV1dHccee2xccsklERGxaNGiGD16dKxZsya+9a1vxbhx46KgICexAQAAaESt9to9WhTm5zpG5lStr4lVK9bmOgYA5EyTt8Fz5syJ5557Lh588MHIy8uLs846Kx555JGYNGlS3H333bHvvvvGOeecE08//XR07949hg8fHtdcc0107tw5Ro0aFdOmTYtTTz21qWMDAADQyFoU5seJD/wp1zEy5/6B34hVuQ4BADnU5JcTKS4ujhEjRkTz5s2jsLAwvvKVr8Tbb78d+++/f7Rv3z4KCgqiX79+UVZWFkuXLo2qqqro3LlzRESUlpZGWVlZU0cGAAAAACBHmrzEPuCAA+pK6bfffjseffTRyMvLi+Li4rp1SkpKory8PJYtW7bJ8uLi4igvL2/qyAAAAAAA5EjOLi79xhtvxDnnnBOXXXZZ5Ofnx9tvv133s5RS5OXlRW1tbeTl5W22fHu0adOyoSJ/7hQXt8p1hF2WsW08xrbxGNvGY2wbl/FtPMa28RhbPovst43L+DYeY9t4jC3QUHJSYs+fPz8uvPDCGDVqVBx//PExd+7cqKioqPt5RUVFlJSURNu2bTdZXllZGSUlJdu1reXLV0dtbar35ybU+lVU7NxV14xt/Yxt4zG2jWdnxzbC+NbH2DYu80LjMbaNpyHmBRqPfXfLHM8alzm38RjbxuN4BmyrZs3ytvpl5Ca/nMh7770X559/fkyaNCmOP/74iIg49NBD46233orFixdHTU1NPPLII9GtW7do165dFBUVxfz58yMiYsaMGdGtW7emjgwAAAAAQI40+Tex77zzzqiuro4JEybULTv55JNjwoQJMWzYsKiuro7u3btHnz59IiJi0qRJMWbMmFi9enV06tQphgwZ0tSRAQAAAADIkSYvsceMGRNjxozZ4s9mzpy52bKOHTvG/fff39ixAAAAAADIoCa/nAgAAAAAAGwrJTYAAAAAAJmlxAYAAAAAILOa/JrYAAAAAEBu7b3n7pHfPD/XMTKlZl1NfLByba5jsAVKbAAAAAD4nMlvnh/v3/B6rmNkStsfdcx1BOrhciIAAAAAAGSWEhsAAAAAgMxSYgMAAAAAkFmuiQ0AAABA5rTec48oaO77l5+0YV1tfLhyTa5jQJNSYgMAAACQOQXNm8Wr/1me6xiZ0+ncfXIdAZqcX2cBAAAAAJBZSmwAAAAAADJLiQ0AAAAAQGYpsQEAAAAAyCwlNgAAAAAAmaXEBgAAAAAgs5TYAAAAAABkVkGuAwAAAA2r1V4tokVhYa5jZE7V+vWxakVVrmMAALCdlNgAALCLaVFYGH3v/3WuY2TOIyeeFqtCiQ0A8FnjciIAAAAAAGSWEhsAAAAAgMxSYgMAAAAAkFlKbAAAAAAAMkuJDQAAAABAZimxAQAAAADILCU2AAAAAACZpcQGAAAAACCzlNgAAAAAAGSWEhsAAAAAgMwqyHUAAAAAAIBdxd577hb5zdWuG6tZtyE+WPnRDt/faAIAAAAANJD85gWx7NYncx0jU0qG9dip+7ucCAAAAAAAmaXEBgAAAAAgs5TYAAAAAABklhIbAAAAAIDMUmIDAAAAAJBZSmwAAAAAADJLiQ0AAAAAQGYpsQEAAAAAyCwlNgAAAAAAmaXEBgAAAAAgsz4TJfbDDz8cxx13XPTq1St+/etf5zoOAAAAAABNpCDXAT5NeXl5TJ48OaZPnx7NmzePk08+OQ4//PD46le/mutoAAAAAAA0ssx/E3vOnDlxxBFHxF577RW777579O7dO8rKynIdCwAAAACAJpD5b2IvW7YsiouL626XlJTEggULtvn+zZrlffo6rfbYoWy7um0Zu0+T36p1AyTZ9TTE2DZvWdIASXY9DTG2uxnbLWqIsY2IaLnHPg3yOLuShhrb1rsb2y1piPEt2d3xbEsaZmxbNUCSXU/DjK1z3C1pqDm3ZPfdGuRxdiUNNbbFuzdvkMfZ1TTE+P7L7vkNkGTX0yCfIXbP/HcEc6IhxrawlbHdkoaac/P/pbBBHmdX0lBj26xViwZ5nF3J1sb208Y9L6WUGjpQQ/r5z38e1dXVcfHFF0dExLRp02LhwoVx9dVX5zgZAAAAAACNLfO/zmrbtm1UVFTU3a6oqIiSEt+SBI8CJEgAACAASURBVAAAAAD4PMh8iX3kkUfG888/Hx988EF89NFH8fvf/z66deuW61gAAAAAADSBzF8Te5999olLLrkkhgwZEuvXr48TTzwxDjnkkFzHAgAAAACgCWT+mtgAAAAAAHx+Zf5yIgAAAAAAfH4psQEAAAAAyCwlNgAAAAAAmaXEBgAAAAAgs5TYAAAAAABklhK7ES1ZsiQOOuigGDBgQAwYMCB69+4dI0eOjMrKynrvU15eHkOHDm3ClLlVVlYWpaWl0b9//+jXr1/ccccdn3qfV199NY4++ug47bTT4sknn4ypU6duts706dPjsMMOiwEDBkT//v2jT58+cdttt0VNTc0WH3P06NHx5z//ud5tLlmyJHr27LntT+wzbsmSJXHggQfG2LFjN1m+aNGiOPDAA2P69Onb/ZjTpk2LRx55pKEibuaT77eP/7z33nvb/Bgf7werVq2K888/v9GyflLPnj1jyZIldbdra2ujR48e8fLLL2+y3pIlS6Jr166xfv36Btnutj7PAw88cIvLBw8eHC+++GKDZNlYfe+3+nI0pBEjRmzX/j19+vQYMWLEZstffPHFGDx4cENG+1Qvvvhi/Ou//mvdvHfsscfGr371q0+935Zexz//+c8xevToBsn1wQcfxOjRo+OYY46JY489Nk444YR4/PHHG+SxI5pmv/ikHTl2bY9tOebceuut0bVr103mu5EjRzbJthtaQ8zfjaFnz55x3HHHbZLpscce2+nHvfXWW+PWW29tgIQNb0fnkW3xyWNdRERNTU2ceeaZ0bt37+0+njTUeUVDH3M2fryNz1Ob4nWvbxtbGvsdPYbfd999cd999+1wxp3RWOcHWxqfT74XevfuHdddd12sWbNmi49x8803f+qxLRfHq8awevXqGDduXPTt2zcGDBgQgwcPjldffbXJc+yqn88acx7+5Haa+lx1Z2zt3OuWW26Jl156aae3Ud95/bYYMWJEHH300ZucM0yePHmnM33WXqc1a9bEuHHj4phjjon+/fvHqaeeGs8//3xEbHrc3t7PXJ9HDfV54913343evXvHgAEDYvXq1TuVKQuvW0FOt/45UFJSEjNmzIiIiJRS3HjjjXHhhRfGvffeu8X199lnn7j99tubMmLOlJeXx8SJE2P69OnRunXrWLNmTQwePDi+9KUvxXe/+9167/fkk09G//7949JLL93qh4GePXvGhAkTIiJi7dq18cMf/jBuvfXWuPjiizdb99prr935J7SL2WuvveLZZ5+NmpqayM/Pj4iIWbNmxd57771Dj/enP/0pDjvssIaMuJmN32874uP9YMmSJbFo0aKGirXdmjVrFt///vfj4Ycfjs6dO9ctnzFjRvTv3z8KCwsbZDsrV67M6fOk4R100EFx9913R8Q/P2Qef/zx0bVr1/jqV7+6XY9z8MEHx8EHH7zTedatWxenn3569O7dO8rKyiI/Pz/+93//N84888xo165ddOzYcae30dR29NjVGE4++eQYNmxYk26zsezs/N1YfvnLX8Z+++2X6xhNqqHmkW1RXl4ef/nLX+K5557b7vs2xXnFzlq4cGGuIzS4U045JdcRmszG74X169fHqFGj4qqrroqf/vSnm6170UUXNXW8nKitrY2hQ4fG4YcfHg899FAUFBTECy+8EEOHDo3f/e530bp161xH3CU05Tz8WfBp517z5s2Lww8/PNcx48ILL4zS0tJcx8iZlFKce+658bWvfS1+97vfRfPmzeO1116Ls88+O2644YbPxHE7Kxry88bcuXPjoIMOihtuuKGR0jYtJXYTysvLi2HDhkXXrl3j9ddfj3vuuSfeeOONqKysjAMPPDBuvPHGqKysjCFDhsQTTzwRI0aMiBUrVsTixYvjRz/6Udxxxx3xm9/8JiL++VvCV155JcaNG5fjZ7XjPvzww1i/fn1UVVVFRMQee+wREyZMiKKiooiIePnll+Paa6+N6urqaN26dVx99dXx9ttv1337o3nz5nXj8cUvfjEGDhxY77Z23333uPTSS2Po0KFx0UUXxW233RYvv/xyvPfeezFo0KB49NFH44ILLoiIiF/84hfRokWLePPNN+PAAw+MSZMmbfJYs2fPjp/97GfxX//1X/HHP/4x7rjjjsjPz4/99tsvfvrTn9bl/6zbY489omPHjjFv3rw44ogjIiLij3/8Yxx55JEREfHMM8/ELbfcEhs2bIj99tsvfvKTn0Tr1q3j0UcfjalTp0ZVVVWsW7currvuuqiqqoonnngiXnjhhSguLo7vfOc7TfpcNn4vDR8+PK655pq46667Yr/99osXX3wxbrvttrj77rtj8ODBccEFF8TUqVNj2bJlcf7558fEiRPj0ksvrfsXFOeff/5mB465c+fG5MmTo6qqKv7xj3/EyJEj43vf+16MGDEiWrZsGa+++mqUl5fH+eefHwMHDowVK1bE8OHD4/3334+vfOUrUV1dvVnm0tLSOPnkk2PUqFF1v0SYOXNmTJkyJSorK2Ps2LHx/vvvR15eXvzoRz+KI488MlatWhWXXXZZ/P3vf4/27dvH+++/H7fddlvsu+++cf3118fcuXOjpqYmSktL44wzzohrrrmm7nn+7Gc/i8mTJ8fzzz8fK1eujJKSkpg8eXJ84QtfiIiIK664IhYsWBCtW7eO6667Lr74xS9ukveXv/xlPProo1FTUxNHHXVUDB8+PNasWfOpY7e9amtr47rrrovnn38+8vLyon///nH22Wdv8jp+/Jofdthh0atXry1mWLx4cVx11VWxYsWKaNGiRVxxxRXx9a9/PSIinnrqqbj33ntj+fLlce6558ZJJ50UH330UYwZMyb+8pe/RF5eXpx55pnx/e9/f5Nszz33XIwfPz6KioriS1/60k49z4ZQXV0d+fn50apVq4iILb43v/GNb9Stv3z58jj99NPj4osvjlatWm3yvjj44INj/vz58cEHH8SYMWOie/fu8f7778ePf/zjWLlyZXTo0CHmzZsXzzzzzCYZZs+eHUVFRXXza0TEl7/85bjqqqvq/mXMlub6/fffP956660YO3ZsrFixInbfffcYPXp0HHLIIbFkyZIYPnx4rF27Ng499NC6x33++efrioU999wzbrjhhh3+pdvWfNqxq2fPntG/f/947rnn4qOPPoqJEyfGQQcdVO88sXTp0hg5cmR88MEH0aJFi7jmmmuiZcuWm4zhx8ecbX0+Tz75ZNx0001RW1sb7du3j6uvvjq+8IUv1DvWr732Wt037zf+xcLDDz+c82PcJ+fvvffee7PnsO+++8YPfvCDuvssWbIkBgwYEGPHjt3i3LR06dK44IIL4oADDohFixZFmzZt4uabb4699tprmzLVNw9FRPznf/5nzJw5M/Lz86Nr164xfPjwyM/PjzvuuCOmTZsWrVu3jn/5l3+JQw45pK4Ue+ONNyIi4tRTT41/+7d/a/hB3AkbzyM9e/aMQw45JBYtWhT33ntvPPvss/GrX/0qamtro1OnTnHllVdGUVFR3HPPPTFjxoz46KOPorCwMG644Yb48pe/XPeYb731Vpxzzjlx/fXXxxVXXBErVqyI0tLSmDZtWlx11VWbnRtv2LBhs3l8t912a7Lzipqami0eQzds2LDFvB/729/+tsl5akTEggUL4uSTT47y8vIoLS3d7JdQq1evjlGjRkV5eXksW7YsunTpEtdee23MnTt3i+enzZs33+K+tT1uuummSCnFJZdcEhH/fM9169YtvvGNb2xxjv/4CyTDhg2r91ywvnlwa8fehrC18fvpT38atbW1ccABB8TIkSM/9TzskwoLC+Oyyy6Lo48+Oq644or4wx/+EA8++GCsWLEievToEcuWLYvDDjssDjvssE+dX/70pz/FiBEj4vbbb4933323SY5dDeXFF1+M9957Ly688MJo1uyf/6j7iCOOiPHjx0dtbW2MGTNmi59v6xuTo446Knr37h3z58+P/Pz8uOmmm6J9+/axYMGCGD9+fFRVVUXr1q1j3Lhx0b59+3qPV7uyhpyH6ztXnTp1ajz44IPRrFmzOOSQQ+Lqq6/O4TPe3NbOvR566KFYuHBhjBkzJm677bZYuXJlvZ/LNj6faNGiRUyYMCFSSvHFL36xruBbvHhxDB48ON59993o0qVLXHPNNTF8+PD49re/XXeMHjx4cPz4xz/e5Dx0ax544IGYOnVq5OXlRadOneKKK66IPfbYo97ztc/q6zR37tx4991346677oq8vLyIiPj6178e5513XpxxxhnRqlWruuN2xJY/c61ZsyauvvrqeOONN6KmpiaGDh0affv2jenTp28y5x5wwAE5P0dtTFvb5+ubH7f0WaNdu3Zx0003xdq1a2Ps2LExcuTILX6mre/cNqUUEyZMiKeeeipKSkqipqYm97+ISDSad955J/Xo0WOz5QMHDky33npruuqqq1JKKdXU1KRBgwalsrKyTe5z+eWXp8svvzyllFJtbW3q2bNnWrx4cUoppcGDB6eXX365iZ5J4xk7dmz6+te/ngYOHJiuv/76tGjRopRSStXV1alHjx7plVdeSSmlNGvWrFRaWppSSumWW25Jt9xyy2Z/39gDDzxQN3YfW7t2berQoUOqrKxMt9xySxo0aFDdzwYNGpReeOGF9MILL6TOnTun9957L9XU1KSBAwemxx9/vO51efbZZ1O/fv1SRUVFSimlnj17psrKypRSShMmTEivvfZaA49Qbnz8fGfOnFm3n77yyitpxIgR6fLLL0+333576t+/f1qxYkVKKaX77rsvjRo1KtXU1KQhQ4ak5cuXp5RS+u1vf5vOOeeclNI/9+cHHnigUTN36tQp9e/fv+7P7bffXrftjfeHHj16pHfeeSellNILL7xQty98vB9s/D6cPn163Ri89tpracKECZtte9iwYelvf/tbSimlOXPmpL59+9Zt9/zzz0+1tbXp9ddfT4cddlhKKaVx48alG2+8MaWU0ty5c1OHDh3q8mxs0KBB6ZlnnkkppfQ///M/6aSTTkoppXTxxRenP/zhDymllMrLy9N3v/vdtGrVqjR+/Pg0ceLElFJKCxYsSF/72tfSO++8k+6999503XXXpZT++d4aNGhQmjdv3ibP8+23304XXHBBqqmpSSmlNHz48HTnnXemlFLq0KFDmjFjRkoppXvuuSf98Ic/3GS8nn766TRs2LC0YcOGVFNTky699NL00EMPbdPYbcmWXsv+/funDh061G1/w4YNae3atWngwIHpySef3OR1/HjsH3jggXoznHTSSenVV19NKaX0xhtvpF69etXd75xzzkm1tbXpL3/5Szr88MNTSilNnDgx/eQnP0kppbR8+fLUs2fPtGjRorq5prq6OnXt2rVuPxg1atQmeZrCx/NX//79U9++fdMhhxySLr/88lRbW7vV9+agQYPSY489lkpLS9Ojjz5a91gbvy+uueaalFJKjz/+eDrhhBNSSildcMEF6Z577kkppfT73/8+dejQYbNMP/nJT7b6um9trh84cGCaPXt2Sumf+//RRx+dqqur09lnn52mTZuWUkrpwQcfrNvuoEGD6h7nl7/8ZXr22Wd3aBy3RX3HrpT+Ob9MnTo1pZTSXXfdlS644IKUUv3zxNChQ+vG8amnnkoXXnhhvcecjd1yyy3pyCOP3OQ98uabb6bKysp01FFH1c0pt99+exo2bNhWx7pv377pueeeSymldNttt9XNC011jNvW+Xtrz+FjL7/8curVq1davnx5vXPTO++8kw488MC6OeCCCy5Id91112a5evTokY499ti6TBdddFFKKdU7Dz311FPpBz/4QVq7dm1av359Ovfcc9M999yTFixYkPr06ZNWr16d1qxZk/r27ZtuueWW9OKLL6ahQ4emlFJ6//330/DhwxtlfLfH1uaRHj161B3H//rXv6ZTTjklVVVVpZRSmjRpUvrZz36WVq1alU4//fT00UcfpZRSuummm9LVV1+dUvrneM6bNy8dd9xxad68eSmlTc+V586du8Vz4/rm8YY6r9jaMSelVO8xtL68Gz+nT56znnDCCam6ujotX748HXrooWnVqlWbZHn44YfTlClT6rb1ve99L/35z3+u9/y0vn3rkz65L/fv3z917tw5vfDCC+nvf/976tGjR6qtrU1r165N3bt3T1VVVfXO8R8/p+XLl2/xXPDj7W1pHqzv2NtQr9XWxu+b3/xm+sc//pFS2rbzsE+eV3zsiCOOSK+88kp64IEH0jHHHJPWr1+fUvq//XFr80uHDh3SokWLUp8+fdKbb76ZUmraY1dDuOOOO+rOHz5pa++JrY3JY489llJKafz48Wn8+PGpuro69evXLy1dujSllNIzzzyTTj/99JRS/cerXUljzcP1natu2LAhHX744WndunWppqYmjRgxIr3//vu5efJbsbVzr48/k6S09c9lG59PdOnSpe68ZtKkSemuu+5KDzzwQOrevXv68MMPU3V1dfrOd76T/vrXv6bnn38+nXrqqSmllJYsWZKOO+64zfJdfvnlqXv37pvMTatWrUqvv/56+t7/b+++w6K60geOf4ehqVhQEmLDErFiiyKDighWqiKoUTGaWFdRs2IXlWhiEkDsuo+b9YlRk4gtFnQ1FlhjxWSxRd2NQtTo6mILASkzzO8Pnrk/kJkBFBHd9/OXDzPOnHvumfe899xzzu3ZU//gwQO9Xq/XR0ZG6j/77DOz+dqrep7++te/6idPnlzk71evXtW3b9++UL9t6porOjpav2HDBr1er9enp6fr/fz89Ddu3CgSc1/XcZiCjLV5c/HRVNsvODZm6prWVG67f/9+fWhoqD4nJ0d///59fZcuXV7omE5JyEzsl0ClUtGyZUvq16/P5s2buX79OqmpqWRmZhZ5r2E2hUqlIigoiN27dzNgwADu379f4jt/FdlHH33EhAkT+OGHH/jhhx8YNGgQMTExNGzYsNBsEh8fH+bPn096evozf5fhbqDhDp2pmSrOzs689dZbALz99ts8fvwYyL8bNmnSJCZNmqTMTvXy8mLIkCH07NmTPn360KJFi2cuX0Xk7e2t3B3ev38/Pj4+7Nu3D1tbW+7cucN7770H5M9Kq169OhYWFqxevZojR46QkpLCmTNnlFka5cHccvTSzkwyaN++PbGxsdy9e5fu3bsb3UM6Ojqao0eP8ve//51z584V2i+xS5cuqFQqmjZtyqNHj4D8u9SGu/2urq7Ur1/f6HcHBwezd+9ePDw82LVrl7La4MSJE1y/fp0VK1YAoNVquXnzJsePH1dWDrRu3ZqmTZsC+TNUL1++zKlTp4D87XWuXr2qtHOABg0aMHPmTLZu3UpKSgrJyck4OTkBYGtrS2BgIAD9+vVj2bJlhcp58uRJzp8/ryyfy8rKUlZHFFd3phg7l82aNeP06dMEBQWhVqupVKkSAQEBnDx50uSeiMbOX0ZGBhcvXiy0f3BmZiYPHz4EoEePHqhUKpydnZW/nTp1isWLFwNQs2ZNevTowZkzZ5QZs1evXuXNN9/k7bffBiAoKIjly5eX+HjLytPLT0ePHs26desYN26c2d/mggULcHBwoHfv3kY/1zDL0dnZWWnHx48f59NPPwWgV69eVKtWrdjyxcTEcOzYMbKysvDw8GDQoEEmY/2NGzeU8rRr147q1atz/fr1Qr+fwMBAIiIigPzzFhYWRs+ePenRowddunQpdf2VlKm+y1DegvV18OBBwHScSEpKUmZvenp64unpya1bt4z2OU8ztp3I0aNHadOmjbIFxuDBg1m3bh2pqalG6/q3337j3r17Sn0NGDCA7du3A+Xbx5Ukfps6hvT0dKpWrcrdu3cJDw9nxYoV1KxZ02Rs6tChA7Vq1VJmgDo7Oyt9/dOMbSdiKg5ZWFjg5+dHpUqVgPwY/t1335GVlYWnpydVqlQBoG/fvsqM0JSUFEaNGkW3bt2YMWPGc9Zi2TAVRwAl9zx9+jS//vqrMistNzeXli1bYmdnx5IlS4iPjyc1NZVjx44VajdTpkyhdevWdOzYscj3urq6UqNGjSK5cUn64edlqs8B033osGHDjJbXHA8PD6ytralZsyb29vY8fvy40MoLf39/zp8/z5dffsn169d59OiR8pnG8tOUlBSjbcuYp9uyYY/V+vXrU7duXZKSkrh9+zaenp7Y2NgUG+PPnTtnNBcseKyGch88eNBs31uaLSjMnStz9deoUSNlZVJJ8zBjVCqVci3RsmVLLC2LXk6biy+jRo2ib9++yuqE8uy7yoKFhYXJ2Y6mfsNgvk4KtpWzZ8+SmprKzZs3+dOf/qS8548//uDBgwcm+6vXzYuIw6ZyVbVaTfv27QkJCaFHjx68//77ODo6voSjNq+43MvA3HWZIX+4evUqjo6OSv8UHh4O5K9279ixo7JywsnJiYcPH+Lm5sa8efO4desWu3btol+/fkbLaGw7kaSkJLy8vJQ4N3jwYGbPnk2nTp2M5muv8nlSqVRGn0GWm5urjMUUZOya68SJE2RlZSm/7czMTGXFWsGY+7qPw4DxNj927Fij8RHMt30DU9e0Z8+eNZrb5ubm0rt3b6ysrKhZsybdunUrn4M3Qwaxy1lOTg4pKSncvHmT5cuX89577zFgwAAePnyIXq8v8n5bW1vl30FBQYwePRpra2uTgfNVkpCQQGZmJr6+vgQHBxMcHExcXBzbtm1j6tSpRd6v1+tNPpixJAyDdoaLhYJ1W1DBxEylUinnRaVSsXr1aqZNm4afnx+Ojo5ERERw5coVEhMTmT59OmFhYa/FuTEwbCny448/curUKcLDw9m3bx86nY533nmHv/zlL0D+MreMjAwyMjIICQkhMDAQV1dXmjVrxubNm1/yUeR7+nwbzqtWqzX7/xo2bMj+/fs5duwYR48eZf369ezbt6/QAODQoUNxc3PDzc0Nd3d3pk2bprxmaE8FO+6C7QpQtgt5Wp8+fYiNjeWPP/4gISFBSbDy8vLYsGGDkmDdu3ePWrVqoVarjcYRnU7H9OnTlSTvwYMHVKlShf/+97/Key5evEh4eDgjR46kT58+WFhYKJ9V8Fj1en2RCzadTseIESN4//33Afj9999Rq9VUqVKl2Lorracvzg1x4ek6NTz80tj527p1K9bW1oUugv/zn/8o9Wk4HwXP2dP1+nQ8Kuk5LU92dnb4+Phw4sSJYn+bY8aMITExkW+++YZhw4YV+Sxj7dhUeyvIxcVFWU4PMG3aNKZNm8aOHTs4c+aM0cEWvV5v9IZlwTovGJcN7WnkyJF4eXlx9OhRoqOjOX/+fKEEr6yY67sMvzFj9WUqThT8Pen1eq5du4atra3RPqckjP1GtFqtybquXLmyybZbUfo4Q/w2dQw6nY7s7GwmTJjApEmTlIESU7Hp4cOHJvv6kjAVh4zRarVFPt/S0pKcnBzs7e2Jj4/n+PHjJCYmEhQURHx8fIluCJWXgnEE/r9t63Q6fHx8lJtIGRkZ6HQ67ty5w/DhwwkNDaVbt244ODgUevbC3LlzWb16NQkJCXTv3r3Qdx0+fJgVK1YUyY1N9cPlxVQfaqq85hT8vRtrdxs3buTAgQMMGjSIzp07869//Ut5j7E2a6ptlZbhpvnt27eVG2PFxXhTuaDB03EwLy/PbN9bFszVX8E88Fn77LS0NNLT03FycuLSpUulvpaA/Ju5M2bMYODAgTRv3rzc+q6y4uLiwtdff620P4PY2FjatGnDypUrjf4mzNVJwbai1+vJy8ujXr16SlvR6XSkpaVVyFyrPJRVHDZXf2vWrCE5OZl//OMfjB49mpiYmJe/ZUABJcm9DMxdlxl+s1ZWVoXab3p6uhK/jMVplUpF//79iY+PZ//+/fztb38rcdlLmpcZ/v4qn6e2bduyceNGcnNzCz3DKTk5GRcXlyLvN3bNlZeXR3R0NK1atQLy42716tXZs2dPoZhbUXLUF8VUm9+zZ4/R+Ajm276BqWvakl5jG7txW97Kb4qkIC8vj5UrV9K2bVtu3ryJj48PwcHBVKtWjdOnTxc7QFu3bl3eeustvv3229fiB2pra8uSJUuUp4Hr9XouX75MixYtaNy4MY8ePeL8+fNA/gMF69SpUyTRVavVxQ5CQn7HtHz5cqODMyVVo0YN3N3dGTJkCB9//DFarZbevXtjb2/PuHHj6Nev32v5kDwfHx+WLFmCi4uLErSys7NJTk4mJSUFyO9Qo6KiSE1NRaVSMX78eNzc3Pj++++Vdq1Wq5/rJkRZsre355dffgEw+iR5S0tLpV1t2rSJlStX4uPjw4IFC3jw4EGhp/o+evSI1NRUpkyZQrdu3Th8+HCxx+nu7q50POfPn+fGjRtG31epUiW6detGVFQUGo1GuQGj0WiUh8P+8ssvBAQE8OTJE9zd3dmzZw+Qf9Pm3//+NyqVCo1GQ1xcHLm5uWRkZDB06FCSk5MLHWdSUhKdOnViyJAhNGzYkISEBOU4MjMzlXravn27si+6gUajYdeuXWRkZKDVapk4cSIHDhwotu6ehUaj4bvvvkOn0/HkyRP27NmDm5sb9vb23Lx5k+zsbB49esSPP/4IGD9/hkERwzk4fvx4sbFBo9Gwbds2IH8A4/Dhw4WSxmbNmpGWlsaVK1cAiI+Pf67jLAs6nY4zZ87QsmVLs79NgBYtWrBgwQJWrVrF3bt3S/T5BdtbYmIiv//+e5H3+Pr68uTJE9auXavcWEhPT+f06dNYWFiYjPV16tShXr16yizm5ORk0tLScHZ2pnPnzuzevRuAgwcPKnuZDhw4kIyMDEaOHMnIkSP5+eefn7HmzDPXd5liLk507NhRaS8nTpxg3rx5QNE+p6Tatm3LuXPnlPJt2bIFNzc3k3Vtb29PnTp1SEhIAFCeGl8R+zhzucGcOXNwdXUtlB+ZR0I1HwAADExJREFUik3Py1Qc0mg0xMfHk5WVhVarZfv27Wg0Gtzd3Tl69Cjp6elkZ2fz/fffA/n9z/Tp0+nevTsRERFUrlyZO3fuPHf5ylLBOFKQIY7cv38fvV5PZGQkGzZs4MKFCzRo0ICRI0fSunVrDh06VCjWtGnThsjISBYuXFhk1vLJkyeN5sam+pLyyitM9aGmyltQSfNUg+PHjzN48GACAwPJzs7mypUrJmdWAybbVmn17duXkydPkpaWpszyLC7Gt23b1mguaErVqlVL3feWVknrr6R5WEE5OTlERUURFBSkrLZ4Fu7u7oSHhxMREUFeXl659V1lpWPHjtSqVYtVq1Yp7f3YsWPs2LGDY8eOlfr61pjGjRvz+PFjzp49C+TnntOmTTPZX73uyioOm8pVHzx4gK+vL02bNmXKlCl06dKFq1evlvtxmlNc7mXoD0p6XdaoUSPu37+vXA9+8cUXynO3TBkwYADffvsttWvXLtUM6E6dOnHkyBFlJWNcXBxubm4m87VX+Tx17NiRJk2asHjxYiXvv3jxImvXrmXChAkl6rc1Go1yLu7du0dgYGCR3Kgi5qhlzVSbb9eundH4WNK2b+qa1lRu6+7uzv79+8nJyeHx48ccO3as/CrBhJc/jP6au3fvnnJBlZeXR4sWLYiNjVUeiBUfH4+VlRXvvPOO0kDN8fX15eDBgxVu6ciz0Gg0hIWFMX78eCXIeXh4MHHiRKytrVm6dCmLFi3iyZMnVK9enaVLlxb5DFdXV2bOnImDg4OyLNLgyJEj9OvXT1nW0rt3b8aMGfPc5R47diyBgYEkJCQwefJkPvjgA2xsbKhVqxafffbZc39+RePl5cXcuXMLPXXdwcGBxYsX8+GHH5KXl4ejoyPR0dFUq1aNFi1a4OPjg0qlomvXrspgYufOnYmNjaVq1ar07dv3ZR0OkL/Ua9GiRaxatYquXbsWeb1WrVrUqVOH4cOHs3btWqZOnUpAQABqtZrp06cXmiVXo0YNQkJC8PPzw9LSEo1GQ1ZWltklxZMnT2bWrFn4+fnRuHFjs8tYg4ODGTJkCJs2bVL+FhERwfz58wkICAAgKioKOzs7Jk6cyOzZswkICMDJyQkHBwdsbW159913+fXXXwkKCkKr1TJgwADc3NzIzc1VjjMmJoawsDDlM11cXJSYVK1aNQ4dOsTy5ctxdHRUlhcbeHt7c+XKFQYNGoROp8PDw4OgoCDlwY6m6u5ZDB48mNTUVPr160dubi4BAQH06tULyN+Kwc/Pj7p169KhQwcA+vfvb7QM0dHRREZG8sUXX2BlZcXSpUuNLnMzmDhxIpGRkQQEBKDT6Rg/fjytWrVSkkcrKytiY2OZPn06lpaWZfqgqtK4ePGiEve0Wi3NmjVjzJgx2NjYmPxtGjRs2JBhw4axcOFCZXm4OXPnzmXmzJnExcXRvHlzo+fW2tqar776imXLlikPwtTpdPTp00dZWWQq1hvO0cqVK7GysmLlypVYW1szf/58pk+fzpYtW3BxcVGW0U+dOpVZs2ZhaWlJ5cqVSzXwWxrm+i5TzMWJ+fPnExERwddff02lSpWKlNvQ5xw6dIiePXsWWz4HBwcWLlxIWFiY8hv/5JNPiq3r2bNns2zZMtq1awfk38yraH2cqWP46aef2Lt3Ly4uLvTv3x+9Xk+TJk1YsmSJ0dj022+/PVc5zMWhy5cvExwcjFarpWvXroSGhmJpacmIESMICQmhWrVqykP+unXrxsGDB/Hz88PGxobAwEBlW4SXyVQcKTiDtnnz5oSFhTFixAglvx07dixarZZvvvkGX19f9Ho9rq6uyjJgA1dXV9zc3Fi2bFmhWDNw4ECjufGYMWOMxvHyyitM9aE1atQoNpcvmKeWxIgRI4iMjGTdunXY2dnRvn17bt26pWzv9bQWLVoYbVulZWtrS7t27ZRtyKD4GP/GG28YzQXNKW3fW1olrb+S5mGG3wLk910ajaZMtv3p378/O3bsYOPGjeXWd5UVlUrFmjVr+PTTT/H398fS0hJ7e3vWrVuHWq1+puvbp1lbW7N8+XLlIb52dnZ8/vnngPH+6nX0IuKwqVy1Zs2aDB48mJCQECpVqkSjRo2ULQwriuJyLw8PDxYsWMDnn39eousyGxsboqOjmTFjBrm5uTg5OREVFWX2Rnft2rWpXbs2QUFBpSp78+bNGTduHMOHDyc3N5dWrVrx0UcfYWdnZzRfe5XPE8CqVatYunQp/v7+qNVqqlevTnR0NG5ubqSlpSn9tilhYWFERkbi7++vrIRycnJSBm2hYuaoZc1Um580aRLe3t5F4mNJxyRMXdM2bdrUZG574cIF/P39cXBwULa5eZlU+tKsnxQvlVarZcaMGfTt29fkvqVCiP9du3btol69enTo0IHbt28TGhrKoUOHynVfcvG/46uvvqJz5840adKES5cuMW/ePHbs2PGyiyWEEKIU9Ho9GRkZDB48mC+//JI33ngDkBgvhBAGer2ee/fuMXz4cPbu3Yu1tfXLLpIQ/7NkJvYrQq/X4+HhQefOnUs0E0sI8b+ncePGLFiwgLy8PCwsLFi4cKEMYIsXpkGDBkydOlV5yNOiRYtedpGEEEKU0oULFxg9ejQTJ05UBrBBYrwQQhgcOHCAyMhIIiMjZQBbiJdMZmILIYQQQgghhBBCCCGEqLBkip4QQgghhBBCCCGEEEKICksGsYUQQgghhBBCCCGEEEJUWDKILYQQQgghhBBCCCGEEKLCkgc7CiGEEEIIUQa8vb1Zvnw5iYmJNG/e/IU/jPvjjz8mKSkJgGvXrlG3bl1sbW0B2LJli/JvIYQQQgghXnUyiC2EEEIIIUQZOn36NE2aNHnh3xMREaH829vbm5iYGFq3bv3Cv1cIIYQQQojyJoPYQgghhBBClJHExEQuXrxIVFQUarUaT09PYmJiSEpKQqfT0bJlSyIiIrCzs8Pb2xt/f39OnTrF48ePGT16ND/99BOXLl3C0tKStWvX4ujoWOoyZGZm4unpSVxcHI0aNQJg5MiRhIaGcujQIWxsbLhy5Qr379+nS5cuREREYGVlxbVr1/jkk0949OgROp2O4cOHExISUtZVJIQQQgghRKnJnthCCCGEEEKUEU9PT1xcXJgxYwa9evVi3bp1qNVqduzYwe7du3nzzTeJiYlR3p+dnU1cXBxTpkxh/vz5jBgxgt27d1O7dm127tz5TGWoXLky/fv3Z+vWrQDcuHGD1NRUvLy8ADh//jzr169n3759XLt2jS1btqDVapk8eTLh4eHs2LGDTZs2sX79epKTk5+/UoQQQgghhHhOMhNbCCGEEEKIFyQhIYH09HROnDgBQG5uLrVq1VJe7927NwD169fHwcGB5s2bA+Dk5MTjx4+f+XuHDh1KaGgof/7zn9myZQshISGo1WoAgoKCqFKlCgD9+vXj8OHDaDQabty4wZw5c5TPyMrK4ueff6Zdu3bPXA4hhBBCCCHKggxiCyGEEEII8YLk5eUxZ84cPD09AcjIyCA7O1t53draWvm3lZVVmX1vo0aNaNasGYcPH2bv3r3ExcUprxkGswH0ej0WFhbodDqqVq3Krl27lNfS0tKoWrVqmZVJCCGEEEKIZyXbiQghhBBCCFGG1Go1Wq0WgK5du7J582ZycnLIy8tj3rx5xMbGlks5hg4dSlRUFG3atCm0t/b+/fvJyckhOzubnTt34uXlRaNGjbC1tVUGse/cuYO/vz8XL14sl7IKIYQQQghhjgxiCyGEEEIIUYa8vb2JjY1l586dTJgwgbp16xIUFISvry96vZ5Zs2aVSzm8vLzIzMzk3XffLfR3W1tbhg4dSkBAAB07diQ4OBhra2vWrFnDtm3bCAgI4IMPPmDKlCl06NChXMoqhBBCCCGEOSq9Xq9/2YUQQgghhBBClK1//vOfREREsHfvXlQqFQCzZs3C2dmZUaNGveTSCSGEEEIIUXKyJ7YQQgghhBAV1IcffkhKSorR15YuXUrjxo2NvjZz5kzOnDnD0qVLlQFsIYQQQgghXlUyE1sIIYQQQgghhBBCCCFEhSV7YgshhBBCCCGEEEIIIYSosGQQWwghhBBCCCGEEEIIIUSFJYPYQgghhBBCCCGEEEIIISosGcQWQgghhBBCCCGEEEIIUWHJILYQQgghhBBCCCGEEEKICksGsYUQQgghhBBCCCGEEEJUWP8HhztZMazRbV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,6))\n",
    "sns.countplot(x='Item_Type', data=bigmart_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='Outlet_Type', data=bigmart_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'px' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-221-68180f3134a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0msale_visibility\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbigmart_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Item_Outlet_Sales'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbigmart_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Item_Visibility'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msale_visibility\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Item_Visibility'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Item_Outlet_Sales'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'px' is not defined"
     ]
    }
   ],
   "source": [
    "import plotly_express as px\n",
    "\n",
    "sale_visibility = pd.concat([bigmart_data['Item_Outlet_Sales'],bigmart_data['Item_Visibility']],axis=1)\n",
    "px.scatter(sale_visibility,x='Item_Visibility',y='Item_Outlet_Sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2617e52bc70>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sale_MRP = pd.concat([bigmart_data['Item_Outlet_Sales'],bigmart_data['Item_MRP']],axis=1)\n",
    "sns.regplot(x='Item_MRP',y='Item_Outlet_Sales',data=sale_MRP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  ...  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales\n",
       "0           FDA15         9.30          Low Fat  ...                Tier 1  Supermarket Type1          3735.1380\n",
       "1           DRC01         5.92          Regular  ...                Tier 3  Supermarket Type2           443.4228\n",
       "2           FDN15        17.50          Low Fat  ...                Tier 1  Supermarket Type1          2097.2700\n",
       "3           FDX07        19.20          Regular  ...                Tier 3      Grocery Store           732.3800\n",
       "4           NCD19         8.93          Low Fat  ...                Tier 3  Supermarket Type1           994.7052\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5089\n",
       "Regular    2889\n",
       "LF          316\n",
       "reg         117\n",
       "low fat     112\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data.replace({'Item_Fat_Content':{'low fat': 'Low Fat','LF': 'Low Fat', 'reg' : 'Regular' }}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LABEL ENCODING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data['Item_Identifier']=encoder.fit_transform(bigmart_data['Item_Identifier'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data['Item_Fat_Content']=encoder.fit_transform(bigmart_data['Item_Fat_Content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data['Item_Type']=encoder.fit_transform(bigmart_data['Item_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data['Outlet_Identifier']=encoder.fit_transform(bigmart_data['Outlet_Identifier'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data['Outlet_Size']=encoder.fit_transform(bigmart_data['Outlet_Size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data['Outlet_Location_Type']=encoder.fit_transform(bigmart_data['Outlet_Location_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigmart_data['Outlet_Type']=encoder.fit_transform(bigmart_data['Outlet_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Identifier',\n",
       " 'Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>156</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>9</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1121</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1297</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  ...  Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales\n",
       "0              156         9.30                 0  ...                     0            1          3735.1380\n",
       "1                8         5.92                 1  ...                     2            2           443.4228\n",
       "2              662        17.50                 0  ...                     0            1          2097.2700\n",
       "3             1121        19.20                 1  ...                     2            0           732.3800\n",
       "4             1297         8.93                 0  ...                     2            1           994.7052\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigmart_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2617c38bc40>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(bigmart_data.corr(), cbar = True, square = True, fmt = '0.1f',annot = True, cmap = 'Reds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Features and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = bigmart_data.drop(columns = 'Item_Outlet_Sales', axis = 1)\n",
    "Y = bigmart_data.Item_Outlet_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3735.1380\n",
       "1        443.4228\n",
       "2       2097.2700\n",
       "3        732.3800\n",
       "4        994.7052\n",
       "          ...    \n",
       "8518    2778.3834\n",
       "8519     549.2850\n",
       "8520    1193.1136\n",
       "8521    1845.5976\n",
       "8522     765.6700\n",
       "Name: Item_Outlet_Sales, Length: 8523, dtype: float64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "             importance_type=None, interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, objective='reg:squarederror',\n",
       "             predictor='auto', random_state=0, reg_alpha=0, ...)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_pred = regressor.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8549833167058186"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R squared Value\n",
    "r2_train = metrics.r2_score(Y_train,training_pred)\n",
    "r2_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #R squared Value\n",
    "# r2_train = metrics.r2_score(Y_test,testing_pred)\n",
    "# r2_train"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}