Big Sales Mart - Lasso & Ridge Regression (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is EDA?\n",
    "\n",
    "1. Exploratory Data Analysis as it seems. It helps understand the data better.\n",
    "\n",
    "2. Variables and their linkage with the Target Variable\n",
    "\n",
    "3. To Build the Model\n",
    "\n",
    "4. Understanding of the data better and its kinda know how about the data and its PATTERNS\n",
    "\n",
    "## EDA Process\n",
    "\n",
    "1. Univariate Analysis - Analyising Features Single Handedly. \n",
    "\n",
    "2. Bivariate Analysis - Predictor Vs Target\n",
    "\n",
    "3. Missing Values Treatment\n",
    "\n",
    "4. Outlier Identification - BOX COX TRANSFORMATION..\n",
    "\n",
    "5. Feature Engineering - Extracting Variables from the Existing variables in the Dataset\n",
    "\n",
    "6. Ensuring that the Data is following a Standard Scale. If not, apply Standard Scaling.\n",
    "\n",
    "This 06 Step Process are Called Data Pre-Processing Steps.\n",
    "\n",
    "7. Model Building Stage - This is the stage where the Data is ready to be used as Input for Model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retail Industry: Big Sales Mart\n",
    "\n",
    "The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.\n",
    "Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.\n",
    " \n",
    "Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Required Libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"~/Downloads/ML with R Programming/Big Mart SalesTrain_UWu5bXk.csv\")\n",
    "test = pd.read_csv(\"~/Downloads/ML with R Programming/Big Mart SalesTest_u94Q5KV.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the dataset\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 12)\n",
      "(5681, 11)\n"
     ]
    }
   ],
   "source": [
    "# Basic Checks on Dataset\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      "Item_Identifier              8523 non-null object\n",
      "Item_Weight                  7060 non-null float64\n",
      "Item_Fat_Content             8523 non-null object\n",
      "Item_Visibility              8523 non-null float64\n",
      "Item_Type                    8523 non-null object\n",
      "Item_MRP                     8523 non-null float64\n",
      "Outlet_Identifier            8523 non-null object\n",
      "Outlet_Establishment_Year    8523 non-null int64\n",
      "Outlet_Size                  6113 non-null object\n",
      "Outlet_Location_Type         8523 non-null object\n",
      "Outlet_Type                  8523 non-null object\n",
      "Item_Outlet_Sales            8523 non-null float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# finding the Info\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA Step 01. Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Item Outlet Sales\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1236b3a58>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Item_Outlet_Sales, color=\"brown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: Item_Outlet_Sales, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary Statistics of Sales\n",
    "train.Item_Outlet_Sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
      "       'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
      "       'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
      "       'Outlet_Type', 'Item_Outlet_Sales'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123aab320>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train[pd.notnull(train.Item_Weight)][\"Item_Weight\"], \n",
    "             color=\"magenta\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Item Weight\n",
    "No Shape Identified in Item Weight and However, Item Weight has Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7060.000000\n",
       "mean       12.857645\n",
       "std         4.643456\n",
       "min         4.555000\n",
       "25%         8.773750\n",
       "50%        12.600000\n",
       "75%        16.850000\n",
       "max        21.350000\n",
       "Name: Item_Weight, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Weight.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123fc5710>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DywL8Ch+XEJGo0nS5fBg42szmA9cAB5rZr3vvFEKYEkKYEEKY0NWVg2vt56L+86wMBU7B+9F/hSoyikTWdEIPIXwzhDAmhNCNV/u4M4RwYmaRtYrmoGerG/gkMB24KG4oImVXrnnoLwOvoISetYPwWS9fR4thiESUSUIPIdwdQjgqi2O1VGXKohJ6ttYDPguMAY4GnoobjkhZlauFXpmyqD707G2MV2Nchc9TfzluOCJlVK6EXmmhbx81iuLaGbgeeBo4FMjBpCaRMilfQh+Dr2YvrbEv8HvgMXy1o/lRoxEplXItcKGiXO1xJHB7sv0QPqXxkKrf97WQhi5MEkmlXAl9HpD/odtimAjciyfyQ/GZMJ8AhsQMSqTYytPl8gZeaVEt9PbZDfgWsD9wB3AenVTGTaTjlCehqyhXHEOB44Ezk59/AlwKvB4tIpHCKk9C1xz0uHYBvo33qz+c3L8HlQsQyVB5ErrmoMc3BL/w6H/jS9ldBVwMLIsZlEhxlCehzwU2p5MK/RbXlvgSdscAM4HvAwujRiRSCOWZ5aIpi/lieD31sfg0xh8BK1i3Tr2mMorUrVwJ/cDYQRRcX/PL+zMOXyLlguR2BrqSV6RJ5ehyWQYsQP3nedWFL2U3HLgQ+FvccEQ6VTkS+tPJVl0u+TUK+BpeluFnwItxwxHpROVI6Jqy2BlG4l0uK4GfAm/GDUek05QroavLJf/eA3wFeAn4v8DyuOGIdJJyJPR5wKb413rJvx2Bk4AngNPQxUcidSrHLJfKlEWLHYjU7QP43PT/hy9vd0bccEQ6QXkS+j6xg5CGTQI2wC9C2hn4aNxwRPKu+F0uK4BnUP95J1oPuBIYDxwL/E/ccETyrvgt9GfwdS41w6UzbQzcgH/D+hjwADC1xn66olSkBC10TVnsfGOBP+AXHB2Df0CLyDqU0KUzTMQHSO8Ero0ci0hOlSOhDwO2iB2IpHYSXvflHuCuyLGI5FDxE/o8fEBUUxaL4fvA7sB1wGORYxHJmeIndJXNLZZBwKnAVngXzPNxwxHJk2LPclkFPIWvkiOdqVZJ3g2ALwP/Bvwc+Ke2RiSSW8VuoS/Aa4FoDnrxjAa+BLyCJ/WlccMRyYNiJ/TKOqLqcimmHYHPAfPxC49WRo1GJLpiJ/THk+24qFFIK+0JnAD8CfgMSupSak0ndDPbxszuMrM5ZvaomX01y8Ay8Rh+peGY2IFIS+0H/DtwDXAySupSWmkGRVcCXw8hPGxmw4FpZnZbCCE/k8nmALugKYtlcDawGjgH/8u8Ah88raXWQKtKB0gBNN1CDyEsCiE8nNx/A0+fW2cVWCYqCV3K4Z+AH+Jz1A8ClsQNR6TdMulDN7NuYC+8dFLv3002sx4z61mypI3vsNfwetpK6OXyDTyhP4zXVL8vbjgi7ZQ6oZvZxsDvgDNDCK/3/n0IYUoIYUIIYUJXV1fa09WvUmpVCb18jsHLA6wCPoKXC3g7akQibZEqoZvZEDyZ/yaE8PtsQsrInGSrhF5O+wCz8GmNPwR2Ai5FA6ZSaGlmuRj+FpkTQvhxdiFlZA4wFNg+diASzQh8oek78VIBnwP+Dm+9a/FpKaA0LfQP4zN/DzSz6cntiIziSm8OPv+82MUNpB4HAPfjC2VsBlwFfBP4I/BGxLhEMtZ0ugsh3EueJwTOwS86EQH/Sz0aX/XobOA24CbgVryf/ePA5r2eU2t6I2iKo+RWMduvb+NFuY6PHYhEVysp75TcFuGJ/R683s/Z+ABqX/PXRXKumAn9SfwiEw2Ilkdfren+bIUvmnEoMAP4Dn616S/xKY8iHaaYtVw0w0UasSU+V+vPeJ/6RDy5r44ZlEjjipnQH8P7THeOHYh0lI8CjwInAucBvwCWRY1IpCHFTOhzgO2ADWMHIh1nBHA58DNgNvADvOa6SAcoZkJ/BBgfOwjpWAacBpyJJ/MfAS9GjUikLsVL6K/gg6Ia1JK0dsaT+lt4Un8hbjgiAyleQu9JtntHjUKKYjvg68AK4EK86JtIThVv2uKDyXZC1Cik0/Q37XEMcDrwYzypfw4Y2Y6gRBpTvBb6Q/hX5U1jByKF0o0vSv08fsXpO1GjEampWAk94BXZ1d0irbAL8I/AvcAp+N+bSI4Uq8vlOWAxXjpVpBX2xlvr/4wXf/tuzGBE1lashF7pP1dCl1b6FjAX+B6wI34hkkgOFCuhPwQMAfaIHYgUmuF11p8BTgXGAvtGjUgEKFof+oPA7qhanrTeULz+y3Z46d0n44YjAkVK6KvxOejqbpF2GQn8CX8XHQm8FDcckeIk9JnA6+gKUWmvHYDr8e6XT6LpjBJVcfrQb8D7Ng+PHYgUXq2LkC4HTgA+D1xBntfykgIrTgv9eryOde9lxETa4Q38gqMr8T71ZhbcEEmpGAn9GWA6/kYSieUI4IPAjayZQivSRsVI6Dck20lRo5CyM3xO+ji82+WOuOFI+RQnoe+Kv5FEYhqC13zZHJ/58se44Ui5dH5CfxlftV2tc8mLYcA38GsiPoH3q4u0Qecn9JuAVaj/XPJlGHA7fgXpScCXgbejRiQl0NkJfTVwAbA9qn8u+TMC+AtwNr7g9ES8GqhIi3R2Qp+Kz275Hp3+L5GiGgL8Oz7OsxCfBXMcfiGcSMY6Nw2uBL4N7AYcHzkWkYEcjdd7+TY+rXEPYE/g+/gY0NJ4oUlxdO6VolcCjwN/AAZFjkWklloXF30PX87uGvxv+NzkcQO2ALauui0ENul1+xqd3AyTFuvMhD4Pr0m9N5rdIp1nNHBacnsRvwjpYnzG1qvANHwOe61W+zeBLYGtku0WVdvNgeHAxlXbyv2NUDmCEkiV0M3sMHzZ3EHAJSGE8zOJqj/zgP3xVdgvRX+k0ln6KglwVI3HluMF515Lbq/iEwAWJbdn8TUAXsAnCPTHWDvRD+912wz/QKh1G05732cBf38vw7tWh1TdBrU5lg7TdEI3s0HARcAhwALgITO7MYTwWFbBrWU1cAvwRfw/+g5gfEvOJJIPQ/HW/OiqxybX2G8VPtvrTbza49t1bN/GPyQqP7+Jv69qGcyaxL8bnuRH4K3+YcltAzwRr8KT8CrWJOW3ku0yYFby+Ar8A2t58vzqfd6i7w+oygfTZsCoZPtKEs8mVdtT8G8xoylVF1WaFvo+wNwQwlMAZnYN3gGSfUL/NXA+8Ci+OswdaFUiKae+WvgjklsaK/DE/gb+zaCyrTz2Bv5tYHZyf2nynP4Ynvg3TG7L8Q+qIcl2I3zx7crvK/vOTn6/Hv7hULmtxD+A3krimpvE8Tprz/P/abIdhHdHVbqoKtvKB1L1bViyXb9XjJVt9f16vykE/EPqlSSOFndypzn81viXvooFtKoaeWURgSuBY/EXVUSyNQRftGNkA89ZxZqWtuHv00qyG4RnmGa6SLZt4jnvsHYX1eu9tgvx8Yl6uqgGYqyb6Aez5oNndbJ9C39twCdx7JTyvANIk9Br/TeFdXYym8yaL4pvmtnjTZ/xM8ktO6PxYam8UnzpKL50FF9fAms+yPq2dnw7pzrj2Hp2SpPQFwDbVP08Bv8MXEsIYQo5rQ5tZj0hhNxeY6r40lF86Si+dGLEl2a44CFgnJltZ2ZD8evfbswmLBERaVTTLfQQwkozOw24Fe8tuyyE8GhmkYmISENSjbmGEG4Gbs4olhhy2RVURfGlo/jSUXzptD0+C2GdcUwREelAJZpyLyJSbIVN6GZ2mJk9bmZzzeycGr9f38yuTX7/gJl1V/3um8njj5vZR/MSm5l1m9kyM5ue3C7OOrY649vPzB42s5Vm9ulevzvZzJ5MbifnML5VVa9fSwbx64jva2b2mJnNNLM7zGxs1e/y8Pr1F18eXr8vmtmsJIZ7zWzXqt+19L2bJr62vH9DCIW74YO08/DKF0OBGcCuvfb5MnBxcv844Nrk/q7J/usD2yXHGZST2LqB2Tl47brxBdZ+BXy66vFRwFPJdmRyf2Re4kt+92YOXr8DgI2S+1+q+v/Ny+tXM74cvX4jqu4fDfw5ud/S924G8bX8/VvUFvq7ZQlCCMvxYqW96zJOwtdmB18q4yAzs+Txa0II74QQnsYvLt4nJ7G1w4DxhRDmhxBmsu71dh8FbgshvBxCeAW4DTgsR/G1Qz3x3RVCeCv58X78Gg7Iz+vXV3ztUE98r1f9OIw1FzS2+r2bNr6WK2pCr1WWYOu+9gkhrMQvDt6szufGig1gOzN7xMzuMbN9M4yrkfha8dx6pT3HBmbWY2b3m1krVqJtNL5T8bJzzTy3GWnig5y8fmb2FTObh68HdUYjz40YH7T4/duZ9dAHVk9Zgr72qaukQQppYlsEbBtCeMnM3g9cb2a79WoRtCO+Vjy3XmnPsW0IYaGZbQ/caWazQgjzMooNGojPzE7EV8P9+0afm0Ka+CAnr18I4SLgIjM7Afhn4OR6n5tSmvha/v4tagu9nrIE7+5jZoPxopsv1/ncKLElXyVfAgghTMP78rIu95Pm39/q1y71OUIIC5PtU8DdwF5ZBked8ZnZwfh6RUeHEN5p5LkR48vN61flGqDyTSE3r1+Vd+Nry/u3lR30sW74N4+n8IGRysDFbr32+QprDzxel9zfjbUHVp4i20HRNLF1VWLBB2WeA0a1+7Wr2vdy1h0UfZo1Nfuezll8I4H1k/uj8VU+d213fHgSnAeM6/V4Ll6/fuLLy+s3rur+x4Ce5H5L37sZxNf692+WB8vTDTgCeCL5wzw3eew8vMUBXpL/t/jAyYPA9lXPPTd53uPA4XmJDfgUXhV+BvAw8LFIr93eeEtlKfAS8GjVc09J4p4LfDZP8QET8SUWZiTbUyPFdzvwPDA9ud2Ys9evZnw5ev0uTN4H04G7qEqorX7vpomvHe9fXSkqIlIQRe1DFxEpHSV0EZGCUEIXESkIJXQRkYJQQhcRKQgldBGRglBCl6jM7M1k251cJt2q8+xvZvf1emywmT1vZluZ2XnJ1ZF9Pf+LZnZScv9uM1tn8V8zO7pSTtXMvmtm30juv3tsMzvTzDbK8t8mUlHUWi7SebqBE4CrWnT8vwJjzKw7hDA/eexgvJzpIuDb/T05hDBg7eoQwo3UWCg9hFB97DOBXwNv9d5PJC210CUvzgf2TQr/n2Vmg8zsh2b2ULLQwhfg3Zb2PWZ2nZk9YWbnm9k/mNmDyaICO9Q6eAhhNX717bFVDx8HXJ0c93JLFsNIjllZ4OFHyWPvtrgTJ5rZf5vZbDPbJ9nnH83s573PXTm2mZ0BvAe4y8zuMrNTzeyCqv0+b2Y/bv4llLJTQpe8OAf4zxDCniGEC/Cyra+FEPbGL+X/vJltl+y7B/BVYDzwGWCnEMI+wCXA6f2c42o8iWNm6+OXcP+uegczGwV8Ar9ce3fg//RxrGEhhIn4YiSX1fMPDCH8FC/kdEAI4QC8cNPRZjYk2eWzwC/rOZZILepykbw6FNjd1iwhtwkwDlgOPJR0k5DUnP5Lss8sfLWdmkIID5nZxma2M7ALcH/whSSqvQ68DVxiZn8CburjcFcnx/yrmY0ws00b/QeGEJaa2Z3AUWY2BxgSQpjV6HFEKpTQJa8MOD2EcOtaD5rtD7xT9dDqqp9XM/Df9DV4K30XkqRcLYSwMulCOSjZ7zTgwBrH6V0EqdmiSJcA3wL+B7XOJSUldMmLN4DhVT/fCnzJzO4MIawws53wcqNpXQ3cgLf4T+39SzPbGF9P82Yzux+veljLsXhf+EfwrqHX6lwlsPLvfBEghPCAmW0DvA9fB1WkaUrokhczgZVmNgOvY34hPvPl4WQ91SWsWcigaSGEx8zsLWBaCGFpjV2GAzeY2Qb4t4Sz+jjUK2b238AIvORtvaYAt5jZoqQfHeA6YM8a3T8iDVH5XJHIzOwm4IIQwh2xY5HOplkuIpGY2aZm9gSwTMlcsqAWuhSOmZ0LHNPr4d+GEP41Rjwi7aKELiJSEOpyEREpCCV0EZGCUEIXESkIJXQRkYJQQhcRKYj/D+fN9yKJAOPkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Visibility\n",
    "sns.distplot(train[\"Item_Visibility\"], color=\"magenta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8523.000000\n",
       "mean        0.066132\n",
       "std         0.051598\n",
       "min         0.000000\n",
       "25%         0.026989\n",
       "50%         0.053931\n",
       "75%         0.094585\n",
       "max         0.328391\n",
       "Name: Item_Visibility, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Visibility.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Item Visibility\n",
    "\n",
    "Item Visibility has lots of 0s in the Dataset which is not correct. This needs correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12412b240>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TgvKayhb3N09/rIw2V+nxURSkxPCLw5cW9GH4k3dryEiICtpCcUsz4hgYcdLipyqwKrhoQlBec76ll+zEaBIDYHGYqVxVmEJFcy+nG+c3lnCxvZ+951q5t6zQ9tXQ5uvyOIJ2G6kpBOdvtQo4I2MuatoHAvLqYNyVeUlEhAkvHJ5ft9F/vHeRMJGAK0E+F6lxkSTHRlDd1m93KCoAaUJQXlHT3o/TZVgRwAkhNiqcW1Zl8qtjDYzNcQWxnqFRdh6oY+u67KC/Q2dpehwX2vpx6TiCmkQTgvKK8829hDuEovTAud10Kn98VR6tvcO8db5tTvv9x3sX6R0e4ws3L/NRZP5TlOYeR2jTcQQ1ia6YFmQCdVW18y19FKXHEREW2N8xtqzJIisxih+8Vc2tqz0rWT006uSpt2u4cUU66/LsK+ftLYVWZda6zgEyg/xqR3lXYP/vVUGhe3CUlt7hgO4uGhcZ7uBzNyzl3ap2jtZ1ebTPc4fqaesb5ou3LPdxdP6RnhBFdISD2o4Bu0NRAUYTglqwqtbAvd10KvdsLiQxOpzv7a2ate2Y08UTb1azoSCZa5am+iE633OIUJASqwlBfYAmBLVgVS19xEaGBc1ga3xUOPddV8TLp5suz52Yzo/eqaG2Y4CHbl2OBEFVU08VpsbS0jPM0KjT7lBUANGEoBbEGEN1Wz9LM+KDogz0uD+9roiocAfffu38tG0utvfzz69U8OGSLLasCdwlMuejMDUWA9R3DtodigogmhDUgrT3j9A9OMqyjMC+u2iytPgoHrhxKbuPNbDrYN0HnjfG8PDzJ4hwOPinbesW1dUBQEFqLALUduh8BPUHmhDUgoyPHyzLCI7xg4m+9KGV3LA8nX/41UmO1/9hgNnlMvzrq+fZV93OIx9ZQ3ZScHSFzUV0RBgZCVE6jqDeRxOCWpCq1n6SYiJIi4u0O5Q5C3MI377nKjLio3jgJ4f47t4q3qtu5zNP7effXjvPx9fnsuPqgtkPFKQKU2Op6xjUCWrqMp2HoObN5TJUt/axKishaLtUUuMi+f5nNvE/nz/OYy+dBSA2MozH/ssVfLK0IGjPyxOFqbGUX+ykvW8kIJY7VfbThKDm7WxTLwMjTpYFye2m01mXl8Rv//JGWnqGOFDTwfr8ZAqsyVuL2fg51nYMaEJQgHYZqQV4t8pd/mFpgJer8FRmYjQfuzI3JJIBQIZOUFOT6BWCHy1kDV2XMRyp7aJrcITSJakkxdhfYnpfVTtpcZEkxwbf+IH6wwS1Ok0IyqIJIQjUtvez+3gDDV3u5R/3nm1lfUEyH7syh+iI+S/yvhBjThf7L3QE7UIxyq0gNZbXz7YwrBPUFJoQAl5LzxA/eOsCcVFhfOrqAgpSYnmnso39F9oZGXNyT1mhLQOfxy910zc8FpS3m6o/GJ+gVqcT1BSaEAKaMYbfHG8kIlx46LYVxEe5366Pr88lKSaCl041caCmg83F/l/fd19VOwDFi2T8IFQVpPxhYFkpHVQOYKcbe6hs7eNDa7IuJ4NxN6xIZ0VmPL893khjt/+/3b1b1caanMQPxKWCS0yke4KajiMo8DAhiMhWEakQkUoReXiK56NE5Fnr+f0iUjThuUes7RUicsek/cJE5IiI/GahJ7LYjDpdvHiikazEqCmvABwi3F1aQExEGL88srCF4+dqaNRJeU0n1y3z/5WJ8r7CVHflU3/+DqnANGtCEJEw4DvAnUAJcI+IlExqdj/QaYxZDnwLeMzatwTYAawFtgKPW8cb9yXgzEJPYjE6WNNB58AoH7sylzDH1GME8VHh3LYmk7rOQapa/VeT5nBtJ8NjLk0Ii0RhaiyDo04u6DrLIc+TK4QyoNIYU22MGQF2AtsmtdkGPG09fg7YIu6Rzm3ATmPMsDHmAlBpHQ8RyQc+Cvxw4aex+Byp7SI3OXrWQdtNhSkkRoez52yznyJzjx+EOYSy4sWxPkCoG19B7XCtZwsGqcXLk4SQB0wsB1lvbZuyjTFmDOgG0mbZ91+B/wHMbbXzENDWO8ylrkHW5yfP2jY8zMFNKzOoaR+gum3m2v7e8k5lG1fmJ5EQbf9cCLVwGQlRRIU7OFzbaXcoymaeJISp+ismdzZO12bK7SLyMaDFGHNo1hcXeUBEykWkvLW1dfZoF4Gj9V0IcKUHCQHg6qJUEqLC2XO2xbeBAX3DYxyr79buokXEIUJBaiyHL2pCCHWeJIR6YGLJx3ygYbo2IhIOJAEdM+x7PXCXiNTg7oK6TUT+Y6oXN8Y8YYwpNcaUZmRkeBBucDPGcKyui+KMOI9nI0eEObhhRTrVrf00dPn2jqN9Ve04XYYbli/+9yKUFKbGcq65l77hMbtDUTbyJCEcBFaISLGIROIeJN49qc1u4D7r8XZgj3HfsrAb2GHdhVQMrAAOGGMeMcbkG2OKrOPtMcZ82gvnE/QudQ3S3j/CBg+vDsaVLkkl3CEcqOnwUWRub51vJTYyjI1L5hafCmyFqbG4DByv03GEUDZrQrDGBB4CXsZ9R9AuY8wpEfmaiNxlNXsSSBORSuDLwMPWvqeAXcBp4CXgQWOMzpGfwdG6LsIdwrq8pDntFxMZxpX5SRyt6/Lpt7y3zrdxzdI0osLtKZmhfGN8gpqOI4Q2j2YVGWNeBF6ctO0rEx4PAXdPs++jwKMzHHsvsNeTOBY7YwynGnpYmZUwrxpFZUWpHK7tYvfRBu7dXOj1+Oo6BrjQ1s9nr13i9WMre8VEhrE8M17vNApxOs00gHRY6xPfsmp+/fMFqbFkJ0bz73vOY4yZscbRfBLGW+fd5a5vXKHjB4vRxsJkXjndPOvvjlq8tHRFAKkcX584fX4F40SEq4tTaege4pIPBpffOt9KblI0yzK0ftFitLEwhc6BUZ2gFsI0IQSQ6tZ+EqPDSYuf//oCVxUkExEmHKzxbl/wmNPF25Vt3LgiQ789LlIbl6QAOkEtlGlCCBAu416feFlG/II+cKMjwliXm8Tx+i5Gxrw35+9YfTe9Q2PcuDLda8dUgWV5RjwJ0eE6sBzCNCEEiOaeIfpHnF5ZX2DTkhSGx1ycauj2QmRub1S0IALXL9OEsFg5HMKGgmSdoBbCdFA5QFRbxemWeqF/vig9jpTYCA7VdnJVYcq8jzNxyc9d5fUsSY3ldyebFhyfClwbC1P4f3vO0zc8pqXNQ5BeIQSIqtY+r61P7BBh05IUqlv76ewfWfDxOvpHaOoZoiRHl8tc7DYuScFl4KiOI4Qk/QoQAJwuw4W2fo+K2XlqY2EKr51p4XBtJ1vWZC3oWKcbewAoyZ3bZLlgNfHKKNRsLEzGIXCgpoMbVmj3YKjRK4QA0Ng9yPCYyyvdReOSYyNZlhHPodpOXAtc+OR0QzfZidGkxi386kUFtoToCNbmJrG/ut3uUJQNNCEEgPHlC5ekeff+/quLU+kaGOV88/zLYvcNj3GxfYCSXO0uChWbi1M5UtfF8JhWmQk1mhACQF3nIAnR4SRGe7cHb01OAnFR4QsqeHe2sQcDOn4QQsqKUxkZc3G83nt3qangoAkhANR3DpCfEuv1CV/hDgelS1KoaOqhe3B0Xsc43dhDckwEOUnRXo1NBa6ri9wr4R244NvKuSrwaEKw2eCIk7a+EQpSYnxy/KuLUjEGyudxldA3PMb55j7W5ibq7OQQkhIXyaqsBPZrQgg5mhBsVt/lHj/It8oPe1tqXCTLM+Mpv9iJ0zW3weXDFztxGnP5G6MKHWXFqRyq6WDMqSvchhJNCDar73QXoctL9s0VArgHCbsHRy/fPuoJl8twsKaDorRYMhO1uyjUlBWn0j/inNPvjAp+mhBsVtcxQEZ8FDGRvltwZnVOIhnxUbx+tsXjW1D3VbfT3j9CWbFeHYSizdb7vr9au41CiSYEGxljqO8cJN9H4wfjHCLcujqDpp4hznj4je+ZA7XERISxNkQmo6n3y0yMZml6HO9WtdkdivIjnalso+7BUfqGx8hP9c34wURX5CXz2pkW9pxtmfUW0tbeYX5/qomyolQiwvQ7Q6i6cUU6z5bXMTTqnNcKftPxZCa4L1b8U7PT/+02qrPGD3x1h9FEYQ7h1lWZNHYPcaaxd8a233z5LMbA5uI0n8elAtctqzMZGnXp7achRBOCjeo7BwhzCNl+GrRdX5BMWlwkvznRQNfA1EXv3qtuZ1d5PZ+7cSnpCVF+iUsFpmuXphEV7mBvRavdoSg/0YRgo4auQbITown3U7dMmEP4ZGkBvYNjfHnXMVyTbkMdHnPy9y+cID8lhi9tWeGXmFTgio4I45qlaew912J3KMpPNCHYxBhDY/eQ32cAF6TG8pErc9hztoXvvF6Jse46Ghxx8tXdp6lq7eefPrHOp3c9qeBxy6oMqlv7L9fbUoubDirbpGdojIERJzk+nH8wnWusWwr/+ZVz/OZ4I1vXZfPcoXoudQ3yZ9cXc+uqTL/HpALTLasy+cdfn2ZvRQufubbI7nCUj3l0hSAiW0WkQkQqReThKZ6PEpFnref3i0jRhOcesbZXiMgd1rZoETkgIsdE5JSI/KO3TihYNHa5B5RzbagRJCL8893r+T9/fAVREQ7+7bXzxEWF8ewD1/CVj5f4PR4VuIrT41iSFqvjCCFi1isEEQkDvgN8GKgHDorIbmPM6QnN7gc6jTHLRWQH8BjwKREpAXYAa4Fc4FURWQkMA7cZY/pEJAJ4W0R+Z4x5z6tnF8AauocA/DagPFlkuIN7ygq5p6yQxu5B0uOj9BZTNaVbVmawq7yewRFn0HUl6i2uc+NJl1EZUGmMqQYQkZ3ANmBiQtgGfNV6/Bzw7+KuhrYN2GmMGQYuiEglUGaM2QeMF+mPsP4sbBWXINPYPUhaXCRRXry/e75ykvzfbaWCxx3rsnl630VeOdPMXetz7Q7H62ZLGqGUMDz5SpgH1E34ud7aNmUbY8wY0A2kzbSviISJyFGgBXjFGLN/PicQrOwYUFZqPq4pTiM3KZoXDtfbHYryMU8SwlR1jyd/m5+uzbT7GmOcxpgNQD5QJiLrpnxxkQdEpFxEyltbF0c/5tCok47+EVsGlJWaK4dD2HZVHm+eb6O1d9jucJQPeZIQ6oGCCT/nAw3TtRGRcCAJ6PBkX2NMF7AX2DrVixtjnjDGlBpjSjMyMjwIN/A197jHD3K0iqgKEn98VR5Ol+HXxyb/11eLiScJ4SCwQkSKRSQS9yDx7kltdgP3WY+3A3uM+wb33cAO6y6kYmAFcEBEMkQkGUBEYoAPAWcXfjrBYXxAWa8QVLBYkZXA2txEXjhyye5QlA/NmhCsMYGHgJeBM8AuY8wpEfmaiNxlNXsSSLMGjb8MPGztewrYhXsA+iXgQWOME8gBXheR47gTzivGmN9499QCV2PXILGRYV5fQ1kpX/qjq/I4cambypaZa2Gp4OXRJ5Ix5kXgxUnbvjLh8RBw9zT7Pgo8OmnbceCquQa7WIwPKOuylCqY3LUhl6//7iw/freG//2JK+wOR/mA3njuZ06XoblnSG/1VEEnMyGaT11dwLMH67SUxSKlCcHP2vuGGXMZsvWWUxWEHrptOSLCt187b3coygc0IfhZU4+9M5SVWoicpBg+vXkJzx+up7q1b/YdPOB0GTr6R6jvHOB8cy/VrX2cqO+mvnOAMafLK6+hPKOjmn7W1DOEQyBD1xqwhSelDNTMvnjrMv7zQC3feKmC735647zGwjr6Rzha10V1ax/1nYOMTPrg/+HbFwAurxeSlxJDfkoM+ckx5KfEkp0UTWpc5OU/3lzRLZRpQvCz5u4h0rRukApi6fFRPHTbcr75cgXffaOKL96y3KP9nC7D70818cSbVdS0DyBATnI0G5ckk5sUQ1xUODERYTiNYXNxKu39I1zqHKS+c4BLXYO8V9VOU88QrimK3MREhJEaF0lKXAQZ8VGUFadx44p0XMbg0Js3PKYJwc+aeobIT/H9GspK+dIXb1lGRVMv33ipgsLUWD525fQ1joZGnTx/uJ4fvFlNTfsAqXGR3F6SxYaCZJJjI6fc5/a12VNuH3W6aOoeorF7iM6BETr7R+gY/7t/lM4Bd9fTYy+d5bGX3NWE77wih2UZ8V4578VOE4IfDY866RwYpbRIxw/09IACAAAWWklEQVRUcBMRvrH9Shq6BvnyrmOca+rlz24oft8HfFVrH7851shP36uhrW+E9flJfPdPNtLePzLvb+0RYQ4KUmMpSJ35S1VL7xB7zrTw9d+d5cm3L7A2N5G7NxUQGa5X5jPRhOBHzTqgrBaR6IgwfvDZUv7uhRN8e08lT71Tw/qCJAShoXuQ6tZ+wL3q2udvWsY1S1MREb+M42QmRLOjrJDhMRdvV7bx6ulmfjxcw33XLgmICsOBShOCHzX1uAuDZWlCUItESlwk3/30Js409vD9N6qo63Qv/FSQEst91xbx4ZIscm0s0RIR5uDWVZmkxkXy8/I6fvRuDX96XZEOQk9DE4IfNfUMERnuIDk2wu5QlPKqNTmJ/OuOwC0+sD4/mTARdh6s5dfHGri7tGD2nUKQdqj5UVP3ENmJ0XrXg1I2WJeXxM0rMzlS18WZxh67wwlIeoXgJ8a4S1asy0uyOxSlFoX5jEXcujqDs009vHDkEktSY4mN0o/AifRfw0+ae4YZHHWSnagT0pSaja8GnsMdDrZvyufx16v47YlG7TqaRBOCn5xtcl+iZmkNI7WIBcNM8JykGK5fnsZb59u4eVUGmQn6f3KcjiH4ydkmdw15veVUKfvdsCKD8DBhb8XiWJbXW/QKwU8qmnpJjA4nNlL/yVVwCoZv/56Kjwpnc3Ea71S2sWV1Jmnx2pULeoXgN2eberXktVIB5MYV6YQ5hL3n9CphnCYEPxh1uqhq6dPuIqUCSEJ0BFcXpXKktpOugRG7wwkImhD84EJbPyNOl85QVirAXL88HZeBQ7WddocSEDQh+MHlAWXtMlIqoKTGRbIsI47DFztxmSnqaocYTQh+UNHUQ5hDyNCBK6UCTumSVDoHRi8X4wtlmhD84GxjL8sy4gjXRXGUCjgluYnERIRRfrHD7lBsp59QfnC2qZdV2Yl2h6GUmkJEmIMNBcmcauhhYHjM7nBs5VFCEJGtIlIhIpUi8vAUz0eJyLPW8/tFpGjCc49Y2ytE5A5rW4GIvC4iZ0TklIh8yVsnFGh6h0a51DXI6uwEu0NRSk2jtCgFp8twtL7L7lBsNWtCEJEw4DvAnUAJcI+IlExqdj/QaYxZDnwLeMzatwTYAawFtgKPW8cbA/67MWYNcA3w4BTHXBTONbsHlFdlaUJQKlDlJMWQkxTN8fpuu0OxlSdXCGVApTGm2hgzAuwEtk1qsw142nr8HLBFRMTavtMYM2yMuQBUAmXGmEZjzGEAY0wvcAbIW/jpBJ4zje6EsDpHE4JSgWxdXhK1HQN0D47aHYptPEkIeUDdhJ/r+eCH9+U2xpgxoBtI82Rfq3vpKmD/VC8uIg+ISLmIlLe2Bt+MwoqmXhKiwsmzcdUopdTs1uW6S9OfagjdqwRPEsJUq7lMvmF3ujYz7isi8cDzwF8ZY6ZcscIY84QxptQYU5qRkeFBuIGloqmXldkJiC6Ko1RAy0iIIisxihOXNCHMpB6YWDQ8H2iYro2IhANJQMdM+4pIBO5k8DNjzC/mE3ygM8ZwprGHNdpdpFRQWJeXRG37AD0h2m3kSUI4CKwQkWIRicQ9SLx7UpvdwH3W4+3AHmOMsbbvsO5CKgZWAAes8YUngTPGmH/xxokEovrOQXqHxyjJ0VXSlAoG63KTMMCpEF1ic9aEYI0JPAS8jHvwd5cx5pSIfE1E7rKaPQmkiUgl8GXgYWvfU8Au4DTwEvCgMcYJXA98BrhNRI5afz7i5XOz3akG9y9VSa7OQVAqGGQlRpOZEMXJEO028qg4vzHmReDFSdu+MuHxEHD3NPs+Cjw6advbTD2+sKicbuzBIXrLqVLBZG1uEnsrWhgYHgu5NZd1prIPnW7ooTg9jpjIMLtDUUp5aHV2AgY419Jrdyh+pwnBh8409lCSq+MHSgWTvJQY4qLCL1cpDiWaEHyke8BdsqIkR8cPlAomDhFWZcVzrrkXpyu0SmJrQvCR0406oKxUsFqVncjQqIvajgG7Q/Gr0Box8aPLCUGvEFSQeGZ/rd0hBIwVmfE4xD2xNJToFYKPnG7oISMhiowEXRRHqWATHRFGUVocZ5tCaz6CJgQfcc9Q1qsDpYLVquwEWnqHqe8MnW4jTQg+MDLm4nxLr3YXKRXEVluLWr1+tsXmSPxHE4IPnG/pZdRptIaRUkEsPT6S1LhIXtOEoBbihLXIxpX5yTZHopSaLxFhVXYC+6raGRxx2h2OX2hC8IFj9V0kRodTlBZrdyhKqQVYnZ3A8JiLd6va7A7FLzQh+MCxum7WFyTrGghKBbnitDhiI8PYEyLdRpoQvGxo1ElFcy9X5mvJCqWCXXiYgxuWp7PnbAvuiv6LmyYELzvV0IPTZXT8QKlF4rbVmTR2D4VEbSNNCF52vL4LgPWaEJRaFG5dnQkQEt1GmhC87FhdF5kJUWQnRdsdilLKC7ISo1mXl6gJQc3d8Xr3gLJSavG4bVUmR2o76ewfsTsUn9KE4EXdg6NUt/WzXgeUlVpUbluThcvAG+da7Q7FpzQheNH4Oqw6oKzU4nJlXhLp8Yt/1rImBC86Zg0o6y2nSi0uDodwy6pM3qhoYczpsjscn9GE4EWHL3ZRnB5Hcmyk3aEopbxsy+pMeobGOHSx0+5QfEYTgpe4XIaDNR2UFaXaHYpSygduWJFORJiwp2Lxdht5lBBEZKuIVIhIpYg8PMXzUSLyrPX8fhEpmvDcI9b2ChG5Y8L2p0SkRUROeuNE7HaupZfuwVHKijUhKLUYJURHUFacuqjLYc+aEEQkDPgOcCdQAtwjIiWTmt0PdBpjlgPfAh6z9i0BdgBrga3A49bxAH5sbVsUDlzoANCEoNQiduuqTM4191G3SNda9uQKoQyoNMZUG2NGgJ3AtklttgFPW4+fA7aIu7LbNmCnMWbYGHMBqLSOhzHmTaDDC+cQEPZf6CA3KZr8lBi7Q1FK+cjtJdkAvHyqyeZIfMOThJAH1E34ud7aNmUbY8wY0A2kebhv0DPGcOBCB2XFqVrhVKlFrDAtljU5iSGdEKb6hJtc9m+6Np7sO/OLizwgIuUiUt7aGpiTQmraB2jtHaasOM3uUJRSPnbH2izKL3bS2jtsdyhe50lCqAcKJvycDzRM10ZEwoEk3N1Bnuw7I2PME8aYUmNMaUZGxlx29ZsDF9oBHT9QKhRsXZeNMfDK6Wa7Q/E6TxLCQWCFiBSLSCTuQeLdk9rsBu6zHm8H9hh38fDdwA7rLqRiYAVwwDuhB4791R2kxUWyLCPO7lCUUj62KiuBJWmxi7LbaNaEYI0JPAS8DJwBdhljTonI10TkLqvZk0CaiFQCXwYetvY9BewCTgMvAQ8aY5wAIvKfwD5glYjUi8j93j01/9mv4wdKhQwRYevabN6taqNnaNTucLwq3JNGxpgXgRcnbfvKhMdDwN3T7Pso8OgU2++ZU6QBqrq1j0tdg3z+5qV2h6KU8pPb12bz/Ter2XOmhU9ctXjuk9GZygs0XiP9NmsRDaXU4ndVQTLZidH85vichkQDniaEBXr1TDOrsxPIT4m1OxSllJ84HMK2DbnsrWilYxGtkeBRl5GaWvfgKAdrOvn8TcHXXfTM/lq7Q1AqqH3iqjy+/2Y1vznewGevLbI7HK/QK4QFePNcK06XYcsa7S5SKtSsyUlkdXYCLxy5ZHcoXqMJYQH2nG0hJTaCDQUpdoeilLLBH12Vx5HaLi609dsdildoQpgnp8vwekULt67KJMyht5sqFYru2pCLCPxykVwlaEKYpyO1nXQNjHKbdhcpFbJykmK4blkaLxy5hMs1p6o8AUkTwjztPtZAVLiDm1YGZjkNpZR/fLK0gNqOAd44F5i11uZCE8I8DI06+eWRS2xdl01idITd4SilbHTnuhwyE6L40bs1doeyYJoQ5uHlU030DI3xydKC2RsrpRa1yHAHn7lmCW+ea6Wypc/ucBZEE8I8/Ly8nrzkGK5dquWulVJwz+ZCIsMcPB3kVwmaEOaormOAd6rauLs0H4feXaSUAtLjo7hrQy7PH66nezB4C95pQpij5w/XA7B9U77NkSilAsl/vb6IgREnP36nxu5Q5k0TwhwMjjh5Zn8tNyxP19pFSqn3WZubxJ3rsnnizSra+oJzNTVNCHPwH+9dpKV3mIduXW53KEqpAPS3d6xiaMzFt187b3co86IJwUN9w2N8940qblyRzmYdTFZKTWFpRjz3lBXwzP7aoCxnoQnBQz96+wId/SP899tX2R2KUiqA/eWWFUSGO3j0t2dwryQcPDQheKC9b5gn3qrmwyVZbChItjscpVQAy0yI5q8/tJJXzzTz7ME6u8OZE00IszDG8LfPHWd41MXf3qFXB0qp2d1/QzHXL0/jH399mqrW4JmspglhFk+9U8Oesy383UdWszIrwe5wlFJBwOEQ/uWTG4iOcPCX/3mEwRGn3SF5RBPCDI7Xd/H1353hQ2uyuO+6IrvDUUoFkazEaL65fT2nG3v43E8OMjQa+ElBE8I0Dl3s5DNPHiAjPopvbr8SEZ2VrJSamw+VZPF/t6/n3ap2Pvd0ecAnBU0IU3jjXCuf/uF+UmIjePbz15ISF2l3SEqpIPVfNuXzze3reaeqjT9+/F0qmnrtDmlaHiUEEdkqIhUiUikiD0/xfJSIPGs9v19EiiY894i1vUJE7vD0mHZo7xvm4eeP86c/OkBRehw//8J1FKTqjGSl1MJs35TPDz5TSnPPEB//97d5fG8l/cNjdof1AeGzNRCRMOA7wIeBeuCgiOw2xpye0Ox+oNMYs1xEdgCPAZ8SkRJgB7AWyAVeFZGV1j6zHdMvjDGcaujhl0cusau8joERJ5+7oZgvfWgl8VGz/vMopZRHPlSSxcuFN/F3vzjBN16q4Ht7q7h38xI+vj6HkpzEgOiW9uQTrwyoNMZUA4jITmAbMPHDexvwVevxc8C/i/vstgE7jTHDwAURqbSOhwfH9Jr+4TH6h8foGx6jc2CUxu5B6jsHOXGpm6O1XVzqGiQiTNiyOou/uWMlyzP1biKllPelx0fxxGdLOVzbyQ/fquaJN6v43htVZCREcXVRCisyE1iaEUdmQjTp8ZHER4cTHR5GTGQY0RFhPo/Pk4SQB0ycXVEPbJ6ujTFmTES6gTRr+3uT9s2zHs92TK+56p9eYWTM9YHteckxbChI5s9vWcZHr8jRsQKllF9sLEzh8T/ZREvvEG+da2PvuVZO1Hfx0skmplqaOS0ukkP/68M+j8uThDDVdczkkKdrM932qcYuppzjLSIPAA9YP/aJSMU0cXoiHWgb/+Ei8C7w+AIOaKP3nUsQWyznAXougWjB5/EnXgpkIS4C8pV5n8sSTxt6khDqgYlrReYDDdO0qReRcCAJ6Jhl39mOCYAx5gngCQ/inJWIlBtjSr1xLLstlnNZLOcBei6BaLGcB/jnXDy5y+ggsEJEikUkEvcg8e5JbXYD91mPtwN7jLuq025gh3UXUjGwAjjg4TGVUkr50axXCNaYwEPAy0AY8JQx5pSIfA0oN8bsBp4EfmoNGnfg/oDHarcL92DxGPCgMcYJMNUxvX96SimlPCXBVp51IUTkAasLKugtlnNZLOcBei6BaLGcB/jnXEIqISillJqelq5QSikFhEhCCMQyGXMhIjUickJEjopIubUtVUReEZHz1t8pdsc5FRF5SkRaROTkhG1Txi5u37bep+MistG+yD9omnP5qohcst6boyLykQnPTVm2xW4iUiAir4vIGRE5JSJfsrYH1fsyw3kE43sSLSIHROSYdS7/aG0vFnc5oPPiLg8UaW2ftlzQghhjFvUf3IPWVcBSIBI4BpTYHdccz6EGSJ+07RvAw9bjh4HH7I5zmthvAjYCJ2eLHfgI8Dvc81euAfbbHb8H5/JV4G+maFti/a5FAcXW72CY3edgxZYDbLQeJwDnrHiD6n2Z4TyC8T0RIN56HAHst/6tdwE7rO3fA/7cevxF4HvW4x3As96IIxSuEC6X3jDGjADjZTKC3Tbgaevx08AnbIxlWsaYN3HfeTbRdLFvA35i3N4DkkUkxz+Rzm6ac5nO5bItxpgLwMSyLbYyxjQaYw5bj3uBM7grCATV+zLDeUwnkN8TY4wZX1otwvpjgNtwlwOCD74n4+/Vc8AWq1zQgoRCQpiq9MZMvzSByAC/F5FD1sxtgCxjTCO4/2MAmbZFN3fTxR6s79VDVlfKUxO67oLiXKyuhqtwfyMN2vdl0nlAEL4nIhImIkeBFuAV3FcwXcaY8bKoE+N9X7kgYLxc0IKEQkLwpPRGoLveGLMRuBN4UERusjsgHwnG9+q7wDJgA9AI/LO1PeDPRUTigeeBvzLG9MzUdIptAXMuU5xHUL4nxhinMWYD7soNZcCaqZpZf/vkXEIhIXhSeiOgGWMarL9bgBdw/7I0j1+2W3+32BfhnE0Xe9C9V8aYZus/sgv4AX/oggjocxGRCNwfoj8zxvzC2hx078tU5xGs78k4Y0wXsBf3GEKyuMsBwfvjvXwu8v5yQQsSCgkhqMtkiEiciCSMPwZuB07y/nIh9wG/sifCeZku9t3AZ627Wq4Buse7MALVpL70P8L93sD0ZVtsZ/U1PwmcMcb8y4Sngup9me48gvQ9yRCRZOtxDPAh3GMir+MuBwQffE+mKhe0MHaPrvvjD+67JM7h7pP7e7vjmWPsS3HfGXEMODUeP+7+wteA89bfqXbHOk38/4n7sn0U97ea+6eLHfdl8Hes9+kEUGp3/B6cy0+tWI9b/0lzJrT/e+tcKoA77Y5/Qlw34O5eOA4ctf58JNjelxnOIxjfkyuBI1bMJ4GvWNuX4k5alcDPgShre7T1c6X1/FJvxKEzlZVSSgGh0WWklFLKA5oQlFJKAZoQlFJKWTQhKKWUAjQhKKWUsmhCUEopBWhCUCFCRPqsv4tE5F4fv9ZXRcSIyPIJ2/7a2lZq/Txe0vy4iLwhIksmtHVaZZtPisjPRSTWl/EqNU4Tggo1RYBPE4LlBNba4pbtuNcWn+hWY8yVuMsU/MOE7YPGmA3GmHXACPAFXwaq1DhNCCrUfB240foG/tdWhclvishB69v65wFE5Bbrm/suETknIl8XkT+xFjE5ISLLZnmdX2KVWReRpbirUbZO03Yf01fdfAtYPs1zSnmVJgQVah4G3rK+gX8Ld/mJbmPM1cDVwH+z6twArAe+BFwBfAZYaYwpA34I/MUsr9MD1InIOuAe4NkZ2m7FnUDexypadifuqw2lfE4Tggp1t+Mu3HYUdy39NNxFzwAOGvciLMO469/83tp+AnfX02x24u42+gTuKrWTvS4iLbgLmT0zYXuMFU85UIu7gJtSPhc+exOlFjUB/sIY8/L7NorcAgxP2OSa8LMLz/7v/Br4JlBujOmZYkGrW4F+4MfA14AvW9sHjbsuvlJ+pVcIKtT04l5/d9zLwJ9bdfURkZVWmfEFM8YMAv8TeHSWNn+F+yol1Ruvq9R8aUJQoeY4MCYix0Tkr3GPB5wGDovISeD7ePHK2Riz01jr/s7QphF3ae0HvfW6Ss2Hlr9WSikF6BWCUkopiw4qKzVPIvL3wN2TNv/cGDPtmIFSgUy7jJRSSgHaZaSUUsqiCUEppRSgCUEppZRFE4JSSilAE4JSSinL/wdHoQ2bf1FTcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item MRP\n",
    "sns.distplot(train.Item_MRP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Item MRP\n",
    "\n",
    "Item MRP shows 04 different distribution which means it needs treating/Transforming of the Values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12425c4a8>"
      ]
     },
     "execution_count": 21,
     "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": [
    "# Item MRP Vs Sales\n",
    "plt.scatter(train.Item_MRP, train.Item_Outlet_Sales, color= \"hotpink\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Cat Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1242f35c0>"
      ]
     },
     "execution_count": 23,
     "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": [
    "# Item Fat Content\n",
    "sns.countplot(x = \"Item_Fat_Content\", data = train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Item Fat Content\n",
    "\n",
    "Fat Content is showing Multiple categories of Two Major Categories which are Low Fat and Regular. Hence, all the remaining cats will be merged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the Item Fat Content with the resp Cats...\n",
    "train.Item_Fat_Content = train.Item_Fat_Content.replace(to_replace=[\"low fat\", \"LF\", \"reg\"],\n",
    "                          value = [\"Low Fat\", \"Low Fat\", \"Regular\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124381908>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"Item_Fat_Content\", data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the Item Fat Content with the resp Cats...\n",
    "test.Item_Fat_Content = test.Item_Fat_Content.replace(to_replace=[\"low fat\", \"LF\", \"reg\"],\n",
    "                          value = [\"Low Fat\", \"Low Fat\", \"Regular\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12491f400>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"Item_Fat_Content\", data = test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Type\n",
    "plt.figure(figsize=[10,7])\n",
    "sns.countplot(x = \"Item_Type\", data = train)\n",
    "plt.xticks(rotation= 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Item Type\n",
    "\n",
    "1. Fruits and Veggies\n",
    "\n",
    "2. Snack Foods\n",
    "\n",
    "3. Frozen Food\n",
    "\n",
    "These are the most saleable items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124a45cf8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hmom3ahcpxcuAUJIJBoPs27fPzdBMmGpra/H5/AxOD+APBGwR0QTTPrHYqL4wOu+ddcKmAv1F5D/a3dWPdjtJGve0JQ9Paz20hkYUtTWL7du3j/z8fLdCM2FqGxp+TH8/ZU1eSktLycnJcTkqE662EX1go/ogvBrLJOBrQA5wSbvLScD/jV1oJlz79u1D0rJo380VdDryrcaSGA4cOADApJzQHIjS0lI3wzERap9MLLGEUWNR1VeBV0XkDFVd1gMxmQjtLykhkJx1aGFSKpKUbHNZEkTb6zR1YCix2FDxxNJWY5FMsaWUiKyPpVJElojIRgAROV5Efh2juEwEDhw4QDDlsMQigqZk2zffBLF//37Sk4Uh6UEyk8USS4Kpq6sDD2ia2jwkIkssjwA3Aj4AVV1PaC8U46JAIEBlZSXB1MzP3edPzqC01JZ1SQR79+5lSHoAERic7rcmzARTW1uLJ9WDpig1tTbwIpLEkqGqh6/DZYviuOzgwYOhfVhSPp9YNCWT0jKrsSSC/Xv3MDgt9HYanB5g316b3JpIqqur0RRFU5Vqm+AaUWKpEJHxODPsReRyQnupGBe1dfq2rQ/WnqZmUVtTc8jkLRN/AoEAB0rLGJweWnp9cHqA0rIyW8wwgVRXVxNMCUIq1FbX9vl5SJEklmuBh4DJIrIP+AnwPzGJyoStrS3+c30sQNAWo0wIFRUV+AMB8tJD66cOTg8SCASpqKhwOTITrvLKcjRNIS20unH74cd9UdiJRVV3qOqXCW1LPFlVz1LVXTGLzIRl9+7dztpg/T53X9tcluLi4p4Oy0SgbYBFblrwkJ9ttVET31SVyorK0Kw+Z2ZfX/9SEM4EyZ8doRwAVb0ryjGZCOzcuRPS+oHH+7n72pZ42bVrF1/60pd6OjQTpsMTyyDnp43oSwy1tbX4Wn2QAZoRagIrKytj7NjPrYLVZ4SzbP7nG+8/07cbEuPA5oIt+NMHdXynNwXSc9iyZUvPBmUi0pZABh2WWGyjtsTwaT9npn66WXtfr22GM0HyNgAROVNVD1loUkTOjFVgpnMVFRVUVpQTGD3uiMf4M3LZsHETqvppLdPEl7KyMjJThDTn3ZjqhexU6fMfToni0zlHmUA6iFf6/HDxSDrv/x5mmekhn3zyCQDB7KFHPCbQbyi1NdXWzxLH9u/fT17aoRuf5qX6LbEkiL1794autK2qlEWf3wspnD6WM4AvAnmH9bf0Az7fsG96zJo1a5CkVIKZR2gKAwL9hgOwevVqW4wyTu3bu4cRaYcOLR6cHqB4j+2nkwiKi4vxZHoIJIW+HASzguzctdPlqNwVTo0lhVAuTiLU39J2qQUu7+qJRSRHRF4QkS0iUiAiZ4jIQBFZLCJFzs8BzrEiIveKyDYRWS8iJ7V7nlnO8UUiMqur8SQaVWXZ8uX4+g0HOfLLqGn9IL0fH398+NxWEw9aW1spOVDKsIxDayzDMgKUlpbZHKQEsH3HdgJZn71+2k8p2V/Sp1+7cPpY3gPeE5EnVDWa7Sn3AG+q6uUikkKo2+smYImq/kFEbgBuAH4FXAxMcC6nAQ8Ap4nIQOAWYDqhgQSrRWSeqh6MYpxxaefOnVRVVhIYe2ynx/qyR7B6zRpaW1tJSUnpgehMuIqLiwkGg4zMCjB3azoA35nUxMisAEFVdu3axaRJk1yO0hyJ3++nuLgYHavIWqcPc1Doi19ffu0i6WN5QkTePvzSlZOKSD/g34DHAFS1VVWrgZnAk85hTwKXOtdnAnM0ZDmQIyLDgIuAxapa5SSTxcCMrsSUaNpqIIH+Izs9NpAzktaWFjZs2BDrsEyEioqKABiTHaC4zktxnffT2+3vN/Fpz549+H1+yAGpFqRa0JzQYNnt27e7HJ17Ikks1wO/cC7/C6wFVnXxvOOAcuBxEflERB4VkUxgiKqWADg/BzvHjwDa94btdcqOVN7rfbxyJWQMQFM/P+P+cIF+w8HjseawOFRQUEB6sjA0I3hI+WBnleOCggKXIjPhaEv8bckEgCyQJOnTXwrCmccCgKquPqzoQxF5rxvnPQn4kaquEJF7CDV7HUlH42T1KOWffwKRa4BrAEaPHh1ZtHHG5/Oxbt06fAMmhPcAbzLBrMGsXrMmtoGZiG3csJ7x2a14DvtP9giM69fKxg3r3QnMhKWwsBDxyqGz/QS0v7K1cKtrcbkt7BqL07HedskVkYuAI49zPbq9wF5VXeHcfoFQoil1mrhwfpa1O35Uu8ePBPYfpfxzVPVhVZ2uqtPz8vK6GHZ8KCgowNfaSqDfsLAf488eRlFhoe1uF0fq6urYsXPXp7tGHm5Sfz+7infb/h5xbOvWraHaymGfpMEBQYqKiggGgx0/sJeLpClsdbvLMuDnwNVdOamqHgD2iEhbz9b5wGZgHtA2smsW8KpzfR5wlTM67HSgxmkqWwhcKCIDnBFkFzplvVpbX0kge0jYjwlkD0FV2bRpU6zCMhHauHEjqnrExDJ5gB9VZf16q7XEo2AwSGFRIcH+HSSPHGhpbumz81kiaQqL9sI3PwKedkaE7QC+RyjRPS8iVwO7gW84xy4AvgJsAxqdY1HVKhG5HVjpHPdbVa2KcpxxZ+PGjZCeA8npYT8mmDUYRNi4cSOnnnpqDKMz4Vq7di1eD4zv33FiGdfPT5IH1q9fz1lnndXD0ZnO7N+/n6bGJhjw+ft0QKhFvrCwkDFjxvRwZO4LK7GIyGBCy+ZPJdSHsRm4T1W7vJiRqq4lNEz4cOd3cKw65+/oeWYDs7saR6JRVTZu2oQvM8LmPG8KZAxg8+bNsQnMRGzD+nWM6xcg9QjTjFO8oeSyft26ng3MhKWwsBD4LIkcol9oaZeioiIuuOCCHo7MfZ02hTnrgbXVCOYATznXP7a1wnpeWVkZNdXVBCNNLIAvI5dNmwv6/CZE8cDn87G1sJAJ/XxHPW5Cfz9FRYV9erJdvNq6dWvoE/TzO1aAJ9SB31cXgA2nj+WvwKWqeouqzlPVV1X1FkJzTGzJ/B7WNvw0mBV5YglmDaahvu6zRfOMa7Zv347P5z9iM1ibY/r78fkDfXpORLwq2FIAORxxYavggCBbtm4hEAh0fEAvFk5i6aeqnxxe6DRlHW1JfRMDmzdvBo+XYMbAiB/bVsuxDnz3tTWjjM0++odO2/1tx5v44Pf7KSgoIDjwKKO+BkFzUzO7du3qsbjiRTiJRdrW7DqscGCYjzdRtH7DBoKZuR1u7NWZYMYAJCnZEksc2LZtG+lJQl760YejDkoLTZS0Gkt8KSwspKW5BXKPfIzmhpqc165d20NRxY9wEsPdwCIR+ZKIZDuXc4A3nPtMD2lpaWHr1q34swZ3fnBHxIM/I4+1a60z2G07dmxnZJafzrbIEYGRmT62b9vWM4GZsKxcGep21sFH6a/MBMmST4/tS8JZhPJhEdkP3M6ho8J+p6rzYxyfaWf9+vUE/H6CzlL4XRHoN5ydO1dx8OBBBgzoYJykiTlVZeeO7ZzaSf9Km1FZfj7ascM2a4sjSz9cCgOB1KMfFxgSYOWqlTQ3N5OWltYjscWDsJqyVPU1Vf03VR2kqrnO9UOSiojcGJsQTZuPP/4YPB4CR9nYqzOB/qGl1Prit6h4UVpaSl19I6Oyw0sso7MCNDY1UVJSEuPITDhKS0vZumUrweGdz6rXEYqv1dfn1umLZh/JNzo/xHSVqvLe++8TyB4O3uQuP08wMxdJyWDp0qVRjM5Eom0Iamcd923y+wUOeZxx19tvhxZ111FhDNvPA0kTFi9eHOOo4ks0E4vV0WNox44dHCgpwT+gm7N4RWjNGc2yZctpbm6OTnAmIuvWrSM1ST5dGr8zo7MCpCYJ62yipOtUlQVvLIBBhLY/7IwHAiMDfPjRh9TV1cU6vLgRzcRis+5i6K233gp1vg/s/so6/kHjaGlp5qOPPopCZCZSH69YzoR+rSSF+e5L8sCk/q2s/HhF5webmCosLKR4VzHBMeEvLqn5it/n/7Sm0xdYjSUBBAIBFi5cFOofSe5+B2AwexiSmsnChb1+vc64s3v3bvbs3cdJeUefcX+4L+T62Ltvf5+cExFP3njjDcQr4TWDtckByRFee/212AUWZ6KZWP4Vxecy7axZs4aKinJ8uWHuv9IZEVoGHsOKFSuorKyMznOasCxatAiPwPS81ogeN31waM+WRYsWxSgy0xmfz8eixYsIDA9AJDt8CwRGB9i6ZSu7d++OWXzxJJL9WMaKyF0i8pKIzGu7tN2vqnfGJkTz+uuvI8mpBAZEb4Myf94EgsGg1Vp6kN/v540FrzN1oI+BaZG1HA9IVY4b6OONBa/j94c3msxE18qVK6mvq0dHR97qr6MVhD7TiR9JjeUVYBfwd0Lrh7VdTAzV1tby/gcf0DpwPHjC3uWgU5qeQzB7CPPnv2aLUvaQpUuXUl5RyZdHdm1ByS+PbKGy6iDvvdfVjVtNdyxZsgRJla5tb5gO5MFbS97qE++3SBJLs6req6rvqOp7bZeYRWaA0Dccv8+HP29i1J/blzuRffv2frpxmIkdVeXpp+YyJEP5Qm5k/SttTsj1MTRT+eczT/eJD6d44vf7WfrhUgLDAl3uQAiODLJv7z6Ki4ujG1wciuRPdI+I3CIiZ4jISW2XmEVmUNVQjSIzN7Q+WJT5B41DkpJ57bW+06nolhUrVrC1sIivjWn83P724fIIXDK6kcKibSxfvjy6AZqjWrduHU2NTeiIrid0HR56bF+YQxZJYjkO+L/AH/isGewvsQjKhGzdupUdO7bTmhv92goA3mRaB4zj7Xfeob6+PjbnMASDQR55+CHyMuDsYZF12h/uzGGtDMlQHn7owT65HLtbPv74Y8Qj0MVl+gBIBxkgrFjR+4eNR5JYvg6MU9Uvqeq5zuW8WAVmYP78+Yg3CX/u+Jidwz94Eq0tLaF5MiYmFixYQNG27XxjXH3Yc1eOJMkDl49rYPuOnbz++uvRCdB0auWqleggjWAz944FBgfYuGljr9+4LZJ/83WEtrWJGhHxisgnIvKac3usiKwQkSIReU5EUpzyVOf2Nuf+/HbPcaNTvlVELopmfG6qq6tj0aLFtA4cB0mdrHTXDcHMPDQzlxdffMna7WPg4MGDPPjA/UzMCXDGkK71rRzu9CE+Jg/w89CDD9hw8R7Q0NDA9m3bCeaFPynySDRPCfgDvX6L8EgSyxBgi4gs7Gi4cRf9GChod/uPwN2qOgE4CFztlF8NHFTVYwgt1f9HABGZAnyT0KrLM4D7RSTyjUri0Pz582lpacY/ZGpsTyRC65ApFBfvYtWqVbE9Vx+jqvzpT3+kqbGe70+u73SJ/HCJwPcmN9Dc2MCf/vRH+0IQY5s3b0ZVP91fpVsGhX709gEzkSSWWwg1h91JFIYbi8hI4KvAo85tAc4DXnAOeZLQ9scAM53bOPef7xw/E3hWVVtUdSewDTi1qzHFi5aWFp577nkC/YcTzBwU8/P5B41HUjOZO/epmJ+rL3nhhRf48MOP+Ma4RkZmdf/bbnsjMoP81zGNLFu2nH/9y+Ymx9LGjRtDVyLftPXzUkD6Cxs3bYzCk8WvsBOLM7R4F5DsXF8JrOnGuf8G/BJoe8cNAqpVtW32115ghHN9BLDHicMP1DjHf1rewWMS1iuvvMLBg1W0Dv9Cz5zQ46V56HGsXfsJa9Z05yU1bdasWcN9993HyXk+ZoyOTXv6RaNamJ7Xyv3338/q1atjcg4Tql1IjkDXFxU/RGBggA0bNxAMRvfLRjyJZOb9/yVUW3jIKRpBaNJkxETka0CZqrZ/N3TUUKCd3He0xxx+zmtEZJWIrCovL48o3p5UV1fHk3Pmhmor/Yb12Hn9gycjqZnc/8ADvfofvifs2LGDm2+6kaEZAX4wtb7Lw4s7IwI/mNrA8IwAN990k21fHAM+n49169cRyI3iCLw8aKhrYFsv3hU0kqawa4EzgVoAVS2i64PvzgT+XUR2Ac8SagL7G5AjIm3jLkYC+53re4FRAM79/YGq9uUdPOYQqvqwqk5X1el5eXldDDv2Hn/8cerr62gddVrPntiTRPOI6RRu3WrLvHRDSUkJ1//8ZyQHm/jliTVkRG+xhA6lJ8EvTqwhVZuZuotQAAAgAElEQVS4/uc/Y//+Dv/9TRetWbMGX6sPHRK9fqy27YyXLVsWteeMN5EklhZV/XQQvvMB36W/tqreqKojVTWfUOf726r6beAd4HLnsFnAq871ec5tnPvf1lCP5Tzgm86osbHABCBht2rbvn07L730Er68SRH3raQUL8PTWImnsZK0za+RUhz5P60/9xg0azD33f9An9o7IloqKir46Y+vo6nuIL88oZbcCNcD66pBacovT6yhpb6an/7kx8RzjTzRvPXWW0iyhIYuRUs6kAuLFi/qtQMvIkks74nITUC6iFxAaDXjaO95/yvgZyKyjVAfymNO+WPAIKf8Z8ANAKq6CXge2Ay8CVyrqgk5aywYDPKnP/0Z9abSOnJ6xI/3NFQiAR8S8OGtO4CnoQvDUEVozv8itbU1PPTQQ50fbz518OBBfvLj66iqKOMXJ9QwOsxNvKJlVFaQX5xQQ3VFWSiOqqoePX9vVFVVxVtL3iIwOgBRHmsaHBtkz+49vbZvLJLEcgNQDmwAfgAsUNWbuxuAqr6rql9zru9Q1VNV9RhV/Yaqtjjlzc7tY5z7d7R7/B2qOl5VJ6nqG92Nxy3z58+noGAzzaNOjcqeK10VzMzFN2Qq8+bN+2w0jDmqmpoafvqTH1O6fx/Xn1DLMf3d+W4zvn+A60+soezAfn76kx9TU1PjShy9xdy5cwkEAuiE6NcqdJQi6cIjjzzSK2stkSSWH6nqI84H/OWq+oiI/DhmkfUhVVVVPPDggwT7Dcefe4zb4dA68iQkNYs//+UvtkR7J+rr67n+5z9jT/Eufnp8LZMHuPv3mpQT4OfH17J3z25+/rOfWpNmF23fvp2XX3mZ4NggZMfgBF4ITA1QUFDQK/fYiSSxzOqg7LtRiqNPe/DBB2lsaqI5/4tEbRZdd3hTaBp9Ojt37OCVV7o08K9PaGlp4aYbb2BbURHXHV/HtEHxkYSnDPTz4+Nq2b59GzfecEOvXz4k2nw+H7f/7nY0WdFpsatNaL5CLtz9t7spKyuL2Xnc0GliEZErRGQ+MLb9jHsReQew9SS6acuWLbz55pu0DpmKpkd1xZxuCQwYQ6D/CB6bPduaVDoQCAT43e23s3bden4wtZ4v5MZHUmlzYq6f/5lSz4YNG7jttttswcoI3HfffezYvgP/SX6I3WpKoZ0lpwdoamnilltv6VWtA+HUWD4iNMN+C4fOuP85oWVUTDc88MADSEo6vp6aDBkuEVpGn0ZDQwNPPWUz8g/30EMP8d777/PtCY18cWh01gCLttOH+rhyYiNLly7lgQcecDuchLBo0SJeeuklghODPTPVOhsCJwfYtHET//jHP3rghD2j08SiqsVOB/sZ7Tf4UtU17WbJmy5Yu3Ytn3zyCc1Dj4ekSDbR7hmaMRDfoPG89PLLtthhOwsWLODZZ5/lgpHNXDwmOs1Mc7emU1znpbjOy+9WZTF3a3pUnvei0S1cOKqZ559/3vbd6cTmzZv5wx//AINBj+u5DnUdrQQnBHnppZeYPz/aA23dEU5TWJ2I1HZwqROR2p4IsreaO3cukpKBf8ixbodyRL4RX8Dn8/HCCy90fnAfsGXLFv76178wdaCfKyc2Re15i+u8NAU8NAU8bKlOprgueuNbr5zYxLSBfu6+66+9flXdriorK+OGG28gkBogcHrXd4nsKj1eYSj89a6/snbt2p49eQyEU2PJVtV+HVyyVbVfTwTZG+3atYuVK1fSMvjYqO5lH22a1h9/zhheefVVmpub3Q7HVfX19dzym/+lX5KfH06rx9vDHz5d5RG4dlo9/ZMD3HrLb2xTt8O0tLRw4003UlNfg/+LMe5XORIPBE4LoJnKTTffxIEDB1wIInoS5K3R+7z00kuIx4tv8GS3Q+mUb+g0Gurr+/xmYHfffTelpaVcO7WW7JTEmnuQnaJcO7WWsrIy7rrrLrfDiSv33HMPRYVF+E/1hxaLcksK+L/op6GlgV//769pbe3ebqNussTigqamJhYuXIRvwFhIjk5beiwFs4dAxgBeeeXVzg/upT788EMWL17MzPwmJuYk5girCTkBvj62ibfeeosPPvjA7XDiwrvvvstrr71GcHIQhrsdDZAN/ul+CrcW8thjj3V+fJyyxOKCJUuW0NTUiG9I/NdWgNAIsbzJFBZuZevWrW5H0+MaGxv561/+zKjsIDPHJnZz4CX5zYzODnLXX/9CY2Oj2+G4qrq6mj/9+U8wEHRq92qgslagGqgGz7ue0O2uGhFa8uXZZ59l06ZN3YrLLZZYXPDyK69CxgCCWdFc2S62/LnHIN4k5s3r7qahiWfu3LlUVFbxvUnd37PebUke+P7keiqrDvLkk092/oBe7NFHH6W+vp7A9O531ku1ID7nUi5IdfcmOusJCulw1913JeQcpAR/mySezZs3U1S4lZa8yfExyz5cSam0DhzHwkWLqK3tO4MBS0pKeP65ZzlraEvCNoEd7pj+Ac4e1sIL/3q+zy6zX1xczPz58wkeE3S3X+VIkiEwLUBRYRFLlixxO5qIWWLpYc899xySlIo/b4LboUTMN3QqrS0tfarW8uijj4IG+MYx0RtaHA++Mb4J0SAPP/yw26G44vHHH4ck0GPjdxCGjlYkR3hs9mMJNyvfEksP2rNnD++++y4teZPAG38TIjujGYMI9B/Bc8//q08MPS4sLGTx4sXMGNXEoB7aW6WnDExTZoxq5O233+5z/WbFxcW88847BMYH3BlaHC4B/xQ/JftLeOedd9yOJiKWWHrQk08+CR4vvmHT3A6ly1qHn0hN9cFeX2tRVR64/36yUuBrUZpdH2++lt9Mdgrcf/99vXLp9iOZM2cOeEEnJsDvPBykv/D4E48nVF+LJZYesn37dhYvXkxr3rGQnOF2OF0W7DeMQP/hPDlnTq9ekn3ZsmWsXrOGr+c3kpmcAB9AXZCRBP8xtpFPPlnL0qVL3Q6nR+zcuZO33nor/msrbZxay949e1m8eLHb0YTNEksPUFX+/ve/Q1IKrSNOcDucbmsddSp1tXXMnTvX7VBioqWlhXvv+RvDMpXzR/bO2kqbc0e0MCJL+cff7+31y+urKvf+/V5IBp2UQF8WRgAD4cGHHqSpKTH6+iyx9IC3336bNWvW0Dz8C5Dk3u6Q0RLMzMWXN4Hn//Uvtm/f7nY4Uff000+zv+QAsyYm/vDiziR5YNbEekoOlPbaLwpt3n33XVavWk1gSoLUVtoIBE4IUFVZFRp0kABceduIyCgReUdECkRkU9tOlCIyUEQWi0iR83OAUy4icq+IbBOR9SJyUrvnmuUcXyQiHW1G5qrq6mruuvtuNGsw/iFTYneiQCvp6elcfvnlpKenQyC2y0G0jjoV9aZw5+//kHAjVo5m9+7dPP3UXM4Y0ho3G3fF2pSBfr44tIVnnn6aXbt2uR1OTFRXV/PXu/8amgw5PoFqK21yITguyHPPPZcQC4m69X3MD/xcVY8FTgeuFZEpwA3AElWdACxxbgNcDExwLtcAD0AoEQG3AKcBpwK3tCWjeBAMBrnjjjupr2+gaexZILH7c4u/la9+9atcd911fPWrX0X8MV5nKDmNptFnUFS4NWG+RXVGVfnLX/5MigS5cmLfmpX+7YlNpHpDM/J7W0e+qvLnv/yZuro6Aif3/MrF0aLHK2TA7b+7Pe5HZbryJ1bVElVd41yvAwoItSTOBNqmAz8JXOpcnwnM0ZDlQI6IDAMuAharapWqHgQWE0ebjz3zzDOsWLGc5tGnoRkDY3ouTUrh9ddf59577+X1119He2B/l8CgcfjyJjF37lyWL18e8/PF2pIlS1i7dh3/Ob6e/qm968O1M/1TlG+Or2ftuvUJ1UkcjkWLFvHB+x+EmsDiZ5PWyCWH1hHbt3df3G/c5nruFpF84AvACmCIqpZAKPkAg53DRgB72j1sr1N2pHLXffTRRzzyyCP4B47DP7gH9lvxptDU1MQLL7wQ6uDroXkyrflnoJmDuOXW2xK6GaW1tZWHH3qQMdlBzh3R86vKNvnlkKbMJn/Pr8rwpeGtjO0X5JGHHuw1HfllZWXcdfddkJtgHfZHMhiCE4K8/PLLrFq1yu1ojsjVxCIiWcCLwE9U9WjrhHT0LtOjlHd0rmtEZJWIrCovL4882Ahs27aNW265lWBmLi3jzk6spVsi5UmiacKXafYH+cUvf0V1dbXbEXXJokWLOFBaxjePacDjwsvV6JdDmjIbXUgsHoH/Gt9AaXkFixYt6vHzR5uq8pe//oXm1mYCpwQ6/rRIQHqcItnCH//0x7gdJeZaYhGRZEJJ5WlVfckpLnWauHB+ljnle4FR7R4+Eth/lPLPUdWHVXW6qk7Py8uL3i9ymNLSUn5+/S9oJfSBizc5ZueKF5qaTeOECygrK+dXv7oh7tt/O/LqKy8zMivItIHudNhnJOkhTZkZSe58u5460M/o7CCvvPxS5wfHuaVLl7J82fJQE1iW29FEkRf8J/spPVAamuwZh9waFSbAY0CBqrbfdWge0Dayaxbwarvyq5zRYacDNU5T2ULgQhEZ4HTaX+iUuaK+vp7rf/ELqmvraJh4IZqS6VYoPS6YNZim8V+ioKCA3/72twk1S7iiooKthUWcNbTZtcplepIe0pSZ7lJiEYGzhjZTtG07ZWVlnT8gTrW2tvL3f/wd6S/ohF7QBHa4PAiODvLsc8/G5UKibtVYzgS+A5wnImudy1eAPwAXiEgRcIFzG2ABsAPYBjwC/D8AVa0CbgdWOpffOmU9LhgM8tvbb6e4eDeNx5wf8876eBQYOJaWMaexdOnShBoptnHjRgCOHdA3hhd3pu3vsGHDBpcj6bp58+ZxoOQA/uP9cdCTHBt6vBIkGJcbgrmy2bqqLuXILZ7nd3C8Atce4blmA7OjF13XzJ07l+XLltEy5gyC/eNi/IAr/EOm4mmsYs6cOUyePJmzzjrL7ZA6VVJSAsCwzMSpZcXS0IzQ36G0tNTlSLqmsbGRJ558IjT0J3G2PIpcOgSOCfDWW2/xrW99i/Hjx7sd0ad6aS7vWbt27WL27Nn4B42P7STIRCBCa/4X0cxc/vDHPyXELoU1NTV4PZDudTuS+JCeBMne0N8lEf3rX/+itqaWwLQe7LD3cegEZV/PnFYnKSTDI4880jMnDJMllii47777wZtCy5gzevcIsHB5kmjO/yK1NdU8/fTTbkfTqUAgQJKIvXTteEUSqp+sTWVlJU89/RQ6XGFQD57YxyGj+noqsZACgUkBPvroI9asWdNDJ+2cJZZu2r9/PytWLKdl6DRITvx1wKIlmDUY/8CxvPjSSwSDQbfD6ZwllUOIkJAz8B988EFaW1sJHt/D/3PJHDKqjx4cDKoTFMkU/nbP3+JmeSVLLN3U1sHpHzDa5UjiTyBnFI0NDXE/cTIRP0BjTSAxvhC08/HHH7Nw4UICEwOQ3cMnT+bQCco9OcvAC/4T/OzauYtnnnmmB098ZJZYumnfvn0AaFrcLFEWN4Lpob/J3r17XY7k6Jqbm0mz/pVDpHpJqNn3VVVV/O6O3yH9BJ3SB78ojIDgqCCzH5/Npk2b3I7GEkt3DR4cWnVGfA0uRxJ/pDX0NxkyJL6H5tTV1bk2ITFeZSQFE6bzvrW1ld/c8htqamvwn+aHPvolQU9SNF359f/+moqKCldjscTSTePGjQPAe3C3y5HEH2/1bpKSkhg1alTnB7uovKyMASk91duaGHJS/FRWuvvhFI5AIMAdd97B+nXrCUxP8EUmuysF/Gf4qaqp4vpfXO/qDq+WWLrp2GOP5fgTTiCtZB0E3PtwCmYOQr3JqDeZQPZQgpk9OSTm86SpmuSKIr7+9a+TkRHfWzFXVpSTk5pY/QmxlpMSpLIitmvqdZfP5+O2227jnbffIXhcEB1ttU5ywH+6n507d/Kj637EwYMHXQnDEks3iQg/uOYa8DWRuv0dUHc+oFrHnEEwYxDBjEE0T/karWPOcCUOAPzNZGxbQnpaOldeeaV7cYSpvr6eTGsKO0RmslJfH7/Nu9XV1fz8+p/z7rvvEjwhiE621+9TQ8F/pp+dxTv5wX//gB07dvR4CJZYouC4447juuuuI+ngblJ2fOBacokLgVbSCxfjba3jzjvvYMCA+B/UkJKaSmvQxhu35wsKKSnxuYBqQUEB37/6+6xbv47gqUF0oiWVzxkK/n/zU1pdyg/++wcsWbKkR09viSVKLrvsMr773e+SXFFE2pY3kNb4n3EebdJYSeameXjry7jlN7/h5JNPdjuksOTnj6WwJgU3Rx2PyQ6Q7g2S7g0yOcfHmGx3JydurUlhTP5YV2M4XCAQYM6cOfzP//sfKhsr8Z/rR8dYUjmiQeA/309LVgu33XYbd9xxBw0NPVMLtcQSRd///ve58cYbSWuuJHPTK3ir43uYbdSoklS2hcxN8xmQ5uGee+7hnHPOcTuqsJ1z7rmUNAiryt37hv6dSU2MyQ4wJjvAr6fX851J7u2zsbosmf31wnnnfW7ZPteUlJTwo+t+xKOPPop/uB//l/0Q/5Vh96VD4EsBglOCLFy0kO9+77s9srioJZYou/jii3nk4YcZMWQQaVvfJLVwMdKcGMM2u8JTV0rG5nmk7lzKF048nscfn82JJ57odlgRufjii5k8aSIPF2Szu66PjlV17Kn38FBBNhMnHMPFF1/sdjhAaMvo737vu2zasongKUH0NIWe2SC1d/CATlUC5wQoqy/jhz/8IbNnz47pBFhLLDEwbtw4Hp89m2uuuYbMplIyNrxI8u4V4IvP3d66QpprSN32Numb5zMwJcjNN9/MXXfdxcCBibddQEpKCr+9/XekZ/XntlX9WVrSNz+1PixJ4daVOaRm9uO3t/+O1NRUV+NRVZ544gluu+02mtKb8F/gR/PVlt/pqlzwf9lPYHSAJ554gltvvTVmk2AtscRIamoqV155Jf985hm+MmMGqQc2krXuOVJ2LUNa3Btf3l2ehgpSi94mY/0LpNfuZdasWTz7z2e46KKL8HgS999p6NChPPrYbI6ddhwPbsrknnWZ7G9I3N8nEiUNHu5Zn8kDmzKZNGUaj81+nOHDh7sdFk8//XTom/WYIIFzAtB39s2LnWTQU5TgCUHeffddbvvtbTE5jSv7sfQlubm53HDDDXzzm9/kmWeeYdHixSSXFeAbNB7/0GmuzzcJiyqe2v2klGzAW7OXtPR0/uOKK7j88svJzc11O7qoyc3N5a67/8YzzzzD00/NZfXyFL40vIWZ+c3kpve+kX4VzcK8nem8uz+VlJRUrr76Sr797W+TlOT+x0JDQ0NoleJhip5itZSoEtCJStAfZOkHS9m6dSuTJk2K6inc/w/qI/Lz87npppu4+uqref7553l13jxaK4oI9htK6+ApBAbkQ7x94w+0klS+jdTyzdBYTb/+OXzzmmuYOXMm2dk9vcpfz0hKSuKqq67ikksuYc6cObz6yiu8tz+Vk/NauWhUC5Ny/Am9vL4qbK1OYtGeVFaVpyAeD5d+/VKuuuqquGrGLC8vp7GhMTTqKwH+3pqjUO3cyHFuxzkdEIpxx44dUU8s0hdXdp0+fbquWrXK1Rjq6up4/fXXefHFlygtPYCkZtKSNxl/3iQ0pWsz1dM2vwZA85SvdSs2aaomuXQzKZXbUH8rEydO4vLLL+Pcc891vd29p5WWlvLyyy8zf96r1NU3MCY7yDnDm/ni0FYyk6P73vndqiwAfj29PqrPC9DgE5aVJvPu/nR21XrIzsrka5f8O1//+tcZOnRo1M8XDb+64Vcs/3g5gakBdLzG/RpgnndDXwyD58R57VZB9gre9V7ysvN4+qmnw3pfi8hqVZ0ezikssbgsEAjw8ccf88ILL7By5UrwePAPGItvyFSCWXkRbRzWrcSiQbzVe0gu3Yy3Zh9er5fzzjuPyy67jClT+viumIRWQF68eDEvvfgi23fsINkrnJLXzL8Nb2XKAD+eKHyrjnZiCSoUHEzivf0prCxPwxdQxo3N5z8uu5wLLrggtNNhHCsvL+eOO+9gzeo1SLoQmBhARynEadhxn1h8ICWCd6sXrVZGjxnNTTfeFPb7u88lFhGZAdxD6DvNo6r6h6MdH0+Jpb3du3fz8ssv8/qCBTQ3NaFZebQOnoJ/0Piwmsm6lFgCrSSVbSG1bAs01zJg4CD+4+uXcskll8RV00i8UFUKCwtZsGABixctpL6hkdx0OHNoE2cPa2VoRtc/VKKVWA40evigJIUPD6RT0QRZmRl8+YIL+epXv8rEiRORBGvLW7t2LY888shn8y8GQnBYMLRLZH/ipqksLhNLQyiZyH7BU+5Bg8rwEcO5+vtXc9555+H1hl8N7FOJRUS8QCFwAbAXWAlcoaqbj/SYeE0sbRobG1m4cCEvvPgie3bvhrRsWoYch3/wRPAcuVssosTiaya5dBOpZZtRXwvHHXc8l19+GWeffXZcdN4mgpaWFpYuXcobb7zBqpUrCaoyMSfA2cOaOWNIK2kR/hm7k1ia/bCiNIX3StIorPbiEWH69OnMuPhizj777IRvwlRVduzYwUcffcQHSz9gS8EWACRTCAwKQC5orkI/XEs0ricWBRpBKgQqwFvpRWtCn+8jRo7g7LPO5swzz2TatGkRJZQ2fS2xnAHcqqoXObdvBFDV3x/pMfGeWNqoKsuWLWPOnDls3rwZScmgZchUfEOndphgUoqXARx9AUpfEyn715FSvhUN+Dj77LO58sorOfbYY2P1a/QJFRUVLFq0iDcWvE7x7j2kJQlnDGnmvBEtjO0X3vIsc7eG2ngimXW/s9bLO/tSWVaaRpNfGT1qJBd/5atceOGF5OXldel3SQSVlZUsW7aMFStWsG79OqoPhnrOJUUIDgyiuYoO0tDs/B5aUKHHE0sAqAGpDCWSpMokgk2hc6dnpHPctOM4+eSTOfPMMxk9uvs73Pa1xHI5MENV/49z+zvAaar6w8OOuwa4BmD06NEnFxcX93isXaWqrFu3jrlz54b6YdL60Tz6NAIDxkTwJEGSyraQtm81EvBxwQUX8O1vf5v8/PyYxd0XqSobN25k/vz5vPP2ElpafYzrH+Qroxo5ZbAPbxQG/gUVVpYls2B3BttrPKQkJ3Pe+edzySWXMG3atIRr6uouVaWkpIQNGzawfv161q1fx+7iz/ZHkv5CYEAABoIOdJrPYjAAU9aG/u56Ygw+UxWoB6kSqAJPlSc0Cs3JYbl5uZx4wokcd9xxHH/88eTn53epVnI0fS2xfAO46LDEcqqq/uhIj0mUGktHVq9ezV13382e3bsJ5IyiJf8sNPXoM8c8DRWk7VyKNFTwhS98gZ/+9KeWUHpAXV0dixcv5sV//Ys9+/aRlwEXj2rknOEtpHThPe8Lwrv7UnljTwZljTBi+DAu/8Z/cuGFF/ba4d9dVVtby5YtW9i8eTObCzazadMm6mpDE5PFK+gAJTgoGKrVDALS3I33c3xAVag2IhWC56AHbQ19VqempTJ50mSmTJnCsccey5QpUz7dyTaW+lpi6bVNYUfi9/t54YUXeOyx2bTipXHCBQQzO56o6K3cSfqO98jJ6c+Pr/sR5557bp/7Ruu2QCDAhx9+yDNPP83mggIGZ8C3jqnn5DxfWIP+VGFNeTLPbMuitBGOPXYy3/rWtznrrLOi/q20t2qr1RQUFFBQUMDGjRspLCzE7/cD4Mny4B/oh0GggxWy6dm+mkaQcqdvpCo0agtC+z2NyR/DcdOO+zSJjBkzxpXXva8lliRCnffnA/sIdd5/S1U3HekxiZ5Y2uzYsYPrf/FLKquqaBp/7ueaxpL3rydlz8dMmTKF3//+9wmxN0pvpqqsWrWKv997D7uKdzNtoJ9rptQzMO3I78GDLcJDm7PYWJlE/pjR/PBH13HKKafYl4MoaG1tpbCwkI0bN7Jp0ybWb1jPwarQjoueDA/+wX4Y4iSaaNdo/EAZSKngLf+skz09I51pU6cxbVrocuyxx5KVlRXlk3dNn0osACLyFeBvhIYbz1bVO452fG9JLBDqxPzVr26gaPt2GqbORNNDycNbtYu0orc455xzuPnmmxN+VFBv4vf7efXVV3nowQdJpYUfTatj8gD/544rrPZy78Z+NGsq1/zgB1x66aU2Yi+GVJUDBw6wevVqVq5cycpVK6mvC43QkwFCYFQgtP1xV+fR+ED2CZ7dHqRc0KCSnJLMiSecyKmnnsr06dMZO3Zs3K651+cSS6R6U2KB0IikWd/9LnWBJBqm/DviayFz00uMzx/Ngw88QEpK31ytN97t2rWLm268gZL9+7n+xDqOG/RZctlYlcSf12YzdOgw7rjz94wbN87FSPumQCBAUVERq1at4v0P3g8NcRbQIYrmKzpCwxsEUA6yU/Du86J+ZdjwYZzzpXM49dRTmTZtWsJ86bPE0onellgAli5dyk033UTLuH8LddZXFjLnyScZNWqU26GZo6irq+OH1/4/Svft5tbp1QzPDFLS4OHWVTnkDR/Fffc/YB3zcWL37t0sXLiQN958g4ryCiRH8B8fai7rUC1413nhQKiJ68vnf5kZM2Yk7Mg9Syyd6I2JRVW58jvfYXdlA57Wes7/0tn85je/cTssE4aSkhL+z9XfZ0xKDTecVMcfP8lmV0t/Hnn0sbhYvt4cKhgM8v7773Pf/fdReqAUHakETw1+tpaZgmwWPAUe0tPTufr7VzNz5syEqZkcSSSJJT4b80zERISLZ8xAGipRXwszZsxwOyQTpmHDhvGdq2axsSqJBcWpbKhM4srvXGVJJU55PB7OOeccnpr7FFdffTWefR68H3qhjFCz11rBs9nDhRdcyHPPPsd//ud/JnxSiZQlll6k/WJyNpM+scycOZOU5GSeKcogOcnLzJkz3Q7JdCI1NZVZs2Zxww03IGWC9z0v3ne9eLZ5uOyyy7j55mxXl1wAAAeySURBVJvJyclxO0xX2BCTXmTSpEn069+f4cOGW7t8gklPT2f6Kafw0UcfcfL06WRkdG3rBNPzLr74YqZOnUpFRQUAGRkZTJo0KSH7UaLF+lh6mWAwiIj06X/qRFVbW0tRURHHHHMM/fv3dzscYw4RSR+L1Vh6mXgdA286169fP04++WS3wzCm2+xTyBhjTFRZYjHGGBNVlliMMcZElSUWY4wxUWWJxRhjTFRZYjHGGBNVlliMMcZEVZ+cICki5UDibHofmVygwu0gTJfZ65fYevPrN0ZV88I5sE8mlt5MRFaFOzvWxB97/RKbvX4h1hRmjDEmqiyxGGOMiSpLLL3Pw24HYLrFXr/EZq8f1sdijDEmyqzGYowxJqosscQBEVERmdvudpKIlIvIaxE+z7siMt25vkBE+ub2dXFGROoPu/1dEfmHc/2/ReSqTh7/6fGm54jIzSKySUTWi8haETktCs9Z7/zMF5GN3Y8yPtl+LPGhAZgmIumq2gRcAOzrzhOq6leiEpmJKVV90O0YzOeJyBnA14CTVLVFRHKBFJfDShhWY4kfbwBfda5fAfyz7Q4RyRSR2SKyUkQ+EZGZTnm6iDzrfKN6Dkhv95hdIpJ7+DcjEbleRG51rr8rIneLyPsiUiAip4jISyJSJCK/64Hfuc8TkVtF5Hrn+inOa7lMRP582Dfa4SLypvPa/MmlcPuSYUCFqrYAqGqFqu533ld3Oq/RKhE5SUQWish2EflvABHJEpElIrJGRDa0vV/7Ekss8eP/t3evIVZVYRjH/49hmU0lSYbZdKHUKJWpyTIiywi/dCGxyChK6ouVEUXFZH2oxBLUwIiyiCzC7kSlJBVTUZR2Mxk1yrCbA2YRaXbRTN8+rDW55zRzzsx0pjk0zw+GmbPW3vusc9m8e689+32fBqZJGgSMA94v9N0GvBER44FJwDxJ+wFXA79FxDhgDtCT8oN/RMREYBHwEnAtMAaYLmloj1+NFe2bp1JWS1oN3NXJcouBGRFxKrCrpK8BuBgYC1wsqb73hmvAa0C9pPWSHpB0RqFvY/6M3gEeAy4EJrDnc90OTImIE0n76wL1s1rhngqrERHRIulI0tnKKyXdk4Hz245sgUHA4cBE4L7C+i09eOqX8+81wLqI2AQg6UugHvixB9u09n6PiIa2B5KmA+3uzs7Xw/aPiPdy05OkqZg2zRGxNS/7KXAEsLE3B92fRcQvkhqB00nB4RlJTbm7uM/URcQ2YJuk7flz/BW4W9JEYDcwAjgE+O4/fRF9yIGltrwMzAfOBIpnCwKmRsTnxYXzQVCl/xf/k/ZnpoNK+nfk37sLf7c99vfjv1PpiLb42ezCn02vi4hdwFvAW5LWAFfkrkr7zKXAwUBjROyU9DX/3O/+1zwVVlseBe6KiDUl7a8C17WdTks6Ibe/TfoSI2kMaQqt1GZgmKShkvah/VGw1YiI+Il01DshN03ry/H0d5JGSxpZaGqg64lrDwS+z0FlEunssl9xYKkhEdEaEQs76JoNDARa8gXd2bn9QaAuT4HdAnzQwTZ3kuZ+3weWAZ/1xtitKq4CHpa0gnQGs7WPx9Of1QGPS/o071/HAXd0cd0lwEmSPiId+PW7fc533pvVCEl1EdF2n0MTMDwiru/jYZl1m+dpzWrHOZJuJe2X3wDT+3Y4Zj3jMxYzM6sqX2MxM7OqcmAxM7OqcmAxM7OqcmAxM7OqcmAxq0DSYZJeygkgN0haKKlspltJs0oe/9LZsrl/iKRrKiwzQNJ9ktbm5IYfSjoq97lMgtUMBxazMnK2gxeAFyNiJDCKdPPcnAqrzqrQX2oIUDawkJJQHgqMi4ixwBRgC6QyCRGxpZvPadYrHFjMyjsL2B4Ri+Hv/FE3AFdKuqZYgEvSMklnSprLnozGS0o3KOnmfLbRIunO3DwXODqvM6+TsQwHNkXE7jyW1pwKplgmYUYhk/JXkt7M/ZNzqvdVkp6TVFedt8fsnxxYzMo7Hvi42BARPwPf0skNxhHRRM5oHBGXFvskTQZGAieT8k815iy4TcCGvM7NnYzlWeC8HDQWFHLGFZ97Uc6kPB5oBe5VKlJ1O3B2TuX+EXBjF1+/Wbf5znuz8kTHGaQ7a69kcv75JD+uIwWabyutGBGtkkaTzqLOApolXRQRzR0svpBUw2eppHNJua7ezXlM9wZW9GDsZl3iwGJW3jpgarFB0gGkWjVbKV+SoCMC7omIh0q2eWRXBpMrGi4HlkvaDFwAtAssud7LEcDMwnO+HhGXdOU5zP4tT4WZldcMDJZ0OYCkvYAFpMqBXwIN+b+16knTW212ShrYwfZeJV2fqcvbGyFpGLAN2L/cQHIZ3EPz3wNIZRK+KVmmEbgJuKztWgywEjhN0jF5mcGSRnX1DTDrLgcWszIiJdObAlwk6QtgPan07CzgXeArUiXB+cCqwqoPk8ocLCnZ3muk6pArcvGo50mVI38kTVWtLXPxfhiwNJdOaCEVcbu/ZJmZwEHAm/lazCMR8QMpoeVTOQX8SuDY7r8bZl3jJJRmZlZVPmMxM7Oq8sV7sxojaSzwREnzjog4pS/GY9ZdngozM7Oq8lSYmZlVlQOLmZlVlQOLmZlVlQOLmZlVlQOLmZlV1V+mJHj3SOliqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = \"Outlet_Size\",y = \"Item_Outlet_Sales\",data = train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate Analysis - Item Visibility and Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "plt.scatter(train.Item_Visibility, train.Item_Outlet_Sales, \n",
    "            color=\"red\")\n",
    "plt.xlabel(\"Item Visibility\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.title(\"Scatterplot - Visibility Vs Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Item Visibility Vs Sales\n",
    "\n",
    "Highly Visible Items have lesser sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x124f45dd8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Weight Vs Sales\n",
    "plt.scatter(train.Item_Weight, train.Item_Outlet_Sales, \n",
    "            color=\"blue\", alpha = 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Item Weight Vs Sales\n",
    "No pattern seen..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124f62cf8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Fat Content vs Sales\n",
    "sns.boxplot(x = \"Item_Fat_Content\", y = \"Item_Outlet_Sales\",\n",
    "           data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1251ad710>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train[train.Item_Fat_Content==\"Low Fat\"][\"Item_Outlet_Sales\"])\n",
    "sns.distplot(train[train.Item_Fat_Content==\"Regular\"][\"Item_Outlet_Sales\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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901jDeDU2NpbNTlbCEB/NIBcZnLK01P0C2GZmU4DrgUOA5qpEFbHu62qFXGdLV9O901jDeDU1NZW1MCtheE0sFyOaQd67WJ4jkUqyJHWd7t4BnAp8090/A4yvTljxGj58eJ/lPOlqujKNNYzbSy+9VFZeu3ZtoEjiE9PFiGYFVxbTcyTSXZakbquZnQGcCdyWHhux60OKW/eZekcddVSgSHQ13RuNNYzb1772tbLyVVddFSiSuMR2MaJZwT3F9hzFQq2X8ciS1J0FvAu43N2fMbNDgP+qTljx2m233crKI0eODBRJQlfTPcU21lBveOWWLVvWZzlvsTw/uhiJn56jytR6GY9+J3Xu/gTwL8CjafkZdx9yl9jdZ+qFnrmnq+meYmpNBb3hdTdp0qQ+y3mL5fmJ7WJEetJz1JNaL+OSZfbrB4A/Anem5b8zs1sHWrGZfcbM/mxmi83sp2a2u5kdYmYPmtkSM/tvMxuZ3ne3tLw0vf3gkt9zSXr8L2Z2wkDj6a9x48b1WZb4hJzMoje8nj73uc+VlS+++OJAkcT1/GjiU/z0HPWk1su4ZOl+/RLwDmAtgLv/kWQGbGZmNgH4JDDN3Q8DhgOnA18FvuHuU4GXgHPSU84BXnL3KcA30vthZm9Iz3sjcCLwXTOr6syFVatW9VmW8Lqvg9a9nKeY9jmNpZtxv/32Kyvvu2+4lZFi+kBqamravhCzmWlIRYQ0c7sntV7GJUtS1+Hu67od25kmkDpgDzOrA/YEVgKNwM/T228ATkl/Pjktk94+3ZJ3v5OBG919s7s/AywlSTyrZvr06WVvvNOnT69mdTIAjY2N22clDx8+POjVdEz7nMbSzdjc3Fz2/ISMJ6YPpEKhQENDAwANDQ0aUiGDglov45IlqVtsZk3AcDObambXAgNqAnH354BrgOUkydw64BFgbbpsCsAKYEL68wTg2fTcjvT+hdLjFc6piqamprJ/YF2pxaepqaksaQj5HMWyz2lM3YwxJbqNjY1lF2khP5CKxSLt7e0ArFy5MniLakxiaWVubm4u+38JfYEUAy2tFZcsSd0FJN2cm4GfAuuBTw+kUjPbj6SV7RCgARgFzKxw166WwEqbQ3ofxyvVea6ZPWxmD69evTp70KlCocCxxx4LwHHHHaer6QjFvNRLqH1OY+pmjCmRmjlz5vYxl+7O+9///mCxNDc3l+0rrYThNbG0Msd0QRKLmN9vh6Iss183uPsX3P3t7j4t/XnTAOt9D/CMu692963AL4EjgX3T7liAA4H29OcVwEEA6e2jgTWlxyuc0z3+69K4p9XX1w8w7B6/c5f8Htn1YlnqJZZ9TmPqZowpkZo3b15Zgnn77bcHiyWm5ygmMbUyq6uxsljeb6UfSZ2Z/drMbu3ta4D1LgfeaWZ7pmPjpgNPAAuBD6f3ORO4Jf351rRMenurJ58KtwKnp7NjDwGmAr8fYEz9UiwWWbRoEQCLFi0K3h0QS7dEbLHEstRLLB8CMbWOzZs3r6wcOpEqTTBDdwXH8L8Sm5hamdXVWFks77fSv5a6a4Cv9/GVmbs/SDLh4VHg8TSO60jWwfusmS0lGTN3fXrK9UAhPf5Z4OL09/wZ+BlJQngncL67bxtITP0V0xsMwOzZs3n88ceZPXt20Dggni6SmMTyIRBT69iCBQv6LOcppkQqlv+V2MTUgqmuRondDpM6d7+nr6+BVuzul7r76939MHf/aDqDtc3d3+HuU9z9I+6+Ob3vprQ8Jb29reT3XO7uh7r769x9Xu817hoxvcEUi0XuvfdeIHyrYUxdJDGJ5UMgptax7o9ByA/GmBKpWP5XYhNT4g3qapS4ZVl8eKqZ/dzMnjCztq6vagYXo+5vKCHfYLq3zoVsrYutBTMmM2fOZI899gjaOtb94iPkxcjKlSv7LOcptkRKCUNPMSXeIrHLMvv1h8BsoAM4Hvgx8JNqBBWzmTPLJ+mG/KC+7777yspdrXYhxNSCCXGN75s3bx4bN24M2joWy9IqwPYP6N7KeYspkdLYpJ5iS7w1zERiluXddA93XwCYuy9z9y+RLBY8pNx4441l5ebm5kCR9Jx9G3I2bmxdJLGMNSwWi8yfPz+6bulQS6tAshRQqeOPPz5MICklUvGLJfHWMBOJXZakbpOZDQOWmNknzOxUYMhtfNq9NSxk69iECRP6LOcppi6SmMYaNjc3b2/B3Lp1a7Cr+1iWVgE4++yz+yyLdBdL4q1hJhK7LEndp0m28/ok8Dbgo7y2zIgE8PnPf76s/MUvfjFQJHF1kcQ01nDBggVls05DzfSMrSVVZDCKbZiJSHdZFh9+yN1fcfcVJMuKnObuv6teaLIjU6ZM2d46N2HCBCZPnhw0nli6SGIaazhu3Lg+y3mJqSV1zpw5fZbztnTpUk499VTa2sLP+4ppLKj0pIsjiV3dju5gZv8K/MzdnzKz3UjWg3sL0GFmTe7+m2oHGZMDDjiA559/fnt5/PjxAaNJWusuuuiioK10Xbq6SEKLaazhqlWr+iznpasl9fbbbw/eknr33XeXlRcuXMiFF14YJhjga1/7Ghs2bOCqq67iuuuuCxYHlA/Cv+CCC4LGIj01NTXR0tIChL84ytvs2bN7vfB57rnngN6HAE2ePJnzzjuvarHJa/rTUve/gb+kP3d1t9YDxwJXVCOomK1bt66svHbt2kCRJKZMmcLNN98cvJUO1MpQyfTp0/ss5ymWltSYku6lS5eybNkyAJYtWxa0tU6D8OMX0zCTmGzatIlNmwa6a6jsSjtsqQO2+GvvuicAN6a7NjxZsk/rkDF9+nRuu+22srIk1MrQ08yZM8v+X0IugRNLS2p9fX1Za3eoLmlIWulKhWytqzQIX6+j+MycOZPW1tagr+UQ+mppu+iiiwC4+uqr8wpHetGflrrNZnaYmdWTrE/XUnLbntUJK15NTU2MGDECgBEjRgRv9YhF6dId8+fPD9rKMGrUqD7Lebr55pvLyr/85S8DRRKP1atXl5VDdUkD21vpeivnKbZB+Gp5ryyGdSdFetOfpO5TJPu0PgV8w92fATCz9wF/qGJsUSoUChx77LEAHHvssWp+T5Uu3dHR0RF0qv/WrVv7LOdp4cKFZeXQH9Qx6L5GXsg18/baa68+y3mKbRD+nDlzePzxx4NPZIF4EsyYLl674onhcZF49Gfv1wfTPVoL7v6VkuN3uPsZXWUzG3LLm4T8MOoSy4s6lqU7AEaOHNlnOU/btm3rszwUve1tbysrT5s2LVAkbL8Q6a2cp5hmKBeLxe0XIAsWLAj+/hLLLg4xXbx2xRPD4yLx2JX783xqF/6uaBWLRRYtWgTAPffcoze7VCxLdwC88sorfZbzFNuWWDFYvnx5WTlkl+d73vOePst5imkQ/pw5c8rG94VsrYupdSymi9dYd6uRsHblJ0z4ZqscxLSieLFY5M4774zizS6WpTsgrgSz63+lt/JQtHLlyj7LeYqpdawrnhhmKFdadiaUmFrHYnpviWW3GonLrkzqwq1LkKOYBjPH9KKePn369u5oMws6K/jll18uK4dsqZOeYhpTF5tYtsOKadmZmFrHYrp4jelxkXiopS6jmAYz/+Y3v+mznKempqbtj0voWcEbN24sK2/YsCFQJFLJ0UcfXVZ+97vfHSiS5MKo9GIkdGtHLGNkjz/++D7LeYqpdeyoo47qs5ynmB4XiUe/kzozO2QHx8LtEJ6jmLprupKo3sp5KhQKnHDCCVGMB4rJW9/61rLy4YcfHiiSeHRf7yrkSvOtra3bJ69s27Yt+Ozkrhmn119/fdA4Tj311LLyaaedFiiSuFrHugvZyvzCCy/0WZahKUtL3S8qHPt51w/u/omdDyd+MQ1mjmlCAMQzHigmXYty9lYeigqFwvZk9/DDDw/6GmpsbCxrqQvZ8l4647S1tTVoa928efPKyiHXZItpaMcDDzxQVr7//nBtGYVCoaw8duzYQJFITHaY1JnZ683sNGC0mX2o5OvjwO5VjzBCsSQv3fedDb0PrfRUKBS2v/kWCoWgCUxMG9d3tbaEbnWZOXNm2bikkLsEdJ9xGrK1rvv4rJDjtWIa2hHTRUDpriwQdsKRxKM/LXWvA04C9gU+UPJ1OPCP1QstXrEOZg4tpsVKY1EsFlm/fj0A69evD9r6UrpxfUhLly7dvgH4ihUrgiaZ8+bNK/uQDtki1X2GacgZpzGN14ppwfeYLgJEKunP4sO3uPtZwEnuflbJ1yfd/YEdnS/VE9OVWmyLlcaiubm57EMg1ED8mDaur7Tfaiitra1lz0/IMXUxzQqOdRxb6AvZmLqlY5rMIvHIMrK+aGYLgP3d/TAzezPwQXe/rEqxyQ5MmjSpbOHWSZMmBYul0mKlF154YZBYhg8fXrZzw/Dhw6te5+zZsysmSosXL97+uHR0dHDHHXf0WHx38uTJVZ8sENPG9THtt3rkkUeWzRoPOZvxuOOOK4sl5If09OnTue2228rKoZQu+L5o0SLOOeecYK11lVYcuOCCC4LEcvbZZ5fFc8455wSJQ+KSJan7T+Ai4PsA7v6YmTUDNZnU9fYhDWzvOpowYULF2/P4kAb43Oc+x/nnn7+9fPHFF1e9zt5U2uM0VFJ3wAEHbH+Ousqh7LvvvmWtlvvtt1+QOGJKpCZMmFD2/Bx44IHBYtm8eXNZedOmTYEiST6kW1tb6ezsZNiwYZx99tnBYpk5c2ZZUheym7HSgu+hEqnYdogZNmzY9v8XEciW1O3p7r/v1iUQbqPEgEK+8ZeaMmXK9ta6SZMmMXny5GCxhNg5obfEu729vUe5+6zTXZ149/a7isUis2bNwt0ZOXIk3/72t4O0MowaNYpXX321rBzK5MmTy5K6Qw7psVpSbrrPZuxezlOhUKCxsZHf/OY3TJ8+PejYsZtvvrms/Mtf/jLYRVqlBd9DJXXd17wMuQZmc3NzWVIXMtmVeGRJ6l40s0NJd44wsw8DNTvdpq8P/K4E4eqrr84rnF597nOf46KLLgraSgfJ+J/S8S4hxwPtt99+Za1jIT8cC4XC9nhCLoHTfUHm7uU8PfTQQ32W8xTbNm5nn302L7zwQtBWOoir5b2xsZE777yTjo6O4Au+x/Q+F1OyK/HIktSdD1wHvN7MngOeAf6+KlFJv02ZMqXHVXUI48ePL2t9aWhoqHqdfbWONTU1AckSCKFax7rsv//+bN68OehSDF1X9KVlietDGl6bWR9a6ZjUSuU8NTU10dLSAoRf8P3oo4/m3nvv3V4OuRtKTMmuxKPf7+zu3ubu7wHqgde7+9Hu/teqRSaDSvfZrsViMVAk5evBnXDCCcGXnhkxYgSHHnpo0Dje9a53lZWPPPLIQJH0HL4QcjiD1nqsrPvkojwmG/UmpgXfTz/99LLyGWecESiSuHY3knj0Z/Hhz5Z+Af8E/GNJWaTH7LiQs+UgaR0bNWqU3uikT90vRrQUT6L7zNvQrUCxLPjevVfkF7+otNFSPmJKdiUe/el+3buP2+Ja/VaCaWpqYv78+WzdujX4qu8QR+tYTEJsb9TbRJZKXZ7VnsjSm+nTp3P77bfj7sG3oIrJqaeeWrZcxoc+9KGA0cTTLV1pgeiQW/81NTWxbNmy4O+3Q1lsK2XsMKlz938DMLOj3L3sk8DMwi3qJECyqOxFF13E17/+9aCzXwuFAieccAK33357FF2eUi6mCQGTJk3ir3/96/bywQcfXPU6e3vj3bp1a1mC+fTTTwdLMGMS0+zXmMS0QDTEk+xKZSGGlmSZKHEtydZgOzomObriiivYsGEDl19+edC9IkFXjTELMSGgr0ToxBNPxN3Zc889+d73vlf1WHozYsQI6urq6OjoYMyYMYwYMaLqdcZ2ZV/J3XffXVZeuHChkjqSsamlEyVCjk2VOMS2UsYOkzozexdwJFDfbQzdPkC40bNScQ/N0K11umqMU4jZyX3paq3713/911zq6+uN99Of/jTLly8PPksa4lkDs/t2XKG354rFbrvtVlYeOXJk1escDBcBEo/+tNSNBPZK71s6vm498OFqBCX9c8UVV5SV82it0xvM4BTT7GSAffbZhze/+c289a1vDRoH5D/+MrYr+0qOP/743LcsGwzvLTHU5GkvAAAgAElEQVQtVg3xXARIPPozpu4e4B4z+5G7h9tbSHoobXmBpLUuJL3BxCumvTwlft0nSpx22mkBo8n/vaW3BHP33Xcv20Vijz32CLZbDcRzESDxyDKm7kdm1qMN3t214mEgsY2T0htMvGKbnSxxmzdvXln59ttvr/puBYPhvWX//fff3uptZowbNy5oPCLdZUnqSkfJ7g6cxhDd+zUWMa1uLnErFAqMHTuWlStXMnbs2OBjxyRuCxYs6FEeSltQ9ZVgnnHGGaxZs4b3v//9Q+oxkcEhy44Sj5R83e/unwWOqGJssgPnnXfe9tY5M9N4NelVsVhk5cpkq+aVK1dqkV3pU/cWKLVIvUYLm8etWCxy4YUXDtn3uH4ndWY2puRrrJmdABww0IrNbF8z+7mZPWVmT5rZu9LffZeZLUm/75fe18zsW2a21MweM7PDS37Pmen9l5jZmQONZzAqFAocffTRQNJKp9YX6c3s2bP7LIuUWrVqVZ/loUwLm8etubmZxYsXM3fu3NChBJGl+/WRkp87gGeAc3ai7v8A7nT3D5vZSGBP4PPAAne/yswuBi4G/gWYCUxNv44AZgNHmNkY4FJgGsnuFo+Y2a3u/tJOxDWonH766TzyyCNB9yCU+N13331l5dJuexm6epsQsMcee7Bx48bt5T333FOLMkv0isUiLS0tuDstLS3MmjVryCXfWbpfDyn5muruM9z9vh2f2ZOZ7QMcA1yf/u4t7r4WOBm4Ib3bDcAp6c8nAz/2xO+Afc1sPHACcJe7r0kTubuAEwcS02A1b948Nm7cyO233x46FImY1h2TLPbff//tP2tCgAwWzc3N23fL6ezsHJKtdf1qqTOzccD5wBtJWsSeAL7j7gNtk58MrAZ+aGZvIWkF/BSwv7uvBHD3lWm9ABOAZ0vOX5Ee6+14pb/hXOBcgIkTJw4w7LjoqkS6663lZeTIkWzZsqWsrJYX0YQAqSWtra10dCTzNzs6OmhtbR1y/7s7bKlL93d9KC3+GPiv9Off78Ter3Uk24vNdve3Aq+SdLX2GkaFY97H8Z4H3a9z92nuPq2+vj5rvFHSVYn0V/cLmUmTJgWKRAYLTQiQwaaxsZG6uqStqq6ujsbGobfiWn9a6r4OnOLufyg5douZ3Qx8n4HNgF0BrHD3B9Pyz0mSuhfMbHzaSjceWFVy/4NKzj8QaE+PH9ft+N0DiGdQ0lWJdNdXy8sHPvABtmzZwoEHHsi3v/3tHKOSwUgTAmSwaWpqoqWlBYBhw4YNyQuS/oyp26dbQgeAu/+R8m3D+s3dnweeNbPXpYemk3Tp3gp0zWA9E7gl/flW4GPpLNh3AuvSbtr5wAwz2y+dKTsjPTYkdN9M+qijBtpwKkPBxIkTGTZsGF/4whdChyIisssVCgVmzJiBmTFjxowheUHSn5Y6M7P9us8oTWee9nuiRQUXAHPTma9twFnp7/uZmZ0DLAc+kt73DuB9wFJgQ3pf3H2NmX2F17qHv+zuQ3NxGjT4Xfq25557cthhhzF58uTQoYiIVEVTUxPLli0bkq100L+k7htAi5ldCDyaHnsb8NX0tgFJW/qmVbipx6aUnmQr5/fye+YAcwYax2AW2+bSIiIiIRUKBa655prQYQSzw6TO3a8zs3bgK5TPfr3M3X9d5fiEuDaXFhERkTj1a0kTd78NuK2v+5jZJe5+5S6JSvpFm0uLiIhIlyw7SuzIRwAldVWgtaRERERkR3ZlUldpzTipsv3335/NmzcP2UGhIiIytPQ2JAngueeeA2DChIr7ENT8kKRdmdRp6mUAWktKREQksWnTptAhBKWWOhERERk0+mpp65osePXVV+cVTlR2Zp257m7ahb9LRERERDLod0udmR1CsmDwwaXnufsH0+9X7OrgRERERKR/snS//gq4Hvg10FmdcERERERkILIkdZvc/VtVi0REREREBixLUvcfZnYp0AJs7jro7o/2foqIiIiI5CFLUvcm4KNAI691v3paFhEREZGAsiR1pwKT3X1LtYIRERERkYHJsqTJn4B9qxWIiIiIiAxclpa6/YGnzOwhysfUfXCXRyUiIiIimWRJ6i6tWhQiIiIislP6ndS5+z1mNgmY6u6/MbM9geHVC01ERERE+qvfY+rM7B+BnwPfTw9NIFmQWEREREQCyzJR4nzgKGA9gLsvAcZVIygRERERySZLUre5dDkTM6sjWadORERERALLktTdY2afB/Yws/cCN5HsAysiIiIigWVJ6i4GVgOPA/8E3OHuX6hKVCIiIiKSSZYlTS5w9/8A/rPrgJl9Kj0mIiIiIgFlaak7s8Kxj++iOERERERkJ+ywpc7MzgCagEPM7NaSm/YGitUKTERERET6rz/drw8AK4GxwNdLjr8MPFaNoEREREQkmx0mde6+DFgGvKv64YiIiIjIQPSn+/VlKq9HZ4C7+z67PCoRERERyaQ/LXV75xGIiIiIiAxcltmvIiIiIhIpJXUiIiIiNUBJnYiIiEgNUFInIiIiUgOU1ImIiIjUACV1IiIiIjVASZ2IiIhIDQia1JnZcDP7g5ndlpYPMbMHzWyJmf23mY1Mj++Wlpemtx9c8jsuSY//xcxOCPOXiIiIiIQVuqXuU8CTJeWvAt9w96nAS8A56fFzgJfcfQrwjfR+mNkbgNOBNwInAt81s+E5xS4iIiISjWBJnZkdCLwf+EFaNqAR+Hl6lxuAU9KfT07LpLdPT+9/MnCju29292eApcA78vkLREREROIRsqXum8DngM60XADWuntHWl4BTEh/ngA8C5Devi69//bjFc4RERERGTKCJHVmdhKwyt0fKT1c4a6+g9v6Oqd7neea2cNm9vDq1aszxSsiIiISu1AtdUcBHzSzvwI3knS7fhPY18zq0vscCLSnP68ADgJIbx8NrCk9XuGcMu5+nbtPc/dp9fX1u/avEREREQksSFLn7pe4+4HufjDJRIdWd58FLAQ+nN7tTOCW9Odb0zLp7a3u7unx09PZsYcAU4Hf5/RniIiIiESjbsd3ydW/ADea2WXAH4Dr0+PXAz8xs6UkLXSnA7j7n83sZ8ATQAdwvrtvyz9sERERkbCCJ3Xufjdwd/pzGxVmr7r7JuAjvZx/OXB59SIUERERiV/odepEREREZBdQUiciIiJSA5TUiYiIiNSA4GPqREREJLzZs2fT1taW+bynn34agIsuuijzuZMnT+a8887LfF6eBtPjoqROREREaGtr43+eXMqE0RMznVe3bSQAr7ZvyXTec+uWZ7p/KG1tbSx94i9M3GdcpvNGdiT7I2xZ8VKm85avX5Xp/qWU1ImIiAgAE0ZP5Px3fz6Xur5z7xW51LMrTNxnHF84clYudV3+wNwBn6sxdSIiIiI1QEmdiIiISA1QUiciIiJSA5TUiYiIiNSAIT1RYjBNUxYRERHpy5BO6pJpyk8ycfSYTOeN3OYAbHnuhUznLV+3JtP9Ja7EO+9YdAEgIiJZDOmkDmDi6DF88d0zcqnrsntbcqmnlrS1tfHUk48xdt+MJ3Ym315c+Vim015c23csTz75GKP3yxbKtjSW9uf7H8u6bMsaiYiIKKmT+I3dF05pzGf4569aO/u8ffR+cEwO1wCLBkn+H1NLqshgpV4A2VWU1InIgLW1tfHYU4th7O4Zz0xWnn/sxaXZTntxU8Z6RCqL6YKkra2Np55aSv2YSdl+oSc7ORRXbe33KavXLMtWhwwqSuqkh5je7GQQGLs7w0+enEtV227p/f9S/7fxi+k5amtr44mnlrBXIduWWFvTRGr56s2Zznul2PeWWPVjJvGR930x0+8ciJvuuKzqdUg4Suqkh2T/v8c4YLRlOm9YOoFkffvjmc57fp1nur9IJUmr4RNQ2Cfbid4BwGOrV2Q7r7g+2/0lfY7+h+GF8ZnO6/ThAPx59cuZzttWXNnn7XsVJnL4yZdk+p0D9egtV+ZSjwxtSuqkogNGG/9wzIhc6vrBov53HYj0qbAPdR88MpeqOm59oNfbYmqRis3wwnj2/MC5udS14dfX5VKPSCyU1ImI7GJJi9STWCHbcknuSav146uzLZfkRS2XJCJK6kREqsIKY6g76YRc6uq4bX6vt6nVUGToUFInMsjoQ1qySFoNn8IK9ZnOSxsNeXx1Mdt5xdWZ7i8iu46SOpFBpq2tjcVPPcYehWznbU4/pJ9enW1B5o3ZPtMlQlaoZ7eTPpJLXZtvuymXekSkJyV1kVDri2SxRwGmnpRtdvJALblNs5NFRAYDJXWRaGtrY8kTi5k4eo9M543clqyVtPm5pzOdt3zdxkz3FxERyYsaOgZGSV1EJo7eg0uOnppLXVfetySXekRERLJqa2tj6RNLmLj3hEznjexI0potz27IdN7yl5/LdP9YKakTERGR6EzcewKXvP2CXOq68qFrc6mn2vLZJV1EREREqkpJnYiIiEgNUFInIiIiUgOU1ImIiIjUACV1IiIiIjVASZ2IiIhIDVBSJyIiIlIDlNSJiIiI1AAldSIiIiI1QEmdiIiISA0IktSZ2UFmttDMnjSzP5vZp9LjY8zsLjNbkn7fLz1uZvYtM1tqZo+Z2eElv+vM9P5LzOzMEH+PiIiISGihWuo6gP/r7n8LvBM438zeAFwMLHD3qcCCtAwwE5iafp0LzIYkCQQuBY4A3gFc2pUIioiIiAwldSEqdfeVwMr055fN7ElgAnAycFx6txuAu4F/SY//2N0d+J2Z7Wtm49P73uXuawDM7C7gROCnuf0xIiIiUrPa29t5df3LXP7A3FzqW7Z+FaPaNw7o3CBJXSkzOxh4K/AgsH+a8OHuK81sXHq3CcCzJaetSI/1dlxkl2tvb2ftOljUUv261r4EdLZXvyIREakZQZM6M9sL+AXwaXdfb2a93rXCMe/jeKW6ziXpumXixInZgxWRHtrb22H9Jrbd0pZPhS9uon2Lkl2Ramhvb+eVta/ynXuvyKW+59YuYy9G5VLXzmhoaGBL50t84chZudR3+QNzGdkwsJFkwZI6MxtBktDNdfdfpodfMLPxaSvdeGBVenwFcFDJ6QcC7enx47odv7tSfe5+HXAdwLRp0yomfiJ9aWhogGEvcsyM6te1qAUaDmiofkUiIlIzgiR1ljTJXQ886e7/XnLTrcCZwFXp91tKjn/CzG4kmRSxLk385gNXlEyOmAFcksffICJJovviyA0MP3lyLvVtu6WNhrFKdmXntbe38/L6V3n0litzqe/l4nLat1ZulWpvb2f9ug3cdMdlVY9jdXEZmzv2rHhbQ0MDr7KF89/9+arHAfCde69gVMPIXOoaKkK11B0FfBR43Mz+mB77PEky9zMzOwdYDnwkve0O4H3AUmADcBaAu68xs68AD6X3+3LXpAkZuPb2dl5e6/xg0dZc6lu51nmFyl1qyZsd/Kq1M5dYXlwLW1zdeyLV0N7ezrb1r7Dh19flUt+24krat76cS10iMQg1+/U+Ko+HA5he4f4OnN/L75oDzNl10YmISK1raGigY8RmDj85n86dR2+5kob63XqNZbe6rXzkfV+sehw33XEZhXEjql6PhBF89qvEp6GhgfUU+Ydj8nnh/2DRVvZpqNyl1tDQwEh7kVMa81lS8VetnYwdr+49kWpoaGjgpREvs+cHzs2lvg2/vo6G+r1zqUskBkrqRKQmJDNx19Nx6wP5VFhcT/tWddWLVEN7ezuvvvwKVz50bS71LXt5BaPa98qlrmoa0klde3s7r65bx2X35rDwGLBs3RpG2bZc6hIRgeR9ztevZ/NtN+VSnxdX0751cy51iUi5IZ3UiUjtaGho4MURndR98Mhc6uu49QEa6tVVL1INDQ0NbNm2gUvefkEu9V350LWMbKg8K3gwGdJJXUNDA1t8OF98dw4LjwGX3dvCyIb9c6lLald7ezsb18OS2/JZbnFjEXUzZpS0jq2j47b5udTnxTW0b63cC9DQ0EBxxG7sdtJHKt6+q22+7SYa6gu51CUi5YZ0UheTpCt4I1fetySX+pav28go0we1iIhIrVBSJzLINDQ0sHHEi0w9qddt9XapJbe5uhkzSlrHhlN30gm51Ndx23wa6tULIDLUKamLRENDA5t9I5ccPTWX+q68bwm79bKMiIiIiAw++Sz+JSIiIiJVpaROREREpAYoqRMRERGpAUrqRERERGqAkjoRERGRGqCkTkRERKQGKKkTERERqQFK6kRERERqgJI6ERERkRqgHSVEREQCW71mGTfdcVmmc9aufx6Affc5IFM9hXFTMtUjg4eSOhERGZJeKS7n0VuuzHTOhnWrANhz9LjMdVFfeRvIyZMnZ/pdXda+vAWAwrgR/T6nMG5Kn/U9t24537n3ikxxvPjKCwCM3Svb/sPPrVvO3zQowdyVlNRJ9F5cC79q7cx0zrpXku+j98pe19jx2c4Z8l7cxLZb2rKdsy75MGL0yMx1MTbbKSKVDDSRenp98r87sX63bCfWT+21zvPOO29AsVx00UUAXH311QM6v7uBPibPP508JqMasr2e/6ah7wRTslNSJ1Eb6At+3dNPAzB2/KGZzhs7vu86170Ei1qyxfLKy8n3vfbu/znrXoKG/veoBDPgD8Z1yfNz6Nhszw9jB16nxGFbcSUbfn1dpnM61xUBGDa6kLku6iu/8GJJpGKix2TwU1InUYvpTWbACcyrSQLTcED/E5iGAwZH8hLT8wNAcT0dtz6Q7Zx1rybfR4/KXBf12U4Z6gbeOpZ0eR7aS4LWq/q9B8XrSCpb/vJzXPnQtZnOeWHDagD23zPbi3P5y88xhcrd44PJkE/qlq9bw2X3Zmt6eeHVpOll/1HZ3mCWr1vDlAm9jzlYvm4jV963JNPvXPXqZgDGjcrWFbB83UamTuj99ufXOT9YtDXT7yy+4gAU9rJM5z2/ztmnIdMpQUSXwEiZgScMaath/YHZTqwfHIk3gBdXs/m2m7Kds24tADZ638x1UV+5RU2vIemvgb62tjzdAcDIg/bMdN4Ueu8eH0yGdFI38H+aZMDWyD4StEqmTNi/1zoHHkvygbTbhGzdWFMn9F7nQGNZncayT0O2WPZpGDwfjrHYWIQlt3mmczavS77vNjp7XYOhRUoJQ2UDT3aTpO7QXhK0XtUX9HqWnabX88AM6aQupn8axSL9tfMtUhnHsQ2iFqmYeHENHbfNz3bOuqQXwEZn6wXw4hqor3yRqdezyNAxpJM6kcFIH9LxG3jinfQCHNpLgtar+t57AURk6FBSJyKyiynxFpEQtE2YiIiISA1QUiciIiJSA9T9KiIiItKH5etXcfkDczOd88KrLwGw/6j9Mtc1hWzndFFSJyIiItKLgS85tgaAkQdmS9CmsN+A61RSJyIiItKLwTTxSWPqRERERGqAkjoRERGRGqCkTkRERKQGKKkTERERqQGaKCEiIiIyALNnz6atra3ibU8/ney33TVhorvJkycPeBJGb2qipc7MTjSzv5jZUjO7OHQ8IiIiMrTtvvvu7L777rnWOehb6sxsOPAd4L3ACuAhM7vV3Z8IG1ltiumqJKZYRERqmd5vK4vt7xr0SR3wDmCpu7cBmNmNwMnATiV1Mf0DxxRLX/K+IulL3rHE8hzFEodiUSyKpfZi6c1Qfb+NUS0kdROAZ0vKK4Ajut/JzM4FzgWYOHHiTlU4lJOXmF4MMcXSl1j+X2KJAxRLbxRLZYqlsjxj0fvt4GDuHjqGnWJmHwFOcPd/SMsfBd7h7hf0ds60adP84YcfzitEERERkQEzs0fcfdqO7lcLEyVWAAeVlA8E2gPFIiIiIhJELSR1DwFTzewQMxsJnA7cGjgmERERkVwN+jF17t5hZp8A5gPDgTnu/ufAYYmIiIjkatAndQDufgdwR+g4REREREKphe5XERERkSFPSZ2IiIhIDVBSJyIiIlIDlNSJiIiI1AAldSIiIiI1QEmdiIiISA1QUiciIiJSA5TUiYiIiNQAJXUiIiIiNcDcPXQMuTOz1cCynfw1Y4EXd0E4u4JiqUyx9BRLHKBYeqNYKlMslcUSSyxxQG3GMsnd63d0pyGZ1O0KZvawu08LHQcolt4olnjjAMXSG8VSmWKpLJZYYokDhnYs6n4VERERqQFK6kRERERqgJK6gbsudAAlFEtliqWnWOIAxdIbxVKZYqksllhiiQOGcCwaUyciIiJSA9RSJyIiIlIDlNSJiIjIgJnZMDPbJ3QcoqRuUDKzQ81st/Tn48zsk2a2b+i4QjOzT5nZPpa43sweNbMZoeMKycxGmdmw9Oe/MbMPmtmIQLF8LX1+RpjZAjN70cz+PlAseg1VYGZ7mtn/M7P/TMtTzeykQLHo9RwxM2tOn59RwBPAX8zsotBxDXVK6jIws5O6PiAD+wWwzcymANcDhwDNoYIxs0/151gOznb39cAMoB44C7gqQBwxWQTsbmYTgAUkj8mPAsUyI31+TgJWAH8DhPoQiO019NX+HMvBD4HNwLvS8grgsgBxQASvZzP7UF9fecbSLa4YLtbekD4/pwB3ABOBj+Ycw3YxXDSa2bVm9q3evvKIIYYEZTA5HViS/vP8bcA4Ot29AzgV+Ka7fwYYHzCeMysc+3jeQQCWfn8f8EN3/1PJsfyCMHvZzNb39pV3OO6+AfgQcK27nwq8IecYunR96LwP+Km7rwkUB8T3GnpvhWMzc48CDnX3rwFbAdx9IwFeQ6kYXs8fSL/OIUn+Z6VfPwCCtDKnYrhYG5EmkqcAt7j7ViDkzMsYLhofBh4BdgcOB5akX38HbMsjgLo8KqkV7v736biBM4AfmpmTXNn+1N1fzjGUrWZ2Bkky9YH0WO5damkMTcAhZnZryU17A8W84wEeMbMWklaXS8xsb6Az7yDcfW8AM/sy8DzwE5IPo1kkj02ezMzeldZ9Tnos1Ov+12b2FLAR+Gczqwc2BYolltfQecA/A5PN7LGSm/YG7s87HmCLme1B+uFsZoeStNyFEPz17O5nAZjZbSQtUyvT8njgO3nG0o25+wYzO4fkYu1rZvaHnGP4PvBX4E/AIjObBOR90Vqqx0WjWb7XAO5+A4CZfRw4Pk10MbPvAS15BaGvjF8ke7l9muQfeh5JJn5BjvW/AfgWcEZaPgS4OMDjMAk4DvgtcGzJ1+FAXYB4hqV175uWC8CbA/6fPNifY1WO4VjgVuBf0vJk4FsBH5P9gOHpz6OAAwLFEctraDRwMPDT9PXU9TUm0OPyXuAeYDUwN32POy5QLNG8noHFFWJbHCKWtP4/kHSR/w54Y3rs8VDxlMSV+/t+Sd1XAk+lj80Iki77XN9vS2L5S+lrOH3f+0sedWudugzM7IMkzdyHkrS+3ODuq8xsT+BJd5+UUxxvc/dHuh37gLv/Oo/6Y2XJZdksYLK7f9nMJpIkDb8PFM8DJFfzN5K0fJwBnO/uR4aIJ5QdjT1y91/mFUus0haxFe6+2cyOA94M/Njd1waIpQC8k6R1+XfuHmRjdDP7srv/a0l5OMljMitALN8GppIk304yFGepu1+QdyxpPMcC/xe4392/amaTgU+7+ydzqPuzfd3u7v9e7Ri6S8cXvhN4Eljv7tvSCRx7u/vzAeI5C/gSsDA9dCzwJU9b8qpat5K6/jOzG4Dr3X1Rhdumu/uCnOJ4FDjT3R9Py2eQvKCPyKP+CvG8E7gW+FtgJDAceNXdc53ibmazSbpnGt39b81sP6DF3d+eZxwl8RwM/AdwFMkHwf0kz9Nfc6j71/QxvsXdP1jtGEpi+WH64zjgSKA1LR8P3O3uuQ04N7PH6ftxeXNesZQysz8C00ha7eaTtK6+zt3fFyCWCSSthdu76Su95+UQx49IWjeutGSm8k3Ao+7+pbxjSeM5FTgmLS5y95tDxBGamV2a/vg64O0k/6uQDGNY5O7/ECiu37r7u3Z8z3yY2QHAESTvN7/PK7nUmLp+Sq8SJ/T25pZXQpf6MPBzM5sFHA18jGSGWCjfJrlyvYnkg+ljwJQAcRzh7od3jS1x95fMbGSAOEjr/ytwcqDqr0m/fwg4APivtHwGSZdabjyucUldy3Ocn37/Sfp9FrAh51hKdbp7R9qq+U13vzbAGKmuGbf/G/gzr41fc5KB+Xk7C5hrZpeQXADMc/dvBIijy6PAy+7+G0uWftnb8x1LHcXFmrv/WxpLC3B412NgZl8i+QwIpcXMTgN+6XG0Vr0DeHf6swO59KQpqeuntDl3g5mNdvd1gWNpM7PTgV8Bz5LM+tkYOKalZjbc3beRTCJ5IEAYW9Pku2uQdz0BJkqY2bX0/cZb9S4Sd78njeUr7n5MyU2/NrMQH9AAB3cldKkXSGao5cbdlwGY2VHuflTJTReb2f3Al/OMp0TXxI2PEXDiBslMxte5e6jJEZjZ4SXF/yAZkH8/cI+ZHe7ujwaI6R+Bc4ExJMNvJgDfA6bnHEo0F2skS5hsKSlvIWlpDuWzJON0t5lZ16xtz7vHCMDMriJpxZybHvqkmR3p7pdUu24lddlsAh43s7uAV7sO5vEhDRW7jsaQdHU+aGbBuo6ADWmL2B/N7GvASpIXV96+BdwMjDOzy0laNL8YII6HA9TZm3ozm+zubQBmdgjJAOIQ7jaz+ZSPS1rY9ylVM8rMjnb3+wDM7EjC/M92OQv4P8Dl7v5M+jz91w7OqYY2kmQyWFIHfL1b+SWSiS1fJ/m/acw9oqRl9x3AgwDuvsTMxuUdRGQXaz8Bfm9mXd3QpwBVHzPWG09XHYjE+4C/c/dO2D506w9A1ZM6janLwMwqrcdGHoMf0/r7nIjR1QqRtzSuF0jG032GZEbfd919aYBYXk9y9WzAAnd/Mu8YukuXYnB3fyVA3ScC15F8WENyJX2uu+czvb5nPFGMSzKztwFzSP5XAdaSLHabeytQTMzsF8BbSNY+257Y5XXhGisze9DdjzCzP7j7W82sjmR8X6gxmE8C76JSflUAACAASURBVO92sXaHu+e6fmraqvpukmT7XnfPfchASSxdE+UOcfevmNlBwPgQE+XS5YmO83QtTjMbQzJ+uOr/L2qpyyCv5K2P+rcnbWk34/5E8By6+7J0bavxXeMt8pS+YLqsImkJ2n6bB1rk1swOI7maHZMUbTXwMXf/c14xuPudZjYVeH166KmQXWvAA0AH6eDhUEGks8ffYsm6kxZ6SEX6HF1J0iK1e9dxd5+ccyi38trA96DSyRGnkVyIlE7aCNFFfo+ZfR7Yw8zeS7K2YMjVBj5D0vJderH2TwHi2EYyxMUJMNSlm++mMTQCXwFeIRmzG2Ki3JXAH8xsIUkDwzHk0EoHaqnrFzP7mbv/r95mzuV9tWZmFwCXkrSObR/MHPCq8QMkYz1GuvshZvZ3wJfzmmFpZs+QPC+VVpr0AB+MwPYlTb7g7gvT8nHAFXkuaWLJiu/n8Vrr2N3A9z1dFDNPZva/gKvTGIzkCv8id/95gFhGk7yGuh6Xe0j+Z4Mkd2Z2XxrPN0jG1J1F8v58aZ8nVieWPYCJ7v6XvOvuFsedwDqSFfq3r8bv7t27Z/OIZRjJ4t0zSP535wM/CDkgP016g12sWbIV5D+SbLlnJLuzXOfu1+YZR0k8j3ZNlHP3t6bH/uTubwkUz3iShNJI1svLZfarkrp+MLPx7r6yt+7PvLs9zWwpyUzPELs29GBmj5BcHd1d8mJ6LOAYvyhUekPJ+03GzH5AMkaqq5X5o8C2EMsOmNmfgPe6+6q0XA/8JsSbbtrNuJjyx+UteS6v0i2eR9z9bWb2uLu/KT12r7u/e0fn7uI4gl6gdYtlsbsflne9g0XaE9C9ZffHOdb/GPAud381LY8CfhuwceFBkiWTHkqTu3qSJa3eGiieD1Jy0eg5rSMbvOtuMOiasRdqzFoFz5Jcwcaiw93XWc5bslRiyZIQR/PaGI9fBQynzcz+H68tm/H3wDM5x/D2bklTa5pchTCsK6FLFQm3//Sh7n5aSfnfLFkrLpRNaWvQEjP7BPAcybp+efsSyYSAuwHc/Y/peK0QHjCzN3m6HmdIZnYUyWPTtX5f18zKUL0Al5Ls5vMG4A6SfYLvA3JL6kgeg9L9TLdRubckL7FMlNPs18HCIllkl2TQ+91mdjvlg5lzX8k7tdjMmoDh6digT5KMncqVmX2XZH28rjF1/8fM3uvu5/dxWjWdDfwb8EuSN7tFJN1qedpmZoe6+9MAlqw8n8vG0hXcWTL7FZL10O4IFMvGbrNfjyLZkzaUTwN7krx2vkKyLlvFiVlVVukCLVR3ztHAx9PhFZt5LZEK0RJ0Pck4trKu4IA+TDKh5Q/ufpaZ7Q/8IOcYfkiy8sLNJM/NySSPUxDuPjftNeqaKHdKwIlywWa/KqnLJpZFdpenXyPTr9AuAL5A8sb7U5LxJl8JEMexwGFd41zSF1Kwq3x3f4nkQzqki4CF6YBqI2lpyDuxBMDdLyppSTWS8TehVuU/D7ghHVtnwBoCJFFm9hN3/yhwpLs/RDK4O8jzk4riAi01M1C9laxz93mhgyix0d07zawjneyzimRf59y4+7+b2d0kr2eAs0LOfk0tAdaT5jZmNtHdlweKZV+S9xV4bZZ91Smpy8gjWGQ3xAzTvrj7BpKk7guBQ/kLyYKYXd3kBwGPhQrGzP4GuJCes/dyW2fL3RekH86vI0leQs9+vR/YSvjZr3/ktdmvuPv6QKG8LR2re7aZ/Zhu3VcBZm4Hv0Azs33S5yPX3Rp2YKGZXU3S6l7aOxJqCZyHzWxf4D9JWg9fIczraRvJazn47NduEwi7uoKdZB/lvGn262BgyeKO7yFp5n6eZJHdj+c10NvMvunun7ZetorJezCzmfW59EGOs1+7Ho/RJOMYfp+WjwAecPf35BFHhbj+RLLqfPfZe4/kGINmv1aOJYrZr2b2SZLnZzLJOLrSpC7YmK2QzOw2dz+pl1ntQR6T9MO5O8/zAq03luwxvY+753oBG+Hs1+ATCC3Zqeb+dGbyGDT7NW4WeJFdM3ubuz9iZsdWut3T1cbzYsm6a8+SXNE/SM9Whlzi6e3xyDuO7rpmNIaouyQGzX6tHEtss19nu/t5IepO64/tgtGAgwJ2nUUtfXxmAZPd/ctmNhE4wHNcaDfC2a8LSd5fOkLUn8bQNYv9UXc/fMdnVCEGJXXZpB9EuPvqQPUPB25w978PUX+FWN5Lsu/gm4HbgZ96jovrxsheWwz5kyRjXW6mvMsmty61GJZVKal3+3IdaXkY8KfSYznG8kd3/7sdHcsxnnPc/fpux65y94tzqj+qC8Y0puAXRV0sroWQMbPZpAvtuvvfmtl+JMt35LbQriXrtr7d3Tel5d1JlhPJ9fVsZp9Nf3wjyTCTYBMIzex3wJMkEyX+u/vtnsPOLBpT1w/pVdGlwCdIWqOGmVkHcG3eL2p332Zm9WY20t237PiM6sYC3Ekyq3E3kuTubjP7cogm+IhmJz9CebfRRSW3OfkOaNbs18pim/36YTPb5O5z03i+S8n6Y9XWNSQgVKt2L35nZm9PJ5CEdguvLYQcckxqlyM8XWgXkklZluy/nafS2a+Q7P0aYvZr156vlSYQ5t1qdRLJEK1Gkv+V3Kmlrh/M7DMkmfe57v5MemwyMBu4092/kXM83+f/t3ff8XZVZf7HP9+AQOiOgAM/BZQyEoHQpYnSBnkhRRApUkSKjsoEGf3NCCNDEUFxFAaVKkWEoAKCSG9SpYQQiAJjRqWIdAYIHcL398daO/fcm3NvAj/uWqc879frvnL23gn7Ibn3nLXXWs/zwBqkdj4vNudrlDTJg7mtSAO6ZXNMp9l+pEIsk5g1O3kF2weVjqVTSNqU9OY7KPvVuctFhXh2ADbIsdTs/TqeVNOryUr7X2DP0vuSWuIZS/7ZIWV9PmP7gApxtOua8xwwCfhWyf1Kku4lzbw8QHqfq1bSRB1WCFkdUmhXqfdrk81+Q83sV0k72v7l7M4VimW87Sr1QGNQNwfy09Dmtp8acr7WD1Lb1kGls2JzyZCVgcuAc23/vuT928QzyfZaaulmIekWF2zLNSSe+YEDSS2X9muyUG3/pnAc81Ix+1XSurZvLXnPOVU7+1WD+xYvBFxIyhA+JMdVNPtV0ndJM7nn5FM7k75vngM2tL11wVg6ooNPjuVk0spM9ULIAJI+S5rpXoO0L/TTwL+XGMBI2t72Bfn1u51KN1XXbh9brb1teSl6b9KScGvHj8+P+r1jUDd7Iz2lddoTXEmS3mRgprD1G6l5oi667Fk7O7lNPD8nTcHvYXvlPBvzuxL7tvJg5b22p+XjHYGx+fIVth8f7RhaYpn5xirpd7bXK3XvNrGcYftz+fWets+czR8Z7XhaMzyrZ3pKutn2Bu3ODd0TWSCWVRjobXpfzYfGPGu4PKkjTO1CyE1MH2Kg0O41LlRod8jPc7WEgJZ4tiStpH2GwfvYFgbG2V6nQky/BO4HdgUOJyW13Gd7wmjfO/bUzZmR9q4V3dcmaU9gAmnmBdKmzP9ywZ5/Ddu1WjwNZ3dS26mvkLKT3w9UyWbMlrO9k6RdAGy/nPdnlvA9UtHYafn426QZ1flJyzZfLBQHDB6oFNsnNozWAf4EBrJfq7BdqwXXcBaU9BHbtwFIWgdYMF8rklWoVG7mIgbqTApYRdJDwLaVZlU7phByTjC6J08m3F8jhGFe1/I30vaAHYE/kh6OZpAqVXy1UkzL295R0ra2z5R0Dqnm46iLQd2cGS+p3RuJKPghJWkPUjuhA4HJ+f5rAMdIKtrMuUNtZ/s44BVSe66mltJxleJ5Lc/ONR0ulqPcJuu1gS+0HL/QZF5JuqlQDI0xOTtvTMvrmR8GhZcYO3ZpQpUbtGf7AKdJagZy04G9lcpVHFUohiNIH9KbeKDN0hjgaOBIUoHkopolX0lLUPnBxKmTxN2q1y1hrKTVST/P8+XXrT/PpQsy30uaCZuH1JpRpAeC04GiW11aNHVAn80/14+R9pyPulh+7SI5XXpn2w8MOb8saU/buhXC6hjD7Km4q/Sex5Z7b05qKD0OuJKUIPA5278tcO+h5UNWbpavSm8ZkPQAqfxCu6f6okuMkp4Azs2x7JRftwZTpa2bhmnQbvvTheOYK2fYL0L6fHi25P1zDPcCq3pIvTFJcwNTba9UIaZtgP8EliKVKVqGtJz24dKx5HiuZaDQemuy3KjXE1T7QswtIZQtyCzpB6TZ5ANtT8/nFiatVrxcYsmzTUz7kIoyr0oaXC4IHGL7xFG/dwzquoeke22Pe6vXel1e3tyVlIV1Y8ulhUkNyqt0lACQ9B5gXdIg4tahyTajeN+7gS08pIq5pP8DXFZzL1BNefvCsGrtsctZp02D9vHKDdpLJibkOP4CnEfKYK/SDF0j1Asc6doox3Q3qUzF1bZXl7QxsIvt/UrHkuPpmHqCtUmaBqzoIYMZpTqq99teoU5kdcTya3cZqY5WzRpbtd1CSopYjPQ03ZhO3d6vGwBTbF8iaTfgIEnHFcreOwa4WNK/AE2ZgTVIT6/HFLh/R6qdGDGC6g3as1VJGa8/yUuep5FWAUruY5tlSS8TMG/BOFq9bvtpSWMkjbF9naTvVIpl0OBN0mLA00MHNX3E7f7f84xzlb+T/FD2bWAp21tKGkfqvjHqdfxipq6LSHoJaNeSTKR2MQsUDqnj5DIIK9i+Ou9nm7uZkq8Qyz2k2ZdVSTXRTgO2tz1iW7N38P6fAA4ipdUb+ANwtO3LStw/zDmlYsMHkQZU/0Jq0D7F9l4VY9qIVCh6UdLs3REu0BJxNst72N54tGMYStLVpOK6R5EeHp8gdVMoWi5JqcD60cAzpL2HZ+V4xpCy7C8vGU8nkHQhcMHQ/af5QfozJZak28R0GWnZ9eA88z43aRZ+1LPHY1DXRTRM3aZGoRmgjiVpX2A/4O9sL6dUF+5E25tWimeyU2HQQ4BHbP+kE0oAhM6mSg3a873nIhUT34u0sfss4Gzgo8C3ba9YOqaaJC0PvBeYQloNGUPalL8McIlzJ46C8UwiDf4XAU4GtrR9ay5vMrHW/uGa8paSC0j/Pk03n7VJJZw+5TqF8O+wvXbrnu5SWwc6rSRFGIHtB0f6qh1fB/gyKRnheQCnGm1LVIxnuqRvALsBl+QPzHdVjKcqSXu3OXd0jVg6jZLdJB2SE6GezeVESpsGbAscY3t129+3/bjt80gtAfvNscB02y/aftP2G3kJ/1Lg0ArxzG37Sqciw485F/W2Xby0iaTzJW2Vl+mrsf2I7Y+Q6sE9QGoVdrjtdWoM6LIX837qpvLBuqQC3qMu9tSFXvKq7deaUnB5yrvmVPROpASOvW0/Jmlp+ng/G+37m1bZI6XUDWZfZm3QPuoV34fxY3KDdtKH03RS9lyxBu3ZqrZfaHehVmZwZcu2mzG1PSnPqJb2ZsvrofuoS7/XnUCa0f0vpWK7Z9QYXDZsXwtcW+v+QxxIavu3nKSbgcVJXT9GXQzqQi+5XtJBpDpKmwNfAi6uFUzOPP1+y/FDpL11/Wp74NdKnUia/qZfqhTLRaRM6atJhUprq9qgXdLxDMwqzHK9Twd0MHJNurEjXBstTc1Ukd7nmgSWojVTAWxfDVydy9/sAlwl6WHgFOBntl8f8T/QgyStDTxse3LOUP4CsAOppNVfS8QQg7ouJGmCU5HdEc/1oX8j9dubSvphupTUMqwKSdMZeHqeh7T0+oLtRYb/U+94DAe2Of0ccKftKYViaO1vug8D/U0Pl/R3LtzfNJvf9r9WuO9wXs/L883AanEGz8qMtkktrw8D2vaXLkGpSfywXLa47R2S9rV9SuvJvJWg6H46ANtzlb7nSPIS426kbj53kfZfbgjsSaq72G9OIrWqhNS552BSsezVSHsgR322LhIlulC7zfaqWGQ3zBlJ2wHr2D6o4D3PAdZiYMZyK+AOUk/NX9r+boEYOqq/aY7pW8Atti8tfe92VLFBe5tYqr6XtGS/zkf63r2b9D2zKnCb7Q0LxvJe4FekdpDNIG4t0kPap4bWgewnki4gvY+cRVp6fbTl2iTba1ULrhJJdzv3Gpf0I+BJ24fm4yKJEjGo6yIjFNldCJjhikV2O0Eu4Dr0G/o50izEt2w/XT6qwSTd6oKdPyRdAezQ7JNSav90HvAp0mxdvxasng4sQPqwfg1mNmhfuGJMVRq0t4mjIzK0JZ0LHGl7aj5eGfia7c9ViGVjoOnC8oe8f6uvSdok/h4Gk/R7YDXbb0i6H9jP9g3NNRfo5BPLr91lMh1YZLeDXEbaH3VOPt45//o8cAZQujr/9i2HY0hP+KWfopYmDVoarwPL2H5ZUqk+tABI+jJwtnPrKaUesLvY/nHJOABsL1T6nsNR/QbtnepDzYAOwPbvJRXvJpHvfR0wYv28fmP7WknrM2uyUT/vG55I2tv9FCmR5UaYWRonsl/DLCbmzdR/ch+2g5kDG9jeoOV4qqSbbW+QC1GW1jqIfIOUbr9t4RjOAW6VdFFLTBOVGrTfWziWfW3/qDnIyQD7kjI/i1LKBvgs8AHbR0h6P7Ck7dtLx+L6DdqH7v+cf8gG/FozmPdLOhX4WY5tN6DK7GWYlaSzgOVINfyaZCPTx8lgto+UdA2wJHClB5ZCx5D21o26WH7tInlq9xjgEODrQ6/bvqB4UB1EqT/jfrZvy8frAKc4VfTu2z2HktYi1e8TqUn8pNn8kdGK4x5gfPNGlxMD7nGFpuiSTiCXELG9Up41vNJ26RIiTTzVGrR3KknzAf8EbJRP3QCcYPuVelGFhqT7gHGOQURHiZm67vJF0uzCosy6lGhSVe1+tg9wWt43BmlZep88K3VU6WAkvQ84njSgMnATMMF2kdT2FncBfyP/vFecEboC+IWkE0l/H1+kXkHbqiVE2jis4r07Th7wn2p7N+AHteMJbf0e+HvSlqDQIWKmrgtJ2tsFGgN3q1w3Sc3erYpxXEVa/jwrn9oN+KztzQvGsD+pPMXjpCWSZjlt1VIxtMQyhlRqpkkGuJL0wV28Tpyk20glB+7Ig7vFSTN11WdzFQ3agZlJPlvbfm22vzkUI+li0kPZQqRSHbcDM/fn9vPscieIQV0XyjMKX2RgWeJ6Uo/Tviv22CqXH/g2sJTtLSWNA9arNQBul8JeKq295X7/Q5qVqp7520k6pYSIokH7sCSdRPr3+TWDl6S/P+wfCqMuF9UdVuz3riuWX7vTj0mFbJsN5ruTWrbsUy2iznAGcDqp4CPAH4GfA7VmNZ/KCRoT8/EuQOnB1cMUyrqaHUkrkJbBx9FS/b5GnTrbZ0u6k4FZw+0qlRD5IQMN2q9lSIN2+rPfauNv+WsMaVYodIBm0CbpA8CjzR5HSWOB99aMLcRMXVdqLXA40rl+I+kO22u3JkWUnhkbEs/SpA/t9UjLFbeQ9tQ9WDCGnwD/AFzC4CWS4rMdkm4iLQX/gLQndC/Se1Dx7gX57+X41q4akg5tCoUWjGPm96ek+2yv1HKtWHLPkOzXWdSs3xc6k6RJwPrN8nheQbq5VrJRSGKmrjvNkLSc7T8BSPogndG/srYXc9uaJrtyXSrOUuVkhNr7Sx7KX/Pkr5rG2r5GkvLA9lBJN1KnJdUWwJqSvt9SV2sb4NDCcXREg/ambp+kw4HHSMvATdmXKrNkeZ/j/wU+zOCZ3U1qxBNmMXfrfkfbr1VONgrEoK5bfR24TtKfSW+8y5BmPfrdgaT9N8tJuhlYnAK99oZSS3P0dlywObrtTsqqfCUnS0yT9BXgEWCJSrE8QepNebakjwATGNy+rJSOadCebWH7Iy3HJ+SkklFvJ9fG2aTtE58k7SHeE3iyQhyhvSclbWP71wCStgWeqhxT34vl1y4laV7SspqA+20X7Q7QSSS9q0kSkTQ3A38v/10jeUTSni2HszRHt31mgRiOtX1AS6baIDUy1CStTSoeuygpKWAR4Lu2b60QS+sS/aHA5qTiw8X393USSbcAPwLOJX3f7AJ82fb6FWK50/aaku5psrUlXW97xI36oQxJy5EG3kvlU38Fdm9WkEIdMVPXRfKH4sO2H7P9am6ZswPwYN4P9EzlEGt5JHdMmAhcZ/sPNYNpHbRJOqDEIK6NpozK9yrcuy3bd+SXL1B/ZvnXzQvbh+b9QQdWjKdT7Aocl78M3JzP1dA8kD0qaStS0sT7KsUSWuQZ9zVtr5vrgsr29NpxhZip6yqSJgOb2X5G0kakp+n9SbWCVrJdfKmxE+R9dJ8m9XpdgdSwfmLTWaImVW6OLmlN23cOObe17YsrxLIiaevAMgzuFVllj1QugdNs6r7d9hM14gjtSfokqXfm+0lFvBcGDmuW+0Jdkm6wvdHsf2coKQZ1XaQ1w1XSj4Anm2y9mlmenUTSUsCOpAHeEsC5tg8e+U+Najy1B3WTgT2dG6NL2gU4YMi+qVKx3A2cCNxJS2LP0EFnoVg+Q2q591vSUv1Hga/bPq90LJ0kJyfsy6xN2j9fK6bQmSR9k5Tc83MG1xHs1xWjjhCDui6i1Pt1NdtvSLqf1Of0huaa7ZXrRtgZ8nLA9qTltCVtF62dNKQ8xPzAS80lCjdHz5nR55GyGDcE9gA+abt4VnCzR6r0fdvJA8zNm9m5PJi5OsoC6RbS7NjQgff5BWPomESjMDxJf2lz2v2+L7W22FPXXSYC10t6ivSEdCOApOXpkAKztSg1/96atLF7A1LR1m+QWlEV1ZSH6AS2/yxpZ+BCUiHif7Q9tHRGKRdL+hLwKwbXzKvxZD9myHLr06Qit/1uftv/WjmGSS2vZ0k0Cp3B9gdqxxBmFTN1XSbXXluS1KfyxXxuRWBB25OrBleJpHOAzYAbSPsMf9NUOe9XkqYyeLZjCdLA/1UA1+n92jFP9pKOAVZloNvHTsA9HTCgqUrSt4BbbF9aOxYoW4A5vHWSVmbWDjE/Hf5PhNEWg7rQ9XIJkQsi+2qApGVGul6yq0WnkrQ9aUlawA22f1U5pOry1oEFSIP/16mwZWBIPFX3pIbhSfoPUq3HccClwJbATf2asNcpYlAXQh+QtASDn6YfKnjvmaV48vEe5FI8QPFSPJLmAq6wvVnJ+4a3LgZ1nSuvBowH7rI9PmeTn2p768qh9bXYQxJCD5O0jaRpwF+A64EHgMsKh3ES0PSH3Ag4GvgpaTn45MKxYHsG8JKkRUrfuxtIerekdSRt1HwVvv90Sc/n7hqrNq+b8yVjCSN62fabwBuSFiZ1aYkkicoiUSKE3nYEsC4ps3N1SRuTkklKmqtlNm4n4OScTXm+pCmFY2m8AkyVdBWDyzH0dWalpH1ILdPeB0whfe/8DihWS7CTEo3CiCZJWhQ4hZQt/QJwe92QQgzqQtfLe6OGZfuCUrF0oNdtPy1pjKQxtq+T9J3CMcwlaW7bbwCbAvu1XKv1HnRJ/gqDTSAVZL7V9saSPkTKQA1hENtfyi9PlHQ5sLDte2rGFGJQF3pDs4djCWB94Np8vDGpuGw/D+qezXX7biA1r38CeKNwDB1TikfS0rYfqtS6rRu8YvsVSUia1/b9kv6hdlCh80i6xvamALYfGHou1BGDutD1bO8FIOk3wDjbj+bjJUnNyfvZtqSB1FdJBYgXAQ4vGYDtIyVdw0ApniY7awypzV1JFwJrAEg63/YOhe/f6f6al9QuBK6S9L+knqshADNrgs4PLCbp3aQMaUht3JaqFlgAIvs19JChXTVy0+l7otNGImkx4Gn38Q99a92zqIE2MkkfIz0EXG77tdrxhM4gaQJwAGkA9wgDg7rngVNs/7BWbCFm6kJv+a2kK0jLfSb1f72ubkh15CLVRwPPkJIlzgIWA8ZI2sP25TXjq8jDvA5D2L6+dgyh89g+DjhO0v62j68dTxgsZupCT8lJEx/Nh31bUFbSJOAg0kzLycCWtm/NG98n9usMlaQZpGxXAWOp2Jc3hG7UaXUnw2AxqAuhB0maYnu1/Po+2yu1XItlxxDC2yJpMrCZ7WdyDcNzSXtjVwNWio4SdUXx4dAzJG0vaZqk56JYKW+2vH55yLV4kgshvF1t607a/iawfMW4ArGnLvSW7wJb276vdiAdYHwe0AoY2zK4FS3twkJolbcvfIdUHkjEsnSYVSfWnQxZ/AOEXvJ4DOgS23PVjiF0pXgwCrPTMXUnw6xiT13oGZKOA/6eVGPr1eZ8n3eUCGGOSbrZ9ga14widLWfXN3UnX8znVgQWtD25anB9LgZ1oWdIOr3Nadv+fPFgQugiLa32PkY8GIXQtWJQF0IIfW6YB6JGPBiF0CViUBd6Rm5fszfwYVqSAeIDKYQ5I2kD2zfP7lwIoTNFSZPQS84iLR1tAVwPvA+YXjWiELpLuw4B0TUghC4R2a+hlyxve0dJ29o+U9I5wBW1gwqh00laD1gfWFzSgS2XFgYikzqELhGDutBLXs+/PitpZeAxYNl64YTQNeYBFiR9JizUcv55IDoEhNAlYk9d6BmS9gHOB1YBziB9SH3T9kk14wqhW0haxvaDteMIIbw9MagLIYQ+J+liRmgfZ3ubguGEEN6mWH4NIYTwvdoBhBD+/8VMXQghhBBCD4iZuhBCCABIWgE4ChjH4FqPH6wWVAhhjkWdutAzJO0oaaH8+t8lXSBpjdpxhdBFTgdOAN4ANgZ+Sqr/GELoAjGoC73km7anS9qQVID4TNIHVAhhzoy1fQ1pa86Dtg8FNqkcUwhhDsWgLvSSGfnXrYATbF9Eqr8VQpgzr0gaA0yT9BVJnwKWqB1UCGHORKJE6BmSfgM8AmwGrAm8DNxue3zVwELoEpLWBu4DFgWOIHWUOMb2rVUDCyHMkRjUhZ4haX7gE8BU29MkLQmsYvvKyqGF0FUkLWD7xdpxhBDemlh+Db3kJNsX2J4GYPtRYPfKMYXQNSStJ+le0mwdksZL+nHlsEIIcygGdaGXfLj1QNJciCZUoAAABJhJREFUpGXYEMKcOZaUZPQ0gO27gY2qRhRCmGMxqAtdT9I3JE0HVpX0fP6aDjwBXFQ5vBC6iu2Hh5ya0fY3hhA6TgzqQtezfZTthUgbuhfOXwvZfo/tb9SOL4Qu8rCk9QFLmkfS18hLsSGEzheJEqHrSfqQ7fuHKzRse3LpmELoRpIWA44jZZALuBKYYPvpqoGFEOZIDOpC15N0su39JF3X5rJtR/HUEEIIPS8GdSGE0OckHQ8M+2Fg+58LhhNCeJvmrh1ACO8USXu0O2/7p6VjCaHLTGp5fRjwH7UCCSG8fTFTF3pGnm1ozAdsCky2/elKIYXQdSTdZXv12nGEEN66mKkLPcP2/q3HkhYBzqoUTgjdKp70Q+hSUdIk9LKXgBVqBxFCCCGUEDN1oWdIupiBWYYxwDjgF/UiCqE75GLdzc/O/JKeby6RMsgXrhNZCOGtiD11oWdI+ljL4RvAg7b/WiueEEIIoaQY1IWekPu8XmF7s9qxhBBCCDXEnrrQE2zPAF7KyREhhBBC34k9daGXvAJMlXQV8GJzMgqnhhBC6AcxqAu95JL8FUIIIfSd2FMXup6kpW0/VDuOEEIIoabYUxd6wYXNC0nn1wwkhBBCqCUGdaEXqOX1B6tFEUIIIVQUg7rQCzzM6xBCCKFvxJ660PUkzSBluwoYS2oPBlENP4QQQh+JQV0IIYQQQg+I5dcQQgghhB4Qg7oQQgghhB4Qg7oQQgghhB4Qg7oQQgghhB4Qg7oQQl+Q9EL+dVlJu47ifQ6WNCV/zWh5HT2IQwijKrJfQwh9QdILtheU9HHga7Y/Weqeo32fEEKAmKkLIfSfo4GP5tmzr0qaS9Ixku6QdI+kLwBI+rik6yX9QtIfJR0t6bOSbpc0VdJyb+WmkhaV9GdJc7cc/yXf/yZJx0r6Xf5vr5V/z4KSzsj3vEvS1u/430YIoWfEoC6E0G/+DbjR9mq2fwDsDTxne21gbWBfSR/Iv3c8MAFYBdgdWNH2OsCpwP5v5aa2nwVuBj6RT+0K/ML2jHw8r+318v1OzecOAS7P99wE+E9J873l/+MQQl+IQV0Iod/9I7CHpCnAbcB7gBXytTtsP2r7VeBPwJX5/FRg2bdxr1OBvfLrvYDTW65NBLB9LbCEpAVzbAfn2K4D5gOWfhv3DSH0gblrBxBCCJUJ2N/2FYNOpr13r7acerPl+E3exvun7esl/VDSxsDrtu9vvTz0t+fYtrP9p7d6rxBC/4mZuhBCv5kOLNRyfAXwT5LeBSBpRUkLjOL9fwaczeBZOoCd8v0/Djxu+8Uc28ysWUmrj2JcIYQuFzN1IYR+cw/whqS7gTOA40hLqZMlCXgS2G4U7382aa/cz4ecf17SLaQBZ7NEexhwrKSppIfw/wG2HcXYQghdLEqahBBCQZJ2BrawvVfLuZuAr9ieUi+yEEK3i5m6EEIoRNIJwGYMZMCGEMI7JmbqQgjhbZJ0MLDjkNO/tH1kjXhCCP0tBnUhhBBCCD0gsl9DCCGEEHpADOpCCCGEEHpADOpCCCGEEHpADOpCCCGEEHpADOpCCCGEEHrA/wPOq/XCs0q0VAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Type Vs Sales\n",
    "plt.figure(figsize=[10,6])\n",
    "sns.boxplot(x = \"Item_Type\", y = \"Item_Outlet_Sales\",\n",
    "           data = train)\n",
    "plt.xticks(rotation= 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Snack Foods' 'Dairy' 'Fruits and Vegetables' 'Frozen Foods'\n",
      " 'Starchy Foods' 'Breads' 'Health and Hygiene' 'Soft Drinks' 'Canned'\n",
      " 'Household' 'Breakfast' 'Meat']\n"
     ]
    }
   ],
   "source": [
    "print(train[train.Item_Outlet_Sales>8000].Item_Type.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identifying the Outlet\n",
    "plt.figure(figsize=[8,5])\n",
    "sns.boxplot(x = \"Outlet_Identifier\", \n",
    "            y = \"Item_Outlet_Sales\", data=train)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1985])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outlet 27 Max Sales Guy on the Street...\n",
    "train[train.Outlet_Identifier==\"OUT027\"][\"Outlet_Establishment_Year\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Outlet 27\n",
    "\n",
    "We have observed that if an Outlet is of Medium Size, Belongs to Tier 3 city and is of Supermarket Type3 then it will have max sales.\n",
    "\n",
    "What all Items are kept in Outlet 27: All Item Types are being Sold there...\n",
    "\n",
    "How old is Outlet 27: 28 years of existence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation: Outlet 10\n",
    "We have observed that if an Outlet is of Small Size, Belongs to Tier 3 city and is of Grocery Store which results in Lower Sales\n",
    "\n",
    "What all Items are kept in Outlet 10: All Item Types are being Sold there...\n",
    "\n",
    "How old is Outlet 10: 15 years of existence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     528.000000\n",
       "mean      340.329723\n",
       "std       249.979449\n",
       "min        33.955800\n",
       "25%       153.633350\n",
       "50%       265.321300\n",
       "75%       460.733600\n",
       "max      1482.070800\n",
       "Name: Item_Outlet_Sales, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[(train.Outlet_Size==\"Small\") & (train.Outlet_Type==\"Grocery Store\")][\"Item_Outlet_Sales\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations Store Type\n",
    "\n",
    "Store Type: Small and Grocery\n",
    "\n",
    "1. Max Sales is 1500 where as Min is 33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
       "       'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
       "       'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
       "       'Outlet_Type', 'Item_Outlet_Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]), <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zq69qJkjOAJD0zohYZaFJSe+sV2BmRXj00Uf7PDaz4tUyKuysKsvMGkZE9HlsZsWr5h7LrsA7gLHd7rdsBAytV2BmZtacqrnHMhzYIJ9beb/lGeCQegRlZmbNq5p7LNcB10k6PyIeKuqNJY0CfgpsTxoE8EngXuAiYALwIPCBiHhSaQjamcABwAvA4RFxc36d6UDXmmXfioi2omLsr5kzZ9Le3l7Y6x177LH9et7EiRP7/Vwzs/6qZYLk+ZJW66COiL36+d5nAn+MiEMkDQdGAicCV0fEaZJOAE4AvgLsD0zKXzsDPwJ2ljQaOBmYTEpO8yXNjogn+xmTtZjRo0ezdOnSV469urFZ/dWSWL5U8fMI4P30c60wSRsBuwGHA0TEcmC5pGnAHvm0NuBaUmKZBvw80p3XGySNkrRFPnduRCzNrzsXmApc0J+4ijKQq4Tdd999lRvMkpg5c2YRYQ1KlUkF8CKUZmtB1aPCImJ+xddfI+J40tVDf7wWWAz8TNItkn6aZ/RvFhGL8vstAjbN548DHql4fkcu6618NZKOlDRP0rxGniT39a9/fZXjU045pZxAzMz6qerEIml0xdcYSfsBm/fzfdcBdgR+FBFvBZ4ndXv1+vY9lEUf5asXRpwTEZMjYvLYsWNrjXet2WeffV75WRJ77rlnidGYmdWulnks8yu+rge+CBzRz/ftADoi4sZ8fAkp0TyRu7jI3/9Zcf5WFc8fDzzWR3lT23rrrQFfrZhZc6plEcpti3rTiHhc0iOS3hAR9wJ7A3flr+nAafn7pfkps4GjJV1I6n57OiIWSboS+E9JG+fzppA2I2tqo0ePZvTo0b5aMbOmVFVikbQpadn8N5O6mu4CfhgR/+zziX07BvhlHhF2P/AJ0hXUxZKOAB4GDs3nXk4aatxOGm78CYCIWCrpm8BN+bz/6LqRb2Zm5ahm5v07gV8B5wM/J93X2BH4u6SPdF8/rFoRcStpmHB3e/dwbpASW0+vcx5wXn9iMDOz4lVzxXI68N6IuKWi7FJJ/wvMov8jw8zMrAVVc/N+o25JBXjliqOvJfXNzGwQqiaxqOLmeGXh6Cqfb2Zmg0g1ieEMYI6k3SVtmL/2AK7Ij5mZmb2imkUoz5H0GPBNVh0V9q2I+H2d47NBrugFPcGLeprVW1XDjSPiMuCyvs6R9NWI+HYhUZmZWdOqZRHKNTkUcGKxQg30CuELX/gC8+fPf+V4p5124vTTTx9oWGbWhyJvvve0bpdZqU466aRVjk888cSSIjEbPIq8YvFm4tZwxowZwwYbbMBzzz3HTjvt5P1Y7BX1uH/XHwsXLgQGfnU+UEXeQywysfiKxRrSNttsw4MPPuirFVtFe3s799x6a7+XaC9KV7fRU7feWloMjxf8ekUmll8X+FpmhRk2bBiTJk3y1YqtZnPgCH8m5tyCO5yqTiyStiUtHDmh8nkRcXD+/p+FRmZmZk2pliuW3wHnAr8HVtYnHDMza3a1JJZlEeHN183MrE+1JJYzJZ0MzAFe7CqMiJsLj8rMzJpWLYnl34CPAXvxaldY5GMzMzOgtsTyPuC1EbG8XsGYmVnzq2Xm/W3AqHoFYmZmraGWK5bNgHsk3cSq91gOLjwqMzNrWrUklpPrFoWZmbWMqhNLRFwnaRtgUkRcJWkkMLR+oZmZWTOq+h6LpE8DlwCzctE40qTJfpM0VNItki7Lx9tKulHSQkkXSRqey9fNx+358QkVr/HVXH6vpP0GEo+ZmQ1cLTfvPwe8E3gGICIWApsO8P2PA+6uOP4OcEZETAKeBI7I5UcAT0bERNJ2yN8BkLQdcBhpZ8upwNmSfBVlZlaiWhLLi5VDjSWtwwCWypc0HjgQ+Gk+FmlOzCX5lDbgvfnnafmY/Pje+fxpwIUR8WJEPAC0A2/vb0xmZjZwtSSW6ySdCKwnaV/SasYD2fP++8D/49XJlpsAT0XEinzcQepuI39/BCA//nQ+/5XyHp6zCklHSponad7ixYsHELaZmfWllsRyArAYuB04Crg8Ik7q+yk9k/Qe4J8RMb+yuIdTYw2P9fWcVQsjzomIyRExeezYsTXFa2Zm1atluPExEXEm8JOuAknH5bJavRM4WNIBwAhgI9IVzChJ6+SrkvHAY/n8DmAroCN3wb0GWFpR3qXyOWZmVoJarlim91B2eH/eNCK+GhHjI2IC6eb7nyLiI8A1wCEV73dp/nl2xfsfks+PXH5YHjW2LTAJ+Ht/YjIzs2Ks8YpF0oeADwPbSppd8dCGwJKC4/kKcKGkbwG3kPZ/IX//haR20pXKYQARcaeki4G7gBXA5yLi5YJjMjOzGlTTFfY3YBEwBji9ovxZYMFAA4iIa4Fr88/308OorohYBhzay/NPBU4daBxmZlaMNSaWiHgIeAjYtf7hmJlZs6umK+xZeh5pJSAiYqPCozIzs6ZVzRXLhmsjEDMzaw21DDc2M2sZHR0dPAuc2/8FRFrGIuC5jo7CXq+W4cZmZmZr5CsWMxuUxo8fz1OdnRzR4wIeg8u5BKPGjy/s9ZxYupk5cybt7e2lxrBw4UIAjj322FLjAJg4cWJDxGFmzcOJpZv29nZuuf0uVo4cXVoMWp76fOff93hpMQAMeWFpqe9vZs3JiaUHK0eOZtl27yk7jNKNuOuyskMwsybkm/dmZlYoJxYzMyuUE4uZmRXKicXMzArlm/dWVx6+vSoP37bBwInF6qq9vZ1/3HEzW29Q3jY5w19KF+bLHryptBgAHn5uaKnvb7a2OLFY3W29wct8bfJzZYdRum/N26DsEMzWCt9jMTOzQjmxmJlZoZxYzMysUE4sZmZWKCcWMzMrVCmJRdJWkq6RdLekOyUdl8tHS5oraWH+vnEul6SZktolLZC0Y8VrTc/nL5Q0vYzfx8zMXlXWFcsK4IsR8SZgF+BzkrYDTgCujohJwNX5GGB/YFL+OhL4EaREBJwM7Ay8HTi5KxmZmVk5SpnHEhGLSNssExHPSrobGAdMA/bIp7UB1wJfyeU/j4gAbpA0StIW+dy5EbEUQNJcYCpwwVr7ZcysaT1O+XveL8nfNykxhseBUQW+XukTJCVNAN4K3AhslpMOEbFI0qb5tHHAIxVP68hlvZX3W0dHB0NeeNp7kQBDXlhCR8eKssOwBtPZ2cmMGTM45ZRT2GSTMv8cDszEiRPLDgGAxXnJoVGTJpUWwyiKrY9SE4ukDYDfAJ+PiGekXvee7umB6KO8p/c6ktSNxtZbb117sGYGQFtbGwsWLKCtrY3jjz++7HD6rVHWbOuKY+bMmSVHUpzSEoukYaSk8suI+G0ufkLSFvlqZQvgn7m8A9iq4unjgcdy+R7dyq/t6f0i4hzgHIDJkyf3eu07fvx4nnhxHe8gSdpBcvz4zcsOwxpIZ2cnV1xxBRHBFVdcwfTp05v6qsXqo6xRYQLOBe6OiO9VPDQb6BrZNR24tKL843l02C7A07nL7EpgiqSN8037KbnMzOqgra2NlStXAvDyyy/T1tZWckTWiMoaFfZO4GPAXpJuzV8HAKcB+0paCOybjwEuB+4H2oGfAJ8FyDftvwnclL/+o+tGvpkVb+7cuaxYke67rVixgjlz5pQckTWiskaF/YWe748A7N3D+QF8rpfXOg84r7jorEgdHR08/+xQr+wLPPTsUNbv6Cg7jAF597vfzZVXvtopsNtuu5UYjTUqz7w3s6p1dnb2eWwGDTDc2Frb+PHjWbZikfdjIe3HMmL8+LLDGJD58+evcjxv3rySIrFG5isWMzMrlBOLmZkVyonFzMwK5cRiZmaF8s37Hgx5YWmpa4Vp2TMAxIiNSosBUj2AZ96bWW2cWLpphIXpFi58FoBJryv7j/rmDVEfZtZcnFi6aYSF6VpxUTozGzycWKzuHn6u3Jn3T7yQbiVuNnJlaTFAqofXlxqB2drhxGJ11QhdacvzfhcjJpS33wXA6ym/PmbOnEl7e3uhr9nfq/yJEyc2RA+BFc+JxeqqEf5wuGvRbO1yYjEbRAaa6HtadNIJ27rzPBYzq9o3vvGNVY5nzJhRUiTWyJxYzKxq++yzzyrHe+65Z0mRWCNzYjGzmmy99daAr1asd77HYmY1GT16NKNHj/bVivXKicWsSdRjqHB/LMzDt8se8efhyo3LicWsSbS3t3PLnbfAqJIDyfNMb3n0lvJieKq8t7Y1c2IxayajYOUe5a4g0AiGXOvbw43MicWsSXR0dMDT/qMKwFPQER1lR1FI92QRXYuN1i3oxGLWTFZQfjfQy/n70BJjWFHiexdsvfXWKzuEwrVEYpE0FTiT1NR/GhGnlRySWeH22GOPhrp5P2lSuWuvlb3uGpQ/gKFRKSLKjmFAJA0F/gHsC3QANwEfioi7envO5MmTY968eXWLaaCXx0X9x220y+P+KLKrwfXp+rT+kzQ/IiZXc24rXLG8HWiPiPsBJF0ITAN6TSyNrhUvjcvk+iyW69PWpBWuWA4BpkbEp/Lxx4CdI+LobucdCRwJsPXWW7/toYceWuuxmpk1q1quWFpheIl6KFstW0bEORExOSImjx07di2EZWY2OLVCYukAtqo4Hg88VlIsZmaDXisklpuASZK2lTQcOAyYXXJMZmaDVtPfvI+IFZKOBq4kDTc+LyLuLDksM7NBq+kTC0BEXA5cXnYcZmbWGl1hZmbWQJxYzMysUE4sZmZWqKafINkfkhYDjT5DcgzQWXYQLcT1WSzXZ7GaoT63iYiqJgEOysTSDCTNq3aWq62Z67NYrs9itVp9uivMzMwK5cRiZmaFcmJpXOeUHUCLcX0Wy/VZrJaqT99jMTOzQvmKxczMCuXEYmZmhXJiMaszST3tGWRWunq1TScWW4WysuNoBXkbB8I3Mq3BSBpZz9d3YjEAJE2StEfXH0Enl4GR9EbgbElnSTpQ0gZlx9SIutqZpI3KjmWwyG3z55LOAA6RtG7R7+HEYkh6HXAj8CdJH3ByGRhJbwB+C1wPLAUOBrYoNagGFREh6UDgR5JOk3SwpPXKjqtV5aTyP8AfgUXAoXV5H1+lm6TPAAHcBvwB+ExEXNSVWNyVUz1J6wAzgUcj4tRcdj6wKCK+WmZsjUjSrqQ5HO8FfkraavzTEbGs1MBakKQhpLq+OSLOzm31auD3wC3AHRHxRCHv5b8Zlj8hbhoRD0maAlwIfDYiLsyPD4uIl0oNsolIGkfazbQjIlZKOhTYJSK+mB8fEhErSw2yQUj6aP7xfuAM4AO5HW4REYtKDK0lSRoREctyUrkSeBy4HdgYeAH4NvDSQD9MtsQOkjYwEfEvSQ/nn+dI+iBwUV4Fuh14r6SznVyqExGPditaCowDkPR2YBtJv42Il9d6cA0id39tAdwFfBdYHzgwIh6V9H5gV0knRsTyMuNsNV1XgnlL99MiYi6ApH8HphVV377HMkhV3DR9U77HslnXY7mx7Q7MJf3H/4eTSt+616ekzSseHgI8JemtQBvwxCBPKv8HOAq4mfTB5T7g18DonHi/AVzrpFKMHtrmOHjl/3mXJ4AxkkYXcW/VVyyDVL5pOg34CnArsIGkH0bEjfmUF4CngY9GxBVlxdkseqnPsyPiBuBhYF9gMvDliPhziaGWStJmwGeBjSPi5lzWBuwKfJ/U7r4REZdJku/vDdya/q9LOgD4T+BrEbG0iPf0FcsgJWlr4AukP3j3A68F/iFpnfyJ5X3ARyLiD57bsma91Oe9+eGngZHAjIi4rJwIy9Ot7TxNGpH0sqTjASLiauC/gIOAj0fEpU4qxenj//rwnOg/Q0UyL+I9fcUyeK1Lumn3QVIS+XhEPJlH6fwdOKOyu8b/ydeot/p8JzAPeHNELB1sfzC7fl9JewCvI32Y/Xn+vqekz0XED3O313LSFYvbW7F6a5u7AAuAD0fEs0W2TV+xDBIV/axdE/XuA0YAXwM+FRH3SdqXNFR2KyeVvtVQn98HNu/qYhhsdZmTyl6kocTDSN1gM0h/6OYAO0s6tsQQW04NbfMsYGxEPAvFtk0PNx5E8kic6cCLpE+NrwMmkPbb/jOpD/aEiPh9WTE2E9dndST9GLglImYpLSXyE6AzIo6TdAhwT0TcUW6UraXstunEMkhI2ok0rHMGsE8ufhy4KR8HbODYAAAQnUlEQVQvJ02cumqwddf0h+uzZzlxbBcR8yS9CfgXqftlfeD7EfGcpNeQJuJOBV7wnJ5iNULbdGIZBCSNJzW0f0XEJ3PZB0nLOfzfiOgsM75m4/rsXa6bY4AtSZ+SPwDsCHwM+AFphvfWpK6xAyNiSUmhtqRGaZu+x9LiJE0g/Qe/G9gxXyITEReRRirtUlpwTcj12bM8P2L3iOggfTqeBtweER0RMRv4K3AkaR7P+cB3nFSK1Uht06PCWlS+gTeSNIzzkoj4D0nPAu+TtAXwF9Kww8dKDLNpuD57p7Sw4UX56zrgGtLorndJOikiTo2I7+eJkU8CG0TEXYOpi7CeGrFtuiushUhaNyJe7FZ2EPD/gH8njQz5EPAp4A7gexHxF3ntqh65Ptcsz5G4hPS7X1iZLPIQ4yNJw63nAB8mzeV5sbfXs+o0ett0V1iLyDdKL8pzGbeXtKukUXnUxzzSEOJHgAtIfd2PkP/9B8sfwVq4Pqs2AVgQecFSYIqkb0n6Mmky5NnAbqQVdP/ipDJwzdA2nVhagNL+H23Ab/OnxY8DHwF+I2k74DVA1yznR4DZ5H5wSeuXE3Xjcn3WZAWwsdI+KrNJn5BfT0omR5D2+TkUmBIRl5cXZmtolrbprrAmJ+n1pOWvr4uIw3PZOpFWLz2GNAJnAvAu0gzba/I5WwHPRcSTpQTeoFyftZN0EqlONgT+KyJuVtr742/AJyPirjLjaxXN1DadWJpYviT+JTAfeIm0Y+HciHi84pxRpOXJvwncHRFfLyPWZuD6rI2koV0rNKjbnj2SdgR+TNpf5cGSQmwZzdY23RXWpPJIkD2BMyPi08C1wH7AvpI2qTj12Yi4m9QtsbdWXc7dMtdnbfJN+peVNoyiK6koLbu+N6m75ltOKgPXjG3Tw42bVF6D6Sdd/6Ej4uLU/jgIQNLlEbEkXl3z603AJqQlHqwb12f1lPaVOQH4YESs6PbwdqTJkCdGxO89pHjgmrFtuiusCXX/z1o5hFDSB4D9gf8PmN0101bSRGCFP0GuzvVZO0lXAGdHD2tNSdokIpY4qQxcs7ZNd4U1EUnD4ZVPMK/pKo+0r7ryzxcDV5HWBBpecU77YP0j2BvXZ20krVtx+AfgDblcld8jz6h3Uum/Zm+bvmJpErkvez9SA3oi//zfkZe8zudUTk7bvPLGnq3K9VmdrjqQtBvw38A5pCVDHiDN6D4k8k6QVoxWaJu+x9I8VpL2B/8lMB6YGmlznlcujfMfAEXSUA2tAbk+q5DrYE/S2l/nkUYknUUa8XUHaWjrzRpEqw2sBU3fNt0V1iRyg1pCanT3Am+rKK88z5egVXB9VifPnfgIcEFE/Dgi2oCDgQ1IG3cdI2mEk0pxWqFtOrE0uIr+63HA86R9q48DpirvGS5pm/wHwNbA9VkdSUMkjSDtlT4ZmNj1WKQVi78XEfuTFp08oqQwW0ortU0nlgaXL3nfy6urx55M2jzpx8Aekn5GWhNovfKibB6uz751/XEDRkbEMuBLwFzgLZLeUnFeVzd6BzAo5/IUrZXaphNLg5O0PelT41TSzdLdgIdJ24ueADxDWjH2ttKCbCKuz77lP24HktaeOgf4NGknwo2A9+c5LERaRmSjXH5RaQG3kFZqmx4V1oC6jfjYjjQRagnwSeCjEXG/pLdExIKenmOrcn1WT9K7gTNIixt+CJgWEW/J3TPfBP5JmlH/XD5/teXbrXqt2jY9KqwBVYzEWQEsBnYGtgI+lBvaVGCGpH8HHssjQxq6oZXJ9VmTDYAvA9uS+vgPzuUrcvnmkfat7xqR5KQyAK3aNt0V1iAkjZd0akXRocB6EXEPacG5dtLaQNOB04FvRsSjzdDIyuD6rE7FDeM3SNqQtBPh+cBJwL4R8aDS2l/fApZFxJ3Q2COSGt1gaJtOLI1lP0ln5p+Hkz49EhHfJW2UtClp6OHnI+Kyihut1jPX5xrkT8zvAX4IbBMRvyHVzXrAcEkHAzOB30XE8yWG2mpaum36HksDqJjdvDXwfeAuYClpk6R2Ur/2Zvl7U1wKl8n1WT3l3QiBT0XE3yvKf0Cqo5HADyLiimbo2290g6Vt+h5LySoa2nuA95JGhfyEtP7PX0gNbARptdLDIuKh0oJtAq7Pmm0G3NeVVCQNj4jlEXF0Pl6/60qlWf/INYrB1DadWEqWG9qOpBE4P4iIhyR9mrQu04sR8VEASa9t5oa2trg++1bxx63r6uN+4EVJ7wCuj4jl+WbyjqQusH+VGW8rGUxt0/dYSiZpJHAIaaG5hwFyo/oiMCl3SQA0dUNbW1yfvatIKvsAX5H0dVL//iOkT81fyjfqfwTcEhEvhZdqKcxgapu+x1KC7n3VkjYFum7kfTby3tSStgE2q+z7ttW5PqsnaXfgZ8DXgMNJ+9LfQeqC2ZP0YfM3EXFZWTG2ksHaNp1Y1rKKT437A28GhkXEtyWNJQ3xfA3wpch7WljfXJ+1kTQDeC4ivqu0FtiJwKYR8X/z4+tFxL98o37gBnPbdFfYWpYb2oHAt4EbgE9IujgiFpNmNi8HZurVtZisD67P6kg6QNJBwD3AmySNi4hlEfEN0jpgXQsbLgPfqC/CYG6bTixrmdL6Sh8DPkwa/fEYsK2kufmTy9eBU2P1vcStB67PNZO0A3A0sAi4nfQHbW9JE3NCWZe0mq4TSoEGc9t0V9ha0EM/62bAaOB/SAvNBfAs8IeIOLjnV7Eurs++Kc2gHxYRSyVtQZo1v2lEHJQfPxTYBdiBdPP++3lipA2Q22bScpdgjShfEr+DtLz4wxExT9LGpE+P65D2Dv82cHWJYTYN12fvJL0B+C5wo6SLSJPv/gocKemIiDg3In4taS5pdv26kZZt8T2VArhtJu4KWwsk7QLMBnYFLpP0fuA+0qfF04HfAnMj4hqpuZZuKIPrs2dKq+P+D3ApcHZEtEfEUuAXpCVbdpX0MYCIeCoiFkXEg/nYSaUAbpuJu8LqpGJEyGbAJGBERFwl6QDgQtIe4n8D3kr6d7i+xHAbnuuzb7n763fAryLi3IryjwPPRMTvJH0EOACYE2mLYSuA2+bq3BVWJ7mh7Qd8jzR7+QZJN0XE5ZIOI10KfzAifl1qoE3C9blG/yLt5nhJV4Gkw0lL3Y+UND4ifpBHIN1cToityW1zde4KqxNJk0n7WXwGOIe038L7JW0YEZeT9rl4psQQm4rrs3e5S2UD0jIs76woWx94F6lb5iOSRgO/jIjby4q1Fbltrs5dYQWquCQeBfwDuCMi9pI0jLQj3/a5/JcR8Uzlc8qLunG5Pmsj6SjSRlE/iIibJQ2NiJcl7Uwa2vqJPIfCBshts2++YilIRUPbH/gA8B7gbZKOjoiXSMto3EuagbtR1/MGS0OrleuzX35LmqtypKS9SNX4LuBs4IdOKsVw21wzX7EUKH8yPBz4dUT8SdLbgKuAr+f+7SHAlhHRUWaczcL1Wbt8A/kDpG6Z20hbDJ8WEb8rNbAW47bZNyeWAuT+7GHAr4CdgNd1zaaV9Fbg76Q1gc7s/VWsi+tz4HKCeZk0T+XRwdQNU09um9VxYhmAikvidSPiRUlbAhcD7RFxeMV5bwNGR8TcsmJtBq5Pa1Rum7VxYhmgPMzwYNK2otcBj5NGhjwSEZ/pdq4/Na6B69Maldtm9XzzfgCU9rY4gzSz+ROkPtdFwKeA10s6r/L8wdzQquH6tEbltlkbT5Dsh9zPKtLQzuNI49aXA9/Nl8vPAR8FxpUXZfNwfVqjctvsHyeWfsifRkLSA8DJpIloB0bEY0orx26Zb94tKjPOZuH6tEblttk/7gqrUv7kgqQdJU1T2kr0QdIeC+cBT0naEfgGsLC0QJuE69MaldvmwPnmfQ3ypLOfANeTlr/+HrBF/noH8BJwRkRcOthv3lXD9WmNym1zYJxYqiTpjcAMUmO6QdL7gCnAxZGWwB4HvBwRj7uhrZnr0xqV2+bAuSusD12XxNk00o57uwNExP8CdwNflzQ8Ih6NiMfzY25oPXB9WqNy2yyWb973IY/62AmYFBHfkbQceK2kaRFxKfAXYG/STNzlZcbaDFyf1qjcNovlxNKH/ClmY+BYSSuBmcAXgS9JOoQ0xPCMiHi+xDCbhuvTGpXbZrGcWHohaVhEvCTpWtKS4ycARMR/SXoZeCPwi4j4fYlhNg3XpzUqt83i+R5LJmlLSdtKGppv3l0nadOIWE5avuG/gGPy2PUzSXst7Cxp3xLDbliuT2tUbpv158TCK6NA5gKTgfUi4h7S+PSLJY3JDe6vpHHsXyZNkvoVcA9paXKr4Pq0RuW2uXYM+uHGkiYAl5H6T8+VNCQiVubHzgDeDnwE2BI4krS3xT358VfOtcT1aY3KbXPtcWKRPgHsEBHHKW3O8xbSBKhHSI3wJNLQw38DvhwRs/PzPH69B65Pa1Rum2uPb97D/cCnlJbE/iCwHmm/6puB90TEUZLGAMMiYlFXI3ND65Xr0xqV2+Za4nsscBPwa+A7pP2pzwZ2A/4bGJEnRHVGxCLwhKgquD6tUbltriWDviusi6TREbG04nh34FTg0K6GZtVzfVqjctusP1+xZF0NTdIwSQeQJkh92w2tf1yf1qjcNuvPiaWCpGGkkSHHA1+LiD+UHFJTc31ao3LbrC93hXWTG9wm4ZVLC+H6tEbltlk/TixmZlYod4WZmVmhnFjMzKxQTixmZlYoJxYzMyuUE4uZmRXKicWsF5LGS7pU0kJJ90k6U9LwNTznxG7Hz63h/FGSPtvH4/8m6db8tVTSA/nnq2r7bczWHg83NutB3qr2RuBHEfEzSUOBc4ClEfHlPp73XERs0NtxD+dPAC6LiO2riOn8fO4lVf8iZiXwFYtZz/YClkXEzwAi4mXgC8AnJX1W0g+6TpR0maQ9JJ0GrJevKH7Z/QUlfVnSTZIWSJqRi08DXpef891aApR0gaQDK44vknSApE9J+l9JV0q6V9LXKs6ZLunv+f3OzsvHmxXKjcqsZ28G5lcWRMQzwMP0st1ERJwA/CsidoiIj1Q+JmkKMIm0jMgOwNsk7UbaX/2+/Jxer4R68VPgE/n1NwZ2Aq7Mj70dOAzYEfiwpB0kbQ+8D3hHROyQf4/DanxPszXyfixmPRPQUz9xb+VrMiV/3ZKPNyAlmof7FV3yJ+AsSZsAHwIujoiXUy8eV0bEkwCSfge8i/T/fSdgXj5nPdImV2aFcmIx69mdwPsrCyRtBGwFPM2qV/sjqng9kVbQndXtNSf0N8CIiNzl9mHg8Pz9lYe7n55jOC8ivt7f9zSrhrvCzHp2NTBS0scB8s3704HzSTsR7iBpiKStSN1OXV7Kixt2dyXp/swG+fXGSdoUeBbYcABx/gz4Mul+0L0V5VPyiLORwDTgr8BVwAfyLolI2kTS1gN4b7MeObGY9SCvdPs+4FBJC4F/AMuAE0l/pB8AbiftPnhzxVPPARZ0v3kfEXOAXwHXS7oduATYMCKWAH+VdEetN+/z6z6WY/tZt4f+kt/vFuCCiLg1Im4HZgBXSVoAzAE2q/U9zdbEw43Nmpik9UkJ7v9ExLO57FPA9hHx+VKDs0HLVyxmTUrSfsDdwBldScWsEfiKxaxBSPo34Bfdil+MiJ3LiMesv5xYzMysUO4KMzOzQjmxmJlZoZxYzMysUE4sZmZWqP8fLA1LnQI/d7UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = 'Outlet_Type', y = \"Item_Outlet_Sales\", data=train)\n",
    "plt.xticks(rotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation - 'Outlet_Location_Type' & Outlet Type\n",
    "1. Tier 3 has max sales\n",
    "2. Supermarket Type 03 has Max Sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium    1862\n",
       "Small     1592\n",
       "High       621\n",
       "Name: Outlet_Size, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Outlet_Size.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlet Size - Missing Values Filled\n",
    "train.Outlet_Size.fillna(value = \"Medium\", inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlet Size - Missing Values Filled\n",
    "test.Outlet_Size.fillna(value = \"Medium\", inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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  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      Medium               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Item Weight - Missing Values\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       17.5\n",
       "759     17.5\n",
       "4817    17.5\n",
       "5074    17.5\n",
       "6163    17.5\n",
       "6952     NaN\n",
       "8349     NaN\n",
       "Name: Item_Weight, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# FDA15\n",
    "train[train.Item_Identifier==\"FDN15\"][\"Item_Weight\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding how the ID will help impute Item Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = train[[\"Item_Identifier\", \"Item_Weight\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight\n",
       "0           FDA15         9.30\n",
       "1           DRC01         5.92\n",
       "2           FDN15        17.50\n",
       "3           FDX07        19.20\n",
       "4           NCD19         8.93"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       9.3\n",
       "831     9.3\n",
       "2599    9.3\n",
       "2643    9.3\n",
       "4874    9.3\n",
       "5413    9.3\n",
       "6696    NaN\n",
       "7543    9.3\n",
       "Name: Item_Weight, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Item_Identifier==\"FDA15\"][\"Item_Weight\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df[\"weight\"]=df.groupby(\"Item_Identifier\")[\"Item_Weight\"].transform(lambda x:x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Item_Weight\"]=train.groupby(\"Item_Identifier\")[\"Item_Weight\"].transform(lambda x:x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Item_Weight\"]=test.groupby(\"Item_Identifier\")[\"Item_Weight\"].transform(lambda x:x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              0\n",
       "Item_Weight                  4\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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "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>927</th>\n",
       "      <td>FDN52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.130933</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>86.9198</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>1569.9564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>FDK57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.079904</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>120.0440</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>4434.2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4187</th>\n",
       "      <td>FDE52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.029742</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>88.9514</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>3453.5046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>FDQ60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.191501</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>121.2098</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>120.5098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "927            FDN52          NaN          Regular         0.130933   \n",
       "1922           FDK57          NaN          Low Fat         0.079904   \n",
       "4187           FDE52          NaN          Regular         0.029742   \n",
       "5022           FDQ60          NaN          Regular         0.191501   \n",
       "\n",
       "         Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "927   Frozen Foods   86.9198            OUT027                       1985   \n",
       "1922   Snack Foods  120.0440            OUT027                       1985   \n",
       "4187         Dairy   88.9514            OUT027                       1985   \n",
       "5022  Baking Goods  121.2098            OUT019                       1985   \n",
       "\n",
       "     Outlet_Size Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "927       Medium               Tier 3  Supermarket Type3          1569.9564  \n",
       "1922      Medium               Tier 3  Supermarket Type3          4434.2280  \n",
       "4187      Medium               Tier 3  Supermarket Type3          3453.5046  \n",
       "5022       Small               Tier 1      Grocery Store           120.5098  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 04 Missing Values Left\n",
    "train[pd.isnull(train.Item_Weight)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    647.000000\n",
       "mean      12.285317\n",
       "std        4.749789\n",
       "min        4.880000\n",
       "25%        8.235000\n",
       "50%       11.650000\n",
       "75%       15.700000\n",
       "max       20.850000\n",
       "Name: Item_Weight, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frozen Food\n",
    "train[train.Item_Type==\"Baking Goods\"][\"Item_Weight\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputting Median\n",
    "#train[train.Item_Identifier==\"FDN52\"].loc(\"Item_Weight\").fillna(12.85, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputting Median using Iloc\n",
    "train.iloc[927,1]=12.85 # Frozen Food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.iloc[1922, 1] = 13.150000 # Snacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.iloc[4187,1] = 13.350000 # Dairy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.iloc[5022, 1] = 11.650000 # Baking Goods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               0\n",
       "Item_Weight                  20\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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>FDL58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.129825</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>263.2568</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>FDY57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.120667</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>94.6752</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>FDH58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.036761</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>116.9834</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>FDI45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.037399</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>173.1054</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>FDG50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015198</td>\n",
       "      <td>Canned</td>\n",
       "      <td>89.7146</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>FDG57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.071948</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>48.8034</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>FDJ09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.058112</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>45.5744</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>FDF22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.056555</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>214.2218</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1741</th>\n",
       "      <td>FDI45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.065800</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>177.0054</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>FDF22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099503</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>212.5218</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2600</th>\n",
       "      <td>FDG50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.026739</td>\n",
       "      <td>Canned</td>\n",
       "      <td>89.6146</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3619</th>\n",
       "      <td>FDL58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.073790</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>265.0568</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3734</th>\n",
       "      <td>DRN47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.029461</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>181.1660</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3875</th>\n",
       "      <td>NCJ30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.141191</td>\n",
       "      <td>Household</td>\n",
       "      <td>167.9790</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3909</th>\n",
       "      <td>FDT21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.035703</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>248.0092</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4298</th>\n",
       "      <td>FDO22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.017773</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>80.2960</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4496</th>\n",
       "      <td>FDG09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.083930</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>188.9556</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5346</th>\n",
       "      <td>FDF05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.047047</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>261.6910</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5609</th>\n",
       "      <td>FDP28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.080250</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>259.4936</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5615</th>\n",
       "      <td>FDF04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.013571</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>258.5304</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "58             FDL58          NaN          Regular         0.129825   \n",
       "482            FDY57          NaN          Regular         0.120667   \n",
       "882            FDH58          NaN          Low Fat         0.036761   \n",
       "884            FDI45          NaN          Low Fat         0.037399   \n",
       "1203           FDG50          NaN          Low Fat         0.015198   \n",
       "1297           FDG57          NaN          Low Fat         0.071948   \n",
       "1300           FDJ09          NaN          Low Fat         0.058112   \n",
       "1571           FDF22          NaN          Low Fat         0.056555   \n",
       "1741           FDI45          NaN          Low Fat         0.065800   \n",
       "2302           FDF22          NaN          Low Fat         0.099503   \n",
       "2600           FDG50          NaN          Low Fat         0.026739   \n",
       "3619           FDL58          NaN          Regular         0.073790   \n",
       "3734           DRN47          NaN          Low Fat         0.029461   \n",
       "3875           NCJ30          NaN          Low Fat         0.141191   \n",
       "3909           FDT21          NaN          Low Fat         0.035703   \n",
       "4298           FDO22          NaN          Regular         0.017773   \n",
       "4496           FDG09          NaN          Regular         0.083930   \n",
       "5346           FDF05          NaN          Low Fat         0.047047   \n",
       "5609           FDP28          NaN          Regular         0.080250   \n",
       "5615           FDF04          NaN          Low Fat         0.013571   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "58              Snack Foods  263.2568            OUT019   \n",
       "482             Snack Foods   94.6752            OUT027   \n",
       "882             Snack Foods  116.9834            OUT027   \n",
       "884   Fruits and Vegetables  173.1054            OUT027   \n",
       "1203                 Canned   89.7146            OUT027   \n",
       "1297  Fruits and Vegetables   48.8034            OUT027   \n",
       "1300            Snack Foods   45.5744            OUT027   \n",
       "1571            Snack Foods  214.2218            OUT027   \n",
       "1741  Fruits and Vegetables  177.0054            OUT019   \n",
       "2302            Snack Foods  212.5218            OUT019   \n",
       "2600                 Canned   89.6146            OUT019   \n",
       "3619            Snack Foods  265.0568            OUT027   \n",
       "3734            Hard Drinks  181.1660            OUT019   \n",
       "3875              Household  167.9790            OUT019   \n",
       "3909            Snack Foods  248.0092            OUT019   \n",
       "4298            Snack Foods   80.2960            OUT027   \n",
       "4496  Fruits and Vegetables  188.9556            OUT019   \n",
       "5346           Frozen Foods  261.6910            OUT019   \n",
       "5609           Frozen Foods  259.4936            OUT027   \n",
       "5615           Frozen Foods  258.5304            OUT027   \n",
       "\n",
       "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "58                         1985       Small               Tier 1   \n",
       "482                        1985      Medium               Tier 3   \n",
       "882                        1985      Medium               Tier 3   \n",
       "884                        1985      Medium               Tier 3   \n",
       "1203                       1985      Medium               Tier 3   \n",
       "1297                       1985      Medium               Tier 3   \n",
       "1300                       1985      Medium               Tier 3   \n",
       "1571                       1985      Medium               Tier 3   \n",
       "1741                       1985       Small               Tier 1   \n",
       "2302                       1985       Small               Tier 1   \n",
       "2600                       1985       Small               Tier 1   \n",
       "3619                       1985      Medium               Tier 3   \n",
       "3734                       1985       Small               Tier 1   \n",
       "3875                       1985       Small               Tier 1   \n",
       "3909                       1985       Small               Tier 1   \n",
       "4298                       1985      Medium               Tier 3   \n",
       "4496                       1985       Small               Tier 1   \n",
       "5346                       1985       Small               Tier 1   \n",
       "5609                       1985      Medium               Tier 3   \n",
       "5615                       1985      Medium               Tier 3   \n",
       "\n",
       "            Outlet_Type  \n",
       "58        Grocery Store  \n",
       "482   Supermarket Type3  \n",
       "882   Supermarket Type3  \n",
       "884   Supermarket Type3  \n",
       "1203  Supermarket Type3  \n",
       "1297  Supermarket Type3  \n",
       "1300  Supermarket Type3  \n",
       "1571  Supermarket Type3  \n",
       "1741      Grocery Store  \n",
       "2302      Grocery Store  \n",
       "2600      Grocery Store  \n",
       "3619  Supermarket Type3  \n",
       "3734      Grocery Store  \n",
       "3875      Grocery Store  \n",
       "3909      Grocery Store  \n",
       "4298  Supermarket Type3  \n",
       "4496      Grocery Store  \n",
       "5346      Grocery Store  \n",
       "5609  Supermarket Type3  \n",
       "5615  Supermarket Type3  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[pd.isnull(test.Item_Weight)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputting Missing Value in Test Basis item type\n",
    "test[\"Item_Weight\"] = test.groupby(\"Item_Type\")[\"Item_Weight\"].transform(lambda x:x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "\n",
    "Hints\n",
    "\n",
    "1. Item ID\n",
    "\n",
    "2. Price Per Unit\n",
    "\n",
    "3. Item Type\n",
    "\n",
    "4. Establishment Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FD'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Item ID\n",
    "train.Item_Identifier[0][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "itemid=[]\n",
    "for i in range(0, len(train.Item_Identifier)):\n",
    "    itemid.append(train.Item_Identifier[i][:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creatng a New Variable called Item Category\n",
    "train[\"Item_Category\"]=itemid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>FD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>DR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>FD</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",
       "      <td>FD</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",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998      Medium               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales Item_Category  \n",
       "0  Supermarket Type1          3735.1380            FD  \n",
       "1  Supermarket Type2           443.4228            DR  \n",
       "2  Supermarket Type1          2097.2700            FD  \n",
       "3      Grocery Store           732.3800            FD  \n",
       "4  Supermarket Type1           994.7052            NC  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "itemid=[]\n",
    "for i in range(0, len(test.Item_Identifier)):\n",
    "    itemid.append(test.Item_Identifier[i][:2])\n",
    "# Creatng a New Variable called Item Category\n",
    "test[\"Item_Category\"]=itemid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Year of Establishment\n",
    "train[\"Existence_Years\"] = 2013-train.Outlet_Establishment_Year\n",
    "test[\"Existence_Years\"] = 2013-test.Outlet_Establishment_Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5681.000000\n",
       "mean        0.065684\n",
       "std         0.051252\n",
       "min         0.000000\n",
       "25%         0.027047\n",
       "50%         0.054154\n",
       "75%         0.093463\n",
       "max         0.323637\n",
       "Name: Item_Visibility, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing 0s with Median\n",
    "test.Item_Visibility.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the Item Visibility 0s with Median Values\n",
    "train[\"Item_Visibility\"].replace(to_replace=0, value = 0.053931, \n",
    "                                 inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Item_Visibility\"].replace(to_replace=0, value =  0.054154, \n",
    "                                 inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12548a550>"
      ]
     },
     "execution_count": 81,
     "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": [
    "plt.scatter(train.Item_Visibility, train.Item_Outlet_Sales, color=\"magenta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Category</th>\n",
       "      <th>Existence_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>DR</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.053931</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",
       "      <td>FD</td>\n",
       "      <td>15</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>0.053931</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>NC</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.053931   \n",
       "4           NCD19         8.93          Low Fat         0.053931   \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      Medium               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales Item_Category  Existence_Years  \n",
       "0  Supermarket Type1          3735.1380            FD               14  \n",
       "1  Supermarket Type2           443.4228            DR                4  \n",
       "2  Supermarket Type1          2097.2700            FD               14  \n",
       "3      Grocery Store           732.3800            FD               15  \n",
       "4  Supermarket Type1           994.7052            NC               26  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Item_Weight\"]=train.Item_Weight.astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Item_Weight\"]=test.Item_Weight.astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5681 entries, 0 to 5680\n",
      "Data columns (total 13 columns):\n",
      "Item_Identifier              5681 non-null object\n",
      "Item_Weight                  5681 non-null float64\n",
      "Item_Fat_Content             5681 non-null object\n",
      "Item_Visibility              5681 non-null float64\n",
      "Item_Type                    5681 non-null object\n",
      "Item_MRP                     5681 non-null float64\n",
      "Outlet_Identifier            5681 non-null object\n",
      "Outlet_Establishment_Year    5681 non-null int64\n",
      "Outlet_Size                  5681 non-null object\n",
      "Outlet_Location_Type         5681 non-null object\n",
      "Outlet_Type                  5681 non-null object\n",
      "Item_Category                5681 non-null object\n",
      "Existence_Years              5681 non-null int64\n",
      "dtypes: float64(3), int64(2), object(8)\n",
      "memory usage: 577.1+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Price Per Unit\n",
    "train[\"Price_Per_Unit\"] = train.Item_MRP/train.Item_Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Price_Per_Unit\"] = test.Item_MRP/test.Item_Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 15)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681, 14)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1254843c8>"
      ]
     },
     "execution_count": 90,
     "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": [
    "plt.scatter(train.Price_Per_Unit, train.Item_Outlet_Sales, color=\"magenta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables',\n",
       "       'Household', 'Baking Goods', 'Snack Foods', 'Frozen Foods',\n",
       "       'Breakfast', 'Health and Hygiene', 'Hard Drinks', 'Canned',\n",
       "       'Breads', 'Starchy Foods', 'Others', 'Seafood'], dtype=object)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Item Type\n",
    "train.Item_Type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perishables\n",
    "perishables =['Dairy', 'Meat', 'Fruits and Vegetables','Frozen Foods',\n",
    "       'Breakfast','Breads','Seafood']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# where it matches the list of perishables, \"Perishables\" else \"Non Perishables\"\n",
    "train[\"ItemType_Cat\"] = np.where(train.Item_Type.isin(perishables), \"Perishables\", \"Non Perishables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"ItemType_Cat\"] = np.where(test.Item_Type.isin(perishables), \"Perishables\", \"Non Perishables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Category</th>\n",
       "      <th>Existence_Years</th>\n",
       "      <th>Price_Per_Unit</th>\n",
       "      <th>ItemType_Cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "      <td>26.861204</td>\n",
       "      <td>Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>DR</td>\n",
       "      <td>4</td>\n",
       "      <td>8.153581</td>\n",
       "      <td>Non Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "      <td>8.092457</td>\n",
       "      <td>Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.053931</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",
       "      <td>FD</td>\n",
       "      <td>15</td>\n",
       "      <td>9.484115</td>\n",
       "      <td>Perishables</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>0.053931</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>NC</td>\n",
       "      <td>26</td>\n",
       "      <td>6.031512</td>\n",
       "      <td>Non Perishables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.053931   \n",
       "4           NCD19         8.93          Low Fat         0.053931   \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      Medium               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales Item_Category  Existence_Years  \\\n",
       "0  Supermarket Type1          3735.1380            FD               14   \n",
       "1  Supermarket Type2           443.4228            DR                4   \n",
       "2  Supermarket Type1          2097.2700            FD               14   \n",
       "3      Grocery Store           732.3800            FD               15   \n",
       "4  Supermarket Type1           994.7052            NC               26   \n",
       "\n",
       "   Price_Per_Unit     ItemType_Cat  \n",
       "0       26.861204      Perishables  \n",
       "1        8.153581  Non Perishables  \n",
       "2        8.092457      Perishables  \n",
       "3        9.484115      Perishables  \n",
       "4        6.031512  Non Perishables  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building Stage\n",
    "\n",
    "Data Pre-Processing which includes SS starts from here..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Item ID, Outlet IDentifier, Outyear\n",
    "newtrain = train.drop([\"Item_Identifier\",\"Outlet_Identifier\", \"Outlet_Establishment_Year\", \"Item_Type\"],\n",
    "          axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest = test.drop([\"Item_Identifier\",\"Outlet_Identifier\", \"Outlet_Establishment_Year\", \"Item_Type\"],\n",
    "          axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 12)\n",
      "(5681, 11)\n"
     ]
    }
   ],
   "source": [
    "print(newtrain.shape)\n",
    "print(newtest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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_MRP</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Category</th>\n",
       "      <th>Existence_Years</th>\n",
       "      <th>Price_Per_Unit</th>\n",
       "      <th>ItemType_Cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "      <td>26.861204</td>\n",
       "      <td>Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>DR</td>\n",
       "      <td>4</td>\n",
       "      <td>8.153581</td>\n",
       "      <td>Non Perishables</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>141.6180</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>FD</td>\n",
       "      <td>14</td>\n",
       "      <td>8.092457</td>\n",
       "      <td>Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>FD</td>\n",
       "      <td>15</td>\n",
       "      <td>9.484115</td>\n",
       "      <td>Perishables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>NC</td>\n",
       "      <td>26</td>\n",
       "      <td>6.031512</td>\n",
       "      <td>Non Perishables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight Item_Fat_Content  Item_Visibility  Item_MRP Outlet_Size  \\\n",
       "0         9.30          Low Fat         0.016047  249.8092      Medium   \n",
       "1         5.92          Regular         0.019278   48.2692      Medium   \n",
       "2        17.50          Low Fat         0.016760  141.6180      Medium   \n",
       "3        19.20          Regular         0.053931  182.0950      Medium   \n",
       "4         8.93          Low Fat         0.053931   53.8614        High   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales Item_Category  \\\n",
       "0               Tier 1  Supermarket Type1          3735.1380            FD   \n",
       "1               Tier 3  Supermarket Type2           443.4228            DR   \n",
       "2               Tier 1  Supermarket Type1          2097.2700            FD   \n",
       "3               Tier 3      Grocery Store           732.3800            FD   \n",
       "4               Tier 3  Supermarket Type1           994.7052            NC   \n",
       "\n",
       "   Existence_Years  Price_Per_Unit     ItemType_Cat  \n",
       "0               14       26.861204      Perishables  \n",
       "1                4        8.153581  Non Perishables  \n",
       "2               14        8.092457      Perishables  \n",
       "3               15        9.484115      Perishables  \n",
       "4               26        6.031512  Non Perishables  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Encoding\n",
    "\n",
    "Label or OHE\n",
    "\n",
    "Label encoding is applied where the data or values follow a certain order where we can say that one value is greater than the other for eg shirt size. S>M>L. However, where we see that there is no order and hence it is imperative to apply OHE(pd.get_dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_train = pd.get_dummies(newtrain)\n",
    "dummy_test = pd.get_dummies(newtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Item_Outlet_Sales</th>\n",
       "      <th>Existence_Years</th>\n",
       "      <th>Price_Per_Unit</th>\n",
       "      <th>Item_Fat_Content_Low Fat</th>\n",
       "      <th>Item_Fat_Content_Regular</th>\n",
       "      <th>Outlet_Size_High</th>\n",
       "      <th>Outlet_Size_Medium</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Location_Type_Tier 3</th>\n",
       "      <th>Outlet_Type_Grocery Store</th>\n",
       "      <th>Outlet_Type_Supermarket Type1</th>\n",
       "      <th>Outlet_Type_Supermarket Type2</th>\n",
       "      <th>Outlet_Type_Supermarket Type3</th>\n",
       "      <th>Item_Category_DR</th>\n",
       "      <th>Item_Category_FD</th>\n",
       "      <th>Item_Category_NC</th>\n",
       "      <th>ItemType_Cat_Non Perishables</th>\n",
       "      <th>ItemType_Cat_Perishables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>14</td>\n",
       "      <td>26.861204</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>4</td>\n",
       "      <td>8.153581</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>14</td>\n",
       "      <td>8.092457</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>15</td>\n",
       "      <td>9.484115</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>26</td>\n",
       "      <td>6.031512</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight  Item_Visibility  Item_MRP  Item_Outlet_Sales  Existence_Years  \\\n",
       "0         9.30         0.016047  249.8092          3735.1380               14   \n",
       "1         5.92         0.019278   48.2692           443.4228                4   \n",
       "2        17.50         0.016760  141.6180          2097.2700               14   \n",
       "3        19.20         0.053931  182.0950           732.3800               15   \n",
       "4         8.93         0.053931   53.8614           994.7052               26   \n",
       "\n",
       "   Price_Per_Unit  Item_Fat_Content_Low Fat  Item_Fat_Content_Regular  \\\n",
       "0       26.861204                         1                         0   \n",
       "1        8.153581                         0                         1   \n",
       "2        8.092457                         1                         0   \n",
       "3        9.484115                         0                         1   \n",
       "4        6.031512                         1                         0   \n",
       "\n",
       "   Outlet_Size_High  Outlet_Size_Medium  ...  Outlet_Location_Type_Tier 3  \\\n",
       "0                 0                   1  ...                            0   \n",
       "1                 0                   1  ...                            1   \n",
       "2                 0                   1  ...                            0   \n",
       "3                 0                   1  ...                            1   \n",
       "4                 1                   0  ...                            1   \n",
       "\n",
       "   Outlet_Type_Grocery Store  Outlet_Type_Supermarket Type1  \\\n",
       "0                          0                              1   \n",
       "1                          0                              0   \n",
       "2                          0                              1   \n",
       "3                          1                              0   \n",
       "4                          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",
       "   Item_Category_DR  Item_Category_FD  Item_Category_NC  \\\n",
       "0                 0                 1                 0   \n",
       "1                 1                 0                 0   \n",
       "2                 0                 1                 0   \n",
       "3                 0                 1                 0   \n",
       "4                 0                 0                 1   \n",
       "\n",
       "   ItemType_Cat_Non Perishables  ItemType_Cat_Perishables  \n",
       "0                             0                         1  \n",
       "1                             1                         0  \n",
       "2                             0                         1  \n",
       "3                             0                         1  \n",
       "4                             1                         0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying Standard Scaler\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc= StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "scaled_train = pd.DataFrame(sc.fit_transform(dummy_train), \n",
    "             columns=dummy_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "scaled_test = pd.DataFrame(sc.fit_transform(dummy_test), \n",
    "             columns=dummy_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = scaled_train.drop(\"Item_Outlet_Sales\", axis = 1)\n",
    "y = scaled_train.Item_Outlet_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the Model\n",
    "rf.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict using Test Set\n",
    "pred = rf.predict(scaled_test) # Scaled Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversion of Scaled Value into Original Values...\n",
    "e^x and Log(e)\n",
    "\n",
    "1. This will be achieved using Standard Scaler...\n",
    "\n",
    "2. U will have to run the standard scaler of the column which you wish to transform and then apply Inverse Transform to bring it back into the org values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply Standard Scaler\n",
    "mysales = sc.fit_transform(pd.DataFrame(train.Item_Outlet_Sales))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse Transform\n",
    "sales = sc.inverse_transform(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Data Frame for Submission into CSV\n",
    "solution = pd.DataFrame({\"Item_Identifier\":test.Item_Identifier,\n",
    "             \"Outlet_Identifier\":test.Outlet_Identifier,\n",
    "             \"Item_Outlet_Sales\":sales})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting the solution in csv\n",
    "solution.to_csv(\"Random Forest Model.csv\", index=False) # 1254.26374553713"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mumbai-Admin\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision tree Model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dtree = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_tree = dtree.fit(x,y).predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse Transform\n",
    "sales = sc.inverse_transform(pred_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Data Frame for Submission into CSV\n",
    "solution = pd.DataFrame({\"Item_Identifier\":test.Item_Identifier,\n",
    "             \"Outlet_Identifier\":test.Outlet_Identifier,\n",
    "             \"Item_Outlet_Sales\":sales})\n",
    "\n",
    "# Exporting the solution in csv\n",
    "solution.to_csv(\"Tree Model.csv\", index=False) # 1625.8461570796003."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adaboost\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "ada =AdaBoostRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_ada = ada.fit(x,y).predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse Transform\n",
    "sales = sc.inverse_transform(pred_ada)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Data Frame for Submission into CSV\n",
    "solution = pd.DataFrame({\"Item_Identifier\":test.Item_Identifier,\n",
    "             \"Outlet_Identifier\":test.Outlet_Identifier,\n",
    "             \"Item_Outlet_Sales\":sales})\n",
    "\n",
    "# Exporting the solution in csv\n",
    "solution.to_csv(\"ADA.csv\", index=False) # 1269"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Lasso and Ridge Regression\n",
    "\n",
    "##### What is Regularization?\n",
    "\n",
    "Regularization is a concept of selecting a model that is less overfitted and generalized enough to give realisitc values.Here, Realistic values mean that model is predicting values closer to the Actual Values.\n",
    "\n",
    "##### Why Base Model is a Problem?\n",
    "Normally the models have coefficients whose weights are assigned on the basis of feature importance meaning that if the Sales of the Store is highly dependent on the ITEM MRP & less dependent on Size of the Store so the coefficient value for MRP will be higher than the Size of the Store. This is called \"Weight assigned basis Feature Importance.\"\n",
    "\n",
    "So, machine will assign higher weights to some variables in comparison to others. Having said that, we need to balance out the weights or penalizing the variables that have higher weights. This is called Regularization.\n",
    "\n",
    "1. Ridge & Lasso Follow L1 and L2 Regularization Techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets Find out the Comparison of Linear Regression & Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "x1 = dummy_train.drop(\"Item_Outlet_Sales\", axis = 1)\n",
    "y1 = dummy_train.Item_Outlet_Sales \n",
    "xtrain,xtest, ytrain, ytest = train_test_split(x1, y1, test_size = 0.20, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lr2 = lr2.fit(xtrain,ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5669361206261654\n",
      "1113.4578598351472\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(ytest, pred_lr2))\n",
    "print(np.sqrt(mean_squared_error(ytest, pred_lr2))) # $1113"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(pd.Series({\"Name\":\"LR MODEL\", \"R_SQ\":r2_score(ytest, pred_lr2).round(3),\n",
    "                       \"RMSE\":np.sqrt(mean_squared_error(ytest, pred_lr2)).round(3)})).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr = Ridge(alpha=1)\n",
    "pred_r = rr.fit(xtrain,ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.append(pd.DataFrame(pd.Series({\"Name\":\"RIDGE\", \"R_SQ\":r2_score(ytest, pred_r).round(3),\n",
    "                       \"RMSE\":np.sqrt(mean_squared_error(ytest, pred_r)).round(3)})).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "l = Lasso(alpha = 5)\n",
    "pred_l = l.fit(xtrain,ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "scrolled": true
   },
   "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>Name</th>\n",
       "      <th>R_SQ</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR MODEL</td>\n",
       "      <td>0.567</td>\n",
       "      <td>1113.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RIDGE</td>\n",
       "      <td>0.567</td>\n",
       "      <td>1113.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LASSO</td>\n",
       "      <td>0.566</td>\n",
       "      <td>1114.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   R_SQ     RMSE\n",
       "0  LR MODEL  0.567  1113.46\n",
       "0     RIDGE  0.567  1113.51\n",
       "0     LASSO  0.566  1114.14"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(pd.DataFrame(pd.Series({\"Name\":\"LASSO\", \"R_SQ\":r2_score(ytest, pred_l).round(3),\n",
    "                       \"RMSE\":np.sqrt(mean_squared_error(ytest, pred_l)).round(3)})).T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets Try Directly on the Data itself...and Submit Predictions on AV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base Model - Linear Regression\n",
    "x = scaled_train.drop(\"Item_Outlet_Sales\", axis = 1)\n",
    "y = scaled_train.Item_Outlet_Sales\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "# Test Set  - scaled_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lr = lr.predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse Transform\n",
    "sales = sc.inverse_transform(pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Data Frame for Submission into CSV\n",
    "solution = pd.DataFrame({\"Item_Identifier\":test.Item_Identifier,\n",
    "             \"Outlet_Identifier\":test.Outlet_Identifier,\n",
    "             \"Item_Outlet_Sales\":sales})\n",
    "\n",
    "# Exporting the solution in csv\n",
    "solution.to_csv(\"LR.csv\", index=False) # 23542127817542"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mukul.singh\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding Magnitude of Coefficients\n",
    "predictors = x.columns\n",
    "lrcoef = lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.Series(lrcoef, predictors).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b52a5f8>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,4])\n",
    "data.plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: As we see on the above plot, some predictors have higher weightage in comparison to others meaning that ITEM FAT REGULAR has more weightage in comparsion to NON PERISHABLES. It means that the features that have high coefficient value is contributing more to the model in comparison to the rest. \n",
    "\n",
    "In Order to solve the problem of uneven weightage, we need to apply Regularization. Regularization adds some bias to control overfitting of the features within the model.\n",
    "\n",
    "### Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "r = Ridge(alpha=0.5, normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_ridge = r.fit(x,y).predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse Transform\n",
    "sales = sc.inverse_transform(pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Data Frame for Submission into CSV\n",
    "solution = pd.DataFrame({\"Item_Identifier\":test.Item_Identifier,\n",
    "             \"Outlet_Identifier\":test.Outlet_Identifier,\n",
    "             \"Item_Outlet_Sales\":sales})\n",
    "\n",
    "# Exporting the solution in csv\n",
    "solution.to_csv(\"RIDGE.csv\", index=False) # 1267.296058493745"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12bef1dd8>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,4])\n",
    "pd.Series(r.coef_, predictors).sort_values().plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ridge Regression: - \n",
    "1. Ridge Regression shrinks the model coefficients so that it becomes less complex and hence more generalized.\n",
    "\n",
    "2. Linear regression (in scikit-learn) is the most basic form, where the model is not penalized for its choice of weights, at all. That means, during the training stage, if the model feels like one particular feature is particularly important, the model may place a large weight to the feature. This sometimes leads to overfitting in small datasets and hence Lasso and Ridge.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso = Lasso(alpha = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lasso = lasso.fit(x,y).predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse Transform\n",
    "sales = sc.inverse_transform(pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Data Frame for Submission into CSV\n",
    "solution = pd.DataFrame({\"Item_Identifier\":test.Item_Identifier,\n",
    "             \"Outlet_Identifier\":test.Outlet_Identifier,\n",
    "             \"Item_Outlet_Sales\":sales})\n",
    "\n",
    "# Exporting the solution in csv\n",
    "solution.to_csv(\"LASSO.csv\", index=False) # 1713 at 0.5 | 1245.8173581418503 at 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "en = ElasticNet(alpha = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_en = en.fit(x,y).predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse Transform\n",
    "sales = sc.inverse_transform(pred_en)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Data Frame for Submission into CSV\n",
    "solution = pd.DataFrame({\"Item_Identifier\":test.Item_Identifier,\n",
    "             \"Outlet_Identifier\":test.Outlet_Identifier,\n",
    "             \"Item_Outlet_Sales\":sales})\n",
    "\n",
    "# Exporting the solution in csv\n",
    "solution.to_csv(\"ENET.csv\", index=False) # 1472 at 0.5 | 1224 - 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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