BigMart Sales Case Study - Final Script.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement: Sales is going down or predict the sales for me.\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 predict the sales of each product at a particular outlet.\n",
    "\n",
    "Using this model, BigMart will try to understand the properties of products and outlets 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. \n",
    "\n",
    "##### Step 01: Define the Hypothesis\n",
    "\n",
    "In the Last Quarter, our sales has dipped 20%. Find the reason.\n",
    "\n",
    "* Identify the FACTORS behind the Sales Dipping.\n",
    "* Customers not coming.\n",
    "* Veggies\n",
    "* Clothes\n",
    "* MacroEco Scenario - LOCKDOWN.....\n",
    "\n",
    "These factors which you think are resp for the sales going down, are called\n",
    "HYPOTHESIS.\n",
    "\n",
    "##### Step 02: Hypothesis are submitted to the Client.\n",
    "A follow up call is done in which the entire context is discussed and after that the client releases the data.\n",
    "\n",
    "##### Step 03: Check if the Data has all the Hypothesis (Columns)\n",
    "There will be some columns/features that you thought of and at the same time, there will be some features which will be missing. The brain stormed feature list will help me in feature engineering.\n",
    "\n",
    "##### Step 04: Exploratory Data Analysis\n",
    "\n",
    "1. Univariate Analysis - Single Variable Analysis\n",
    "\n",
    "In any data, we see two types of Features - Numerical and Categorical.\n",
    "\n",
    "* Numerical - Histogram | distplot.\n",
    "* Categorical - Countplot/Frequency Plot.\n",
    "\n",
    "Purpose: The purpose is to identify the features, shapes and \"try to establish\" a relation with the Target Variable.\n",
    "\n",
    "2. Bivariate/Multivariate Analysis - Predictor Vs Target Variable.\n",
    "\n",
    "Predictor(Num) vs Target (Num): Scatterplot | Linear Pattern, Uptrend, Downtrend.\n",
    "\n",
    "Predict(Num) vs Target (Cat): Barplot, Boxplot as well as Violinplot.\n",
    "\n",
    "Predict(Cat) vs Target (Cat): pd.crosstab() to create a pivot table and then we can plot the Countplot.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Train and Test Data\n",
    "train = pd.read_csv(\"~/Downloads/train-file.csv\")\n",
    "test = pd.read_csv(\"~/Downloads/test-file.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview the Data\n",
    "train.head()\n",
    "\n",
    "# Item Visibility has lots of 0s which needs fixing.\n",
    "# The Items are clubbed and a single entry has been created.\n",
    "# Store is spread out in several location\n",
    "# When the Outlets were created.\n",
    "# Outle Size has Missing Values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8523, 12), (5681, 11))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Dimensions of train & test\n",
    "train.shape, test.shape # test columns = train cols - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Weight    1463\n",
       "Outlet_Size    2410\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values\n",
    "train.isnull().sum()[train.isnull().sum()!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Weight     976\n",
       "Outlet_Size    1606\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values\n",
    "test.isnull().sum()[test.isnull().sum()!=0]\n",
    "\n",
    "# Train and test has similar missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120454710>"
      ]
     },
     "execution_count": 8,
     "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": [
    "# Target Variable\n",
    "sns.distplot(train.Item_Outlet_Sales, color = \"brown\")\n",
    "\n",
    "# Sales is skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Dist of Sales\n",
    "train.Item_Outlet_Sales.describe()\n",
    "\n",
    "# Mean>Median>Mode - Positively Skewed Data.\n",
    "# Mean< Median < Mode - Negatively Skewed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot the Item Weight\n",
    "sns.distplot(train.Item_Weight.dropna()) # seaborn is not compatible with missing values\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120d51400>"
      ]
     },
     "execution_count": 11,
     "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 Visibility\n",
    "sns.distplot(train.Item_Visibility, color = \"red\")\n",
    "\n",
    "# Visibility of products is skewed because bigger products will capture\n",
    "# more area in the store and will be very much visible in comparison to Veggies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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\n",
    "sns.distplot(train.Item_MRP, color = \"hotpink\")\n",
    "plt.show()\n",
    "\n",
    "# ITEM MRP has 04 different distributions. Need to Explore Further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8523.000000\n",
       "mean      140.992782\n",
       "std        62.275067\n",
       "min        31.290000\n",
       "25%        93.826500\n",
       "50%       143.012800\n",
       "75%       185.643700\n",
       "max       266.888400\n",
       "Name: Item_MRP, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_MRP.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SUMMARY - Univariate Analysis\n",
    "\n",
    "1. Target Variable is skewed in nature with the average sales of 2196 dollars and a sd of 1700 and min sales of 31 dollard and max sales of 14000 dollars.\n",
    "\n",
    "2. Similary, Item MRP shows 04 different price distributions with min MRP as 31 dollars and max MRP as 266 dollars.\n",
    "\n",
    "3. Item Weight has no pattern in Univ Plot\n",
    "\n",
    "4. Item Visibility is skewed in shape meaning some products have a higher visibility in comparison to the rest.\n",
    "\n",
    "### Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120e9c240>"
      ]
     },
     "execution_count": 14,
     "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 Type\n",
    "train.Item_Type.value_counts().plot(kind = \"bar\")\n",
    "\n",
    "# Top5 are Fruits and Veggies, Dairy, Household, Snack & Frozen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120eaab38>"
      ]
     },
     "execution_count": 15,
     "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",
    "test.Item_Fat_Content.value_counts().plot(kind = \"bar\")\n",
    "# LF, low fat ~ Low Fat\n",
    "# reg ~ Regular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Item Fat Content\n",
    "train.Item_Fat_Content.replace(to_replace = [\"LF\", \"low fat\", \"reg\"],\n",
    "                              value =[\"Low Fat\", \"Low Fat\", \"Regular\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.Item_Fat_Content.replace(to_replace = [\"LF\", \"low fat\", \"reg\"],\n",
    "                              value =[\"Low Fat\", \"Low Fat\", \"Regular\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12104bcf8>"
      ]
     },
     "execution_count": 18,
     "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": [
    "# Outlet ID\n",
    "train.Outlet_Identifier.value_counts().plot(kind = \"bar\")\n",
    "\n",
    "# OUTLET 27 is the Most Avilable Type of Outlet.\n",
    "# Less No of OUTLETS - OUTLET 10 & OUTLET19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12154d5f8>"
      ]
     },
     "execution_count": 19,
     "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": [
    "# Outlet Size\n",
    "train.Outlet_Size.value_counts().plot(kind = \"bar\")\n",
    "\n",
    "# Medium Size Outlets are the most visible outlets in the market"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Univariate Analysis\n",
    "\n",
    "1. Average Sales is 2196 dollars.\n",
    "\n",
    "2. Most of the Outlets are Medium Size Outlets\n",
    "\n",
    "3. The Most Visible Outlet is OUT27\n",
    "\n",
    "4. The products are sell the most are Fruits and Veggies, Frozen, Dairy, Household Items & Snacks.\n",
    "\n",
    "5. Most of the products that sell are Low Fat Content.\n",
    "\n",
    "6. Some Items have bigger visibility in comparison to rest. Keeping above points in mind, less visible items are selling the most.\n",
    "\n",
    "\n",
    "### Bivariate Analysis - Num Vs Num\n",
    " \n",
    "Analyising the Predictor vs Target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Visibility Vs Sales\n",
    "plt.figure(figsize = [10, 5])\n",
    "plt.scatter(train.Item_Visibility, train.Item_Outlet_Sales, color = \"red\")\n",
    "plt.xlabel(\"Item Visibility\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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, alpha = 0.3)\n",
    "plt.show()\n",
    "\n",
    "# Inference: There is no specific pattern of Sales with Item Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item MRP vs Sales\n",
    "plt.figure(figsize = [10,5])\n",
    "plt.scatter(train.Item_MRP, train.Item_Outlet_Sales, color = \"red\")\n",
    "plt.show()\n",
    "\n",
    "# ITEM MRP is not given for a single item. These Prices are Combined/\n",
    "# Total Prices of certain quantities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "# Bivariate - Cat Vs Numerical | Boxplot\n",
    "\n",
    "# Item Fat Content Vs Sales\n",
    "sns.boxplot(x = \"Item_Fat_Content\", y  = \"Item_Outlet_Sales\", data = train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Item_Fat_Content</th>\n",
       "      <th>Low Fat</th>\n",
       "      <th>Regular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5517.000000</td>\n",
       "      <td>3006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2157.711534</td>\n",
       "      <td>2224.561170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1697.973824</td>\n",
       "      <td>1721.480865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>33.290000</td>\n",
       "      <td>33.955800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>826.257800</td>\n",
       "      <td>857.550400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1765.035800</td>\n",
       "      <td>1844.598900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3050.695600</td>\n",
       "      <td>3198.669650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13086.964800</td>\n",
       "      <td>12117.560000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Item_Fat_Content       Low Fat       Regular\n",
       "count              5517.000000   3006.000000\n",
       "mean               2157.711534   2224.561170\n",
       "std                1697.973824   1721.480865\n",
       "min                  33.290000     33.955800\n",
       "25%                 826.257800    857.550400\n",
       "50%                1765.035800   1844.598900\n",
       "75%                3050.695600   3198.669650\n",
       "max               13086.964800  12117.560000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Item_Fat_Content\")[\"Item_Outlet_Sales\"].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Type Vs Sales\n",
    "plt.figure(figsize = [10,5])\n",
    "sns.boxplot(train.Item_Type, train.Item_Outlet_Sales)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inference:\n",
    "\n",
    "1. Seafood has a high MRP and hence the sales is high. It is competing with the regular items.\n",
    "2. Snack Foods, Dairy, Fruits and Veggies, Household Items are selling the most.\n",
    "\n",
    "Find out the List of all the Items for which the sales is above 8K dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Snack Foods', 'Dairy', 'Fruits and Vegetables', 'Frozen Foods',\n",
       "       'Starchy Foods', 'Breads', 'Health and Hygiene', 'Soft Drinks',\n",
       "       'Canned', 'Household', 'Breakfast', 'Meat'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train.Item_Outlet_Sales>8000, \"Item_Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Snack Foods', 'Dairy', 'Fruits and Vegetables', 'Frozen Foods',\n",
       "       'Starchy Foods', 'Breads', 'Health and Hygiene', 'Soft Drinks',\n",
       "       'Canned', 'Household', 'Breakfast', 'Meat'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Item_Outlet_Sales>8000][\"Item_Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlet Id vs Sales\n",
    "plt.figure(figsize = [10,5])\n",
    "sns.boxplot(x = \"Outlet_Identifier\", y =\"Item_Outlet_Sales\", data = train)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()\n",
    "\n",
    "# Out27 is the Star Outlet whereas Out10 and Out19 are the least performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Outlet Size vs Sales\n",
    "sns.boxplot(x = \"Outlet_Size\", y =\"Item_Outlet_Sales\", data = train)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()\n",
    "\n",
    "# Since Out27 is the best outlet and so the medium size outlets are better fitted.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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.boxplot(x = \"Outlet_Establishment_Year\", y =\"Item_Outlet_Sales\", data = train)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NhGpukLwGuEbS2yPi1sG2N7PaXXjhhXz84x9/uXzRRRflGI3Z8NQyK+xJSTdJug9A0qGSLqhTXGajyoEHHsiECROAUmtl1qxZOUdkNnS1JJbvAucDWwEiYjVpzS4zG74ZM2YwZswYt1as8GpJLJMjov/KwdsqbmkjznfeF9/kyZM59NBD3Vqxwqvlr0+PpANId9hLOpHS0x+tAbS0tPQpN+PDzMysGGq5j+VMSkvb/5GkR4EHgf9dl6isZv0ft/zEE0/kFImZjXZVJ5aIWAe8R9KuwJiI2Fy/sKxWXt3YzBpFNTdInruTegAi4msZx2RmZgVWTYtl9wHe88fiBiGpTyulN/GbmY20am6Q/CyApCMios9Ck5KOqFdgVpvx48fz0ksv9SmbmeWhlllh36iyznJQnlQqlc3MRko1YyxvB/4UmNZvvGUKMLZegZmZWTFVM8YyAdgtbVs+3vIscGI9gjIzs+KqZoxlBbBC0vcj4qGsTixpKvA94I2UJgF8DPgt8GNgBrAe+F8R8ZRKI9GXAO8FngdOi4g703Hagd41yz4fEZ1ZxWhmZrWr5QbJ70t61SywiHj3EM99CXB9RJwoaQIwGfg0cFNEXCzpPOA84FPAccDs9PVW4NvAWyXtCSwC2iglp1WSlkTEU0OMaUQU8UFR4IdFmVl1akks/6fs9UTggwxxrTBJU4B3AqcBRMRLwEuSFgBHpc06geWUEssC4Ioozae9TdJUSfukbZdFxKZ03GXAfOBHQ4mryKZNm9bn7nsv6WL2aiP1oQ5G9xNAa7nzflW/ql9KWjHE874B2AhcLumPgVXAJ4HXRsRj6XyPSdo7bT8deKRs/+5Ut7P6V5G0EFgIsP/++w8x7GzU6xek/Ln3P/nJT+pyDjOzwVSdWFK3U68xlJ51/7phnPcw4OyIuF3SJZS6vXZ6+gp1MUD9qysjFlNa64y2tramvLGzt9XS3t6edyhmDaleH+rOOussVq9e/XJ5zpw5o/oJoLXcx7Kq7OtW4G+A04d43m6gOyJuT+WrKSWax1MXF+n7E2Xb71e2fyuwYYD6UWn69OnMmTOH008f6mUxs6G48MIL+5QXLVqUTyANourEEhEzy75mR8S8iLhlKCeNiP8GHpF0UKo6BlgDLAF6P263A9ek10uAj6jkbcAzqcvsBmCepD0k7QHMS3VmZiOmpaWFSZMmAaXWyl577ZVzRPmqqissjXWcCRxCqatpDfDNiBjO2uxnAz9MM8LWAR+llOiuknQ68DBwUtr2OkpTjbsoTTf+KEBEbJL0OeCOtN1FvQP5ZmYj6Q1veAPr168f9a0VqO7O+yOAfwW+D1xBaVzjMODXkj7cf/2wakXE3ZSmCfd3TIVtg1Jiq3Scy4DLhhKDmVlWxo8fz+zZs0d9awWqa7F8FTghIu4qq7tG0k+B71C6r8TMzAyoboxlSr+kArzc4hhoSX0zMxuFqkksSgPj/Sv3rHJ/MzMbRapJDF8HbpT0Lkm7p6+jgKXpPTMzs5dVswjlYkkbgM/Rd1bY5yPiZ3WOz8zMCqaq6cYRcS1w7UDbSDo/Iv4hk6jMzKywshwjOWnwTczMrNllmVgqrdtlZmajTJaJpSkXdjQzs9q4xWJmZpnKMrH8W4bHMjOzgqrleSwzKS0cOaN8v4g4Pn3/YtbBmZlZ8dTyaOL/AC4FfgbsqE84ZmZWdLUklhcjYvQ+Es3MzKpSS2K5RNIi4EZgS29lRNyZeVRmZlZYtSSWNwGnAu/mla6wSGUzMzOgtsTyAeANEfFSvYIxM7Piq2W68T3A1HoFYmZmzaGWFstrgd9IuoO+YyzHZx6VmZkVVi2JZVHdojAzs6ZRdWKJiBWSXg/MjoifS5oMjK1faGZmVkRVj7FI+gRwNfCdVDWd0k2TQyZprKS7JF2byjMl3S5praQfS5qQ6ndJ5a70/oyyY5yf6n8r6djhxGNmZsNXy+D9mcARwLMAEbEW2HuY5/8k8EBZ+UvA1yNiNvAUcHqqPx14KiJmUXoc8pcAJB0MnEzpyZbzgW9JcivKzCxHtSSWLeVTjSWNYxhL5UtqBf4n8L1UFqV7Yq5Om3QCJ6TXC1KZ9P4xafsFwJURsSUiHgS6gMOHGpOZmQ1fLYllhaRPA5MkzaW0mvFwnnn/T8Df8crNlnsBT0fEtlTuptTdRvr+CEB6/5m0/cv1FfbpQ9JCSSslrdy4ceMwwjYzs4HUkljOAzYC9wJnANdFxGeGclJJ7wOeiIhV5dUVNo1B3hton76VEYsjoi0i2qZNm1ZTvGZmVr1aphufHRGXAN/trZD0yVRXqyOA4yW9F5gITKHUgpkqaVxqlbQCG9L23cB+QHfqgnsNsKmsvlf5PmZmloNaWiztFepOG8pJI+L8iGiNiBmUBt//MyI+DPwCOLHsfNek10vKzn9i2j5S/clp1thMYDbw66HEZGZm2Ri0xSLpFOBDwExJS8re2h14MuN4PgVcKenzwF2Unv9C+v4DSV2UWionA0TE/ZKuAtYA24AzI2J7xjGZmVkNqukK+xXwGNACfLWsfjOwergBRMRyYHl6vY4Ks7oi4kXgpJ3s/wXgC8ONw8zMsjFoYomIh4CHgLfXPxwzMyu6arrCNlN5ppWAiIgpmUdlZmaFVU2LZfeRCMTMzJpDLbPCzMzMBlXLfSxmhdHR0UFXV1feYdRk7dq1AJxzzjk5R1KbWbNmFS5mqy8nFmtKXV1d/O6+O9l/t+LMPp+wtdSB8OL6O3KOpHoPP+c1X+3VnFisae2/23YuaHsu7zCa2udX7pZ3CNaAnFjMrOG4K3Nk1Ksb04nFzBpOV1cXd91/F0zNO5IapHXa73r0rnzjqNbT9Tu0E4uZNaapsOOoHYNvZ0MyZnn9JgV7urGZmWXKLZYBFK2ft4h9vODpqmbNxollAF1dXdx17xp2TN4z71CqopdKK++s+v1/5xxJ9cY8vynvEMwsY04sg9gxeU9ePPh9eYfRtCauuTbvEMwsYx5jMTOzTDmxmJlZppxYzMwsU04sZmaWKScWMzPLlBOLmZllyonFzMwylUtikbSfpF9IekDS/ZI+mer3lLRM0tr0fY9UL0kdkrokrZZ0WNmx2tP2ayW15/HzmJnZK/JqsWwD/iYi/gfwNuBMSQcD5wE3RcRs4KZUBjgOmJ2+FgLfhlIiAhYBbwUOBxb1JiMzM8tHLoklIh6LiDvT683AA8B0YAHQmTbrBE5IrxcAV0TJbcBUSfsAxwLLImJTRDwFLAPmj+CPYmZm/eS+pIukGcCfALcDr42Ix6CUfCTtnTabDjxStlt3qttZvZkVWHd3NzxT36XdR72noTu663LoXK+apN2AnwB/FRHPDrRphboYoL7SuRZKWilp5caNG2sP1szMqpJbi0XSeEpJ5YcR8e+p+nFJ+6TWyj7AE6m+G9ivbPdWYEOqP6pf/fJK54uIxcBigLa2torJx5pHd3c3f9g81s9kr7OHNo9l1+7sP/W2trayURv9oK86GrN8DK3TW+tz7LocdRCSBFwKPBARXyt7awnQO7OrHbimrP4jaXbY24BnUpfZDcA8SXukQft5qc7MzHKSV4vlCOBU4F5Jd6e6TwMXA1dJOh14GDgpvXcd8F6gC3ge+ChARGyS9DngjrTdRRHhB3wYra2tvLjtMS5oey7vUJra51fuxsTW+nzqteLKJbFExC1UHh8BOKbC9gGcuZNjXQZcll10ZmY2HJ5yYWZmmcp9unEj6+7uZszzz/gph3U05vkn6e7elncYZpYht1jMzCxTbrEMoLW1lce3jPMz7+to4ppraW19Xd5hmFmG3GIxM7NMObGYmVmm3BVmZo3p6YKtFdZ7y1RRFnt4mrqtrOjEYmYNZ9asWXmHULO1a9cCMHv67JwjqdL0+v07O7GYWcM555xz8g6hZr0xd3R05BxJ/grUzjQzsyJwYjEzs0w5sZiZWaacWMzMLFMevLem9fBzxXrQ1+PPlz7nvXZycR5u9fBzYzkw7yCs4TixWFMq4nTVl9J01YkzCjJdFTiQYv5bW305sQxizPObCrO6sV58FoCYOCXnSKo35vlNQPZrhXm6qll+nFgGULRPYmvXbgZg9gFFWtTxdYX7dzazgTmxDKBon3r9idfMGoFnhZmZWaacWMzMLFNOLGZmliknFjMzy1RTDN5Lmg9cAowFvhcRF+cckpk1oI6ODrq6uupy7N5l8+sx6WfWrFmFmkxU+MQiaSzwTWAu0A3cIWlJRKzJN7Kdq9cvdz1/saF4v9z1UsTr52tXf5MmTco7hIZR+MQCHA50RcQ6AElXAguAhk0s9eJf7GLz9as/J9eRoYjIO4ZhkXQiMD8iPp7KpwJvjYiz+m23EFgIsP/++7/5oYceGvFYzcyKStKqiGirZttmGLxXhbpXZcuIWBwRbRHRNm3atBEIy8xsdGqGxNIN7FdWbgU25BSLmdmo1wyJ5Q5gtqSZkiYAJwNLco7JzGzUKvzgfURsk3QWcAOl6caXRcT9OYdlZjZqFT6xAETEdcB1ecdhZmbN0RVmZmYNxInFzMwy5cRiZmaZKvwNkkMhaSPQrHdItgA9eQdhQ+brV2zNfP1eHxFV3QQ4KhNLM5O0stq7Y63x+PoVm69fibvCzMwsU04sZmaWKSeW5rM47wBsWHz9is3XD4+xmJlZxtxiMTOzTDmxmJlZppxYzMwsU04sZmaWKSeWJiTJM1ManKTJkv5O0t9KmijpNElLJH1Z0m55x2cDkzRW0hmSPifpiH7vXZBXXI3Cs8IKStKeO3sLuCciWkcyHquNpKuAR4BJwEHAA8BVwPuB10XEqTmGZ4OQ9D1gMvBr4FRgRUScm967MyIOyzO+vDmxFJSk7ZTWO1NZdaTy9IiYkEtgVhVJd0fEHEkCHgP2iYhI5Xsi4tCcQ7QBSFrde40kjQO+RWmdsFOA2yLiT/KML29N8aCvUWodcExEPNz/DUmP5BCPDUFKJtdF+oSXyv601/he/uAWEduAhZL+HvhPYNR3ZXqMpbj+CdhjJ+99eSQDsSFZ2TuWEhEf662UdACwObeorForJc0vr4iIi4DLgRm5RNRA3BVm1mAkKfwf0wrMicXMzDLlrjAzM8uUE0uBqWS/vOOwofH1KzZfv51zYimw1A//H3nHYUPj61dsvn4758RSfLdJekveQdiQ+foVm69fBR68LzhJayjdub0e+AOlGyTDN9gVg69fsfn6VebEUnCSXl+pPiIeGulYrHa+fsXm61eZu8IKLv0C7we8O71+Hl/XwvD1KzZfv8rcYik4SYuANuCgiDhQ0r7Av0XEEYPsag3A16/YfP0qG/WZtQl8ADieUv8uEbEB2D3XiKwWvn7F5utXgRNL8b2Upj0GgKRdc47HauPrV2y+fhU4sRTfVZK+A0yV9Ang58B3c47JqufrV2y+fhV4jKUJSJoLzKM01fGGiFiWc0hWA1+/YvP1ezUnFjMzy5Qf9FVQkm6JiCMlbSb17/a+RekGrSk5hWZV8PUrNl+/gTmxFNeuABEx6megFJSvX7H5+g3Ag/fF5T7MYvP1KzZfvwG4xVJce0s6d2dvRsTXRjIYq5mvX7H5+g3AiaW4xgK7UerTteLx9Ss2X78BeFZYQUm6MyIOyzsOGxpfv2Lz9RuYx1iKy5+Uis3Xr9h8/QbgFktBSdozIjblHYcNja9fsfn6DcyJxczMMuWuMDMzy5QTi5mZZcqJxczMMuXEYoUmqVXSNZLWSvq9pEskTRhkn0/3Kz83yPZTJf3lINvMkHRf9ZFXr//5Je0r6eoMj3+7pLslPSxpY3p9t6QZWZ3DRhcP3lthSRJwO/DtiLhc0lhgMbApIv52gP2ei4jddlausP0M4NqIeONwthmqeh6733lOA9oi4qx6nsean1ssVmTvBl6MiMsBImI78NfAxyT9paR/7t1Q0rWSjpJ0MTApfSL/Yf8DSvpbSXdIWi3ps6n6YuCAtM9XaglQ0hxJt6Xj/VTSHql+lqSfS7pH0p2SDpC0m6SbUvleSQsqnb+8dSRpoqTL0/Z3STo61Z8m6d8lXZ9ac1+uJe50jDPKf15JfyHpyyn2+yX9IJ33KkmT0jZvkbRC0ipJSyW9ttbzWvE5sViRHQKsKq+IiGeBh9nJckVlyvmOAAAC8UlEQVQRcR7wQkTMiYgPl78naR4wGzgcmAO8WdI7gfOA36d9dtoS2okrgE9FxKHAvcCiVP9D4JsR8cfAnwKPAS8CH0h3dB8NfDW1ygY6/5np53oTcArQKWliem8O8OfAm4A/l7RfjbH/K/Bnknr/LT8KfD+9PjjF/6YU9xmSdgEuAT4YEW8G/gX4XI3ntCbgtcKsyETlVWZ3Vj+YeenrrlTejVKieXhIwUmvAaZGxIpU1Qn8m6TdgekR8VOAiHgxbT8e+GJKZjuA6cBgn/iPBL6RjvMbSQ8BB6b3boqIZ9Kx1wCvBx6pNv6I2CzpZuA4SeuA7RGxRtIs4MGIuC1t+i/AQmA5pWT/81I+ZCzQXe35rHk4sViR3Q98sLxC0hRgP+AZ+rbIJzI4Af8QEd/pd8wZw4qy8nkq+TAwDXhzRGyVtJ7B4x5oaZEtZa+3M7T/798DzgXWA5eX1fdP3JFiWR0R7xjCeayJuCvMiuwmYLKkjwCkwfuvUuquWQfMkTQmdQEdXrbf1tQ66O8GSuMzu6XjTZe0N7AZqPmBTqm18JSk3j+0pwIrUnddt6QT0nl2kTQZeA3wREoqR1NqYTDI+W+mlJCQdCCwP/DbWmMd4Gf4JXAAcBLw47K3Zkp6S3p9CnALsAaYLunwFM8ESYdkFYsVhxOLFVaUpjR+ADhJ0lrgd5T6+z8N/BJ4kNK4xj8Cd5btuhhY3X/wPiJupDSucKuke4Grgd0j4kngl5LuG2Tw/iBJ3WVfJwHtwFckraY05nFR2vZU4JxU/yvgdZTGXdokraSULH6T4hro/N8CxqZ4fwycFhFbyNbVwM293WrJ/cAnUvy7AovTeU8EvibpHkpdim/NOBYrAE83NrMBSbqeUhfhilSeBVwdEXPyjcwalVssZlaRpL0k/Q54qmwCgtmg3GIxq4GkNwE/6Fe9JSIK0eUj6XZgl37Vp0bEvXnEY83JicXMzDLlrjAzM8uUE4uZmWXKicXMzDLlxGJmZpn6/y49osCTH5zqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"Outlet_Location_Type\", y =\"Item_Outlet_Sales\", data = train)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()\n",
    "\n",
    "# Much of the Sales is happening in TIER3 Cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rZFbYeQOsMyuNxYsX9yovWrQoUSRmw8dArrG8HXgHMKHP9ZYdgJHNCszMzFrTQGaFbUF2LWUU2XWVHs8DxzQjKDMza10DucZyM3CzpEsi4qGiPljSGOAHwJ5kN11+ErgP+CmwO7Ac+HBEPKNsCtq3gaOANcDHI+KO/H3agJ41y86OiPaiYjQzs8Y1coPkJZI22Nc+Ig4Z5Gd/G/hVRBwjaQtgG+AM4PqIOFfSacBpwJeAWcD0/OttwPeBt0kaC5wJzCBLToslXR0RzwwypkLMmTOn0KVDTjnllEG9btq0aYN+bVXstddeLF269JXyW97yloTRmA0PjSSWv695vBXwVwxyrTBJOwAHAB8HiIiXgJckHQ0clB/WDtxElliOBn4YEQH8XtIYSbvkxy6MiJX5+y4EjgQuG0xcVj1PPPFEv2UzK14jd94v7lP1W0k3D/JzXwesAC6W9BZgMfA5YOeIeDz/vMcl7ZQfPwl4pOb1nXndxuo3IOlE4ESAXXfddZBhD8zmjBKuu+66XutZzZ49m4MPPriIsIalp556qlf5ySefTBSJ2fAx4OnGksbWfI2XdAQwcZCfOwrYG/h+RPwF8ALZaa+NfnyduuinfsPKiLkRMSMiZkyYMKHReIfMYYcd1qvspGJmraaR+1gW13zdAvwdcMIgP7cT6IyIW/PylWSJ5sn8FBf596dqjp9S8/rJwGP91Le0nhHV7NmzE0diZta4Rk6FTS3qQyPiCUmPSHpDRNwHHArcm3+1Aefm36/KX3I18FlJl5NdvH8uP1U2H/iapB3z42aSbUbW0saOHcvYsWM9WjGzljSgxJJf6zgJeBPZqaZ7ge9GxFP9vrB/JwM/zmeEPQB8gmwEdYWkE4CHgQ/lx15LNtW4g2y68ScAImKlpK8Ct+fHfaXnQr6ZmaUxkDvv3wn8BLgE+CHZdY29gdskfbTv+mEDFRF3kk0T7uvQOscGWWKr9z4XARcNJgYzMyveQEYs/wq8PyKW1NRdJemXwPlkp6bMzMyAgV2836FPUgFeGXFsX+d4MzMbxgaSWFRzcby2cuwAX2+WzMiRvddJHTWqkXuCzWwwBpIYvgUskHSgpO3zr4OAeflzZqW1bt26XmXvIGnWfANZhHKupMeAr9J7VtjZEfGfTY7PzMxazIDOC0TENcA1/R0j6fSI+OdCojLLFb2gJ3hRT7NmK/IayYc2fYjZ0Npuu+36LZtZ8Yq8kllv3S6zzbK5I4Suri4++MEPvlK+9NJLGTdu3OaGZWb9KHLEUnfxR7OUxo8f/8ooZd9993VSMRsCHrFY5e22224sX76cM844I3UoViLNuH43GMuWLQM2f3S+uYq8hlhkYvlZge9lVpjRo0czffp0j1asl46ODv7nzjsHvfdHUXpOGz17553JYih6+7sBJxZJU8kWjty99nUR8b78+9cKjs3MrKkmAif4ZAsXFnwlo5ERy38AFwL/CawvNAozM6uMRhLLixExp2mRmJlZJTSSWL4t6UxgAbC2pzIi7ig8KjMza1mNJJY3A8cDh/DqqbDIy2ZmZkBjieUDwOsi4qVmBWNmZq2vkRsk/wCMaVYgZmZWDY2MWHYG/kfS7fS+xvK+wqMyM7OW1UhiObNpUZiZWWUMOLFExM2SdgOmR8R1krYBRm7qdWZmNrwM+BqLpE8BVwLn51WTyG6aHDRJIyUtkXRNXp4q6VZJyyT9VNIWef2Webkjf373mvc4Pa+/T9IRmxOPmZltvkYu3p8EvBN4HiAilgE7bebnfw74Y035X4BvRcR04BnghLz+BOCZiJhGth3yvwBIeiNwLNnOlkcC35PkUZSZWUKNJJa1tVONJY1iM5bKlzQZeA/wg7wssntirswPaQfenz8+Oi+TP39ofvzRwOURsTYiHgQ6gP0GG5OZmW2+RhLLzZLOALaWdDjZasabs+f9vwH/wKs3W44Dno2I7rzcSXa6jfz7IwD588/lx79SX+c1vUg6UdIiSYtWrFixGWGbmVl/GkkspwErgLuATwPXRsSXB/Ohkt4LPBURi2ur6xwam3iuv9f0royYGxEzImLGhAkTGorXzMwGrpHpxidHxLeBC3oqJH0ur2vUO4H3SToK2ArYgWwEM0bSqHxUMhl4LD++E5gCdOan4F4DrKyp71H7GjMzS6CREUtbnbqPD+ZDI+L0iJgcEbuTXXy/ISI+CtwIHFPzeVflj6+u+fxj8uMjrz82nzU2FZgO3DaYmMzMrBibHLFIOg74CDBV0tU1T20PPF1wPF8CLpd0NrCEbP8X8u+XSuogG6kcCxAR90i6ArgX6AZOioh1BcdkZmYNGMipsN8BjwPjgX+tqV8FLN3cACLiJuCm/PED1JnVFREvAh/ayOvPAc7Z3DjMzKwYm0wsEfEQ8BDw9uaHY2ZmrW4gp8JWUX+mlYCIiB0Kj8rMzFrWQEYs2w9FIGZmVg2NTDc2M6uMzs5OVgEXDn4Bkcp4HFjd2VnY+zUy3djMzGyTPGIxs2Fp8uTJPNvVxQl1F/AYXi4kGDN5cmHv5xGLmZkVyiOWPubMmUNHR0fSGJYtWwbAKaeckjQOgGnTppUiDjNrHU4sfXR0dLDkrntZv83YZDHopexi4uI/PZEsBoARa1Ym/Xwza01OLHWs32YsL77xvanDSG6re69JHYKZtSBfYzEzs0I5sZiZWaGcWMzMrFC+xmJN5Vl2vXmWnQ0HTizWVB0dHdx/9x3sul26bXK2eDkbmL+4/PZkMQA8vHpk0s83GypOLNZ0u263jn+csTp1GMmdvWi71CGYDQlfYzEzs0I5sZiZWaGcWMzMrFBOLGZmVignFjMzK1SSxCJpiqQbJf1R0j2SPpfXj5W0UNKy/PuOeb0kzZHUIWmppL1r3qstP36ZpLYUP4+Zmb0q1YilG/i7iPhzYH/gJElvBE4Dro+I6cD1eRlgFjA9/zoR+D5kiQg4E3gbsB9wZk8yMjOzNJLcxxIRj5Nts0xErJL0R2AScDRwUH5YO3AT8KW8/ocREcDvJY2RtEt+7MKIWAkgaSFwJHDZkP0wZtayniD9nvdP59/HJYzhCWBMge+X/AZJSbsDfwHcCuycJx0i4nFJO+WHTQIeqXlZZ163sfpB6+zsZMSa57xkPDBizdN0dnanDsNKpquri9mzZ3PWWWcxblzKX4ebZ9q0aalDAGBFvuTQmOnTk8UwhmLbI2likbQd8HPg8xHxvLTRvafrPRH91Nf7rBPJTqOx6667Nh6smQHQ3t7O0qVLaW9v59RTT00dzqCVZc22njjmzJmTOJLiJEsskkaTJZUfR8Qv8uonJe2Sj1Z2AZ7K6zuBKTUvnww8ltcf1Kf+pnqfFxFzgbkAM2bM2OjYd/LkyTy5dpQ3+iLb6Gvy5Impw7AS6erqYt68eUQE8+bNo62traVHLdYcqWaFCbgQ+GNEfLPmqauBnpldbcBVNfUfy2eH7Q88l58ymw/MlLRjftF+Zl5nZk3Q3t7O+vXrAVi3bh3t7e2JI7IySjUr7J3A8cAhku7Mv44CzgUOl7QMODwvA1wLPAB0ABcAnwHIL9p/Fbg9//pKz4V8MyvewoUL6e7Orrt1d3ezYMGCxBFZGaWaFfbf1L8+AnBoneMDOGkj73URcFFx0VmROjs7eWHVSK/sCzy0aiTbdnamDmOzvPvd72b+/FdPChxwwAEJo7Gy8p33ZjZga9eu7bdsBiWYbmzVNnnyZF7sftz7sZDtx7LV5Mmpw9gsN998c6/yTTfdlCYQKzWPWMxswLKz0hsvm4ETi5mZFcyJxczMCuXEYmZmhXJiMTOzQnlWWB0j1qxMugilXnwegNhqh2QxQNYO4CVdzKwxTix9lGHF02XLVgEw/fWpf6lPLEV7mFlrcWLpowwrnlZttdOHV6e98/7JNdkZ3523WZ8sBsjaYY+kEZgNDScWa6oyjHheyve72Gr3dPtdAOxB+vaYM2cOHR0dhb7nYP8YmzZtWin+kLPiObFYU5XhF0fVRoBmZefEYjaMbG6ir7fopBO29eXpxmY2YJ/61Kd6lf/2b/82USRWZk4sZjZgxx9/fK/ycccdlygSKzMnFjNryMSJ2TR4j1ZsY3yNxcwaMnHiRCZOnOjRim2UE4tZi2jGVOHBWJZP304948/TlcvLicWsRXR0dLDkniUwJnEg+X2mSx5dki6GZ9N9tG2aE4tZKxkD6w9Ku4JAGYy4yZeHy8yJxaxFdHZ2wnP+pQrAs9AZnamjKOT0ZBGnFst2WtCJxcwsoa233jp1CIWrRGKRdCTwbWAk8IOIODdxSGaFmzx5MiueW5E6DFidf0+3riiQtUdqZRollEnLJxZJI4HvAocDncDtkq6OiHtTxbS5w+OiZt2UbXg8GGU51QDp2zP1ApY9etpz+qSEi3pOKk972IZaPrEA+wEdEfEAgKTLgaOBZIllc1VxaJxSVdqziKRWlinLkD5RW/NUIbFMAh6pKXcCb+t7kKQTgRMBdt1116YG5P8sxXFblk9VErU1TxUSi+rUxQYVEXOBuQAzZszY4Hmz4cCJ2oZCFeYtdgJTasqTgccSxWJmNuxVIbHcDkyXNFXSFsCxwNWJYzIzG7Za/lRYRHRL+iwwn2y68UURcU/isMzMhq2WTywAEXEtcG3qOMzMrBqnwszMrEScWMzMrFBOLGZmVihFDL9bOiStAB5KHccmjAe6UgdRIW7PYrk9i9UK7blbREwYyIHDMrG0AkmLImJG6jiqwu1ZLLdnsarWnj4VZmZmhXJiMTOzQjmxlNfc1AFUjNuzWG7PYlWqPX2NxczMCuURi5mZFcqJxczMCuXEYmZmhXJiMbMBkfQuSZ/IH0+QNDV1TK1GmQ9K+kBePlDSNyWdKKnepoUtyRfvS0zSGRHxtdRxtBpJh5Jt+HZDRDxSU98WEe3pImtdks4EZgBviIg9JL0W+FlEvDNxaC1F0nfItlPfElgBbA9cA8wCHomIUxOGVxgnlhKT9HBE7Jo6jlYi6avAIcAS4Cjg6xHx/fy5OyJi75TxtSpJdwJ/AdwREX+R1y2NiL3SRtZaJN0VEW+WNBp4AnhtRKyVNIqsbSvRnpXYj6WVSVq5safI/pqxxrwf2CciXpK0I3C5pNdFxBfJ2tQG56WICEkBIGnb1AG1qJcBIuLl/A+dtXm5W9L6tKEVx9dY0nsB2BOYUOfr8YRxtarREfESQEQ8A7wH2EnS5cDopJG1tisknQ+MkfQp4DrggsQxtaIuSdsBRMThPZWSdgZeShZVwZxY0vsRMCUi1vX56gauSB1cC/qTpHf3FCKiOyLagOXAG5NF1eIi4hvAlcDPgTcA/xQR56WNqvVExMyIWF3nqReAo4c6nmbxNRarlPyvwYiIF+o8t1tElH27hNKRNBKYHxGHpY6lSiQdC7w+Is6RNAXYKSIWp46rCB6xlIikYyV9OX88RdI+qWNqNRGxuiep9G1Psj0vrEERsQ5YI+k1qWOpinx22MHAX+dVLwD/ni6iYvnifUnkHW00cABwDq92tH1TxtWq3J6FexG4S9JCsrYEICJOSRdSS3tHROwtaQlARKyUtEXqoIrixFIele5oCbg9i/Vf+ZcV42VJI4CeWXbjgMrMCnNiKY9Kd7QE3J4Fioj2PDHvkVfdFxEvp4ypxX2XbCLEBEmzgQ8Ds9OGVBwnlvKodEdLwO1ZIEkHAe1ks+sETMlXMvh1yrhaVUT8UNJioGdCxIci4u6UMRXJs8JKRNKbeLWjXV+ljpaC27M4+S/Bj0TEfXl5D+CyiPAEk0GStBfwLrJR9W8jYmnikArjWWHlMpLsztyX8L9NEdyexRndk1QAIuJ+fMPpoOWzFS8DXku2rt1PJJ2eNqrieMRSEnlH+wjwS7JTDUcDP46If04aWItyexZL0kVkf1lfmld9FBgVEZ9IF1XrkvRHsqWH1uTlbYDFEfHnaSMrhhNLSVS9ow01t2exJG0JnER26kbAr4Hv9iyfY42R9CvgwxHxfF7egezU4nvSRlYMX7wvj4fo/e8xCnggUSxV4PYs1v+NiG8C3+ypkPQ54NvpQmppa4B7JM0nGwnOBP5b0jcBWn35fI9YSkLSL8hu3uvV0YAnofU72lBzexar3pYDkpb0LKFvjZF0Qn/PR8SFQxVT7wzTAAAOWklEQVRLM3jEUh59b0D7fapAKsLtWQBJx5Fdq5oq6eqap3YAnk4TVSU8Srb+WiX/sndiKY9Kd7QE3J7F+B3Z9g3jgX+tqV8FVGZ6bAIfB74j6Qrg4ohYljieQvlUWEnk+4XMIFsqv3Idbai5PZsjX8HgAODhqqzEm4qkMWSz6z4B/C9wMfDTeitztxrP7S+JiDiW7Bfho8Blkn4j6ZPeqW9w3J7FkHSNpD3zx7sAdwOfBC6V9PmkwbW4iHgW+AlwCbArcBzwB0mfSRlXEZxYSqTKHS0Ft2chptasWPAJYGFE/CXwNrIEY4MgaZaknwG/IduCfP98R8m3AF9KGlwBfI2lJCTNIvuP+ufAj8k62uP5X9j3At9LGV+rcXsWpnahyUPJtyOOiFVV2qM9geOB70fEDbWVEfFCvvVzS3NiKY9Kd7QE3J7FeETSyUAnsDfwKwBJW+MlXRomaUG+PfFHNnZMRCwYypiawRfvE+vpaKnjqAq3Z7Ek7QR8BdiF7E77BXn9wWQrG3wjZXytZrjc++PEkthw6WhDxe1pZSbpAeDvN/Z8RPxiCMNpGp8KS+81kj64sSer0tGGkNvTyuw1wHvJ1lvrK4BK9E8nlvSGRUcbQm5PK7OHIqLys+mcWNIbFh1tCLk9m0DS2IhYmTqOCqj3B0/l+D6W9IZFRxtCbs/muFXSzyQdJcltPHjHpw5gKPjifWKS9vSWucVxezZHnkwOI7s3aD/gp8Al+U6SZr04sZhZQ/Kpxj8CtgX+AJwWEbekjcrKxKfCzGyTJI2T9DlJi8imy55MtuLx35Etm2MNyDdJ22Rdq3JiKYmqd7Sh5vYs3C1ke7C8PyLeExG/iIjuiFgE/Hvi2FpRW526jw91EM3iU2El4R36iuX2LI6kkcDXvevm5qvZOO1dZAtQ9tgeWBcRhyUJrGCebpxYPzv0bY936GuY27N4EbFO0ltSx1ERw2LjNCeW9IZFRxtCbs/muDNP1D8DXtmIyisZNCYiHgIeAt4uaTdgekRcly/quTVZP215PhVWInU62qiIqERHS8HtWRxJF9epDt+MOjj5CtsnAmMj4vWSpgP/HhGHJg6tEE4sJVH1jjbU3J5WZpLuJLsf6Nae636S7oqIN6eNrBieFVYeJwHvBJ4HyPdo3ylpRK3N7VkgSXtIul7S3Xl5L0n/mDquFrY2Il7qKUgaRbaWXSU4sZRHpTtaAm7PYl0AnE6+o2RELAWOTRpRa7tZ0hnA1pIOJ7t29Z+JYyqME0t5VLqjJeD2LNY2EXFbn7ruJJFUw2nACuAu4NPAtRHx5bQhFcfXWEpC0gjgBGAm2UKK8yPigrRRtS63Z7EkzQM+C/wsIvaWdAxwQkTMShxaS5K0T0Qs7lP3lxFRiT9+nFhKouodbai5PYsl6XXAXOAdwDPAg8BfR8TylHG1Kkl3AG0RcVdePg74fES8LW1kxXBiKYmqd7Sh5vZsDknbAiM8bXvz5In6SuCjZHfhfwx4b0Q8lzSwgjixlETVO9pQc3sWS9LXgP8XEc/m5R2Bv4sIzwwbJEl7AP8BPEK2Btv/Jg6pME4sJVLljpaC27M49dZZq7cem/VP0l30np24E/AcsBYgIvZKEVfRvKRLYnU62lhgJNmOfZXpaEPF7dk0IyVtGRFrAfKVDLZMHFMrem/qAIaCE0t6w6KjDSG3Z3P8CLg+X9olyHaSbE8bUuuJiIfyGYtLI2LP1PE0i0+FlcBw6GhDye3ZHJKOJNueGGBhRMxPGU8rk/Rj4PSIeDh1LM3gEUsJRMR6SX+QtGtVO9pQcns2zRJgNNmIZUniWFrdLsA9km6j92rR70sXUnGcWMqj0h0tAbdngSR9GPg6cBPZDafnSfpiRFyZNLDWNTt1AM3kU2ElIenAevURcfNQx1IFbs9iSfoDcHhEPJWXJwDXRYQ3ALMNeMRSEv6FVyy3Z+FG9CSV3NN4rcFBk7Q/cB7w58AWZDMXX4iIHZIGVhB3jJKQtL+k2yWtlvSSpHWSnk8dV6tyexbuV5LmS/q4pI8D/wVcmzimVvYd4DhgGdnOkX+T11WCRyzl8R2yZch/Bswgu1N8etKIWpvbs0AR8UVJHyRbxUDA3Ij4ZeKwWlpEdEgaGRHrgIsl/S51TEVxYimRKne0FNyexZA0kmx16MMA73FfjDWStgDulPT/gMeBbRPHVBgnlvKodEdLwO1ZkIhYJ2mNpNd4rbXCHE92XeWzwBeAKcBfJY2oQJ4VVhKSdgOeIrtP4AvAa4DvRURH0sBalNuzWJKuAPYHFtJ7+vYpyYKy0nJiMbNNktRWrz4ivKzLIEh6L/BVYDeyM0cCoiqzwpxYSqLqHW2ouT2tzCR1AB8E7ooK/hJ2YimJqne0oeb2LIako4HJEfHdvHwrMCF/+h985/3gSLoRODQi1qeOpRl88b48HgHu9i/Bwrg9i/EPZNO2e2wJ7Es2EeJiss3UrHH/AFwr6WbyvVgAIuKb6UIqjhNLeVS6oyXg9izGFhHxSE35vyPiaeDpfJtiG5xzgNXAVmR33leKE0t5VLqjJeD2LMaOtYWI+GxNcQI2WGMjYmbqIJrFiaU8Kt3REnB7FuNWSZ+KiAtqKyV9GrgtUUxVcJ2kmRGxIHUgzeCL9yUh6Vzghqp2tKHm9iyGpJ2A/yA7nXhHXr0P2bWW90fEk6lia2WSVpFdp1oLvEzFZi06sZRE1TvaUHN7FkvSIcCb8uI9EXFDynhamSQBU6q8CZ0TSwkMh442lNyeVnaSFkfEPqnjaBYvm18C+ZRYrxRbELentYDfS9o3dRDN4sRSHpXuaAm4Pa3MDibro3+StFTSXZKWpg6qKD4VVhKS7gXeACwnW+Sv55rAXinjalVuTyuzfJHUDUTEQ0MdSzM4sZRE1TvaUHN7WtlJehcwPSIuljQB2C4iHkwdVxF8Kqwk8l94U4BD8sdr8L/PoLk9rcwknQl8CTg9rxoN/ChdRMXyf7SSqHpHG2puTyu5DwDvI9/bJiIeA7ZPGlGBnFjKo9IdLQG3p5XZS/nsxQCo2rprTizlUemOloDb08rsCknnA2MkfQq4DrhgE69pGV4rrDz6drRPUqGOloDb00orIr4h6XDgeWAP4J8iYmHisArjWWElkne0noUTF1Spo6Xg9rQykzQR2I9sVH17RDyROKTCeMRSLncBW5N1tLsSx1IFbk8rJUl/A/wTcAPZPVbnSfpKRFyUNrJieMRSEnU62oFAZTraUHN7WplJug94R75pGpLGAb+LiDekjawYTiwlUfWONtTcnlZmkq4HZkXES3l5C+DaiDgsbWTF8Kmw8ugEVtWUV5Ht226D4/a0MnuUbBO1q8hO1R4N3CbpVGj9LbSdWMqj0h0tAbenldmf8q8eV+XfK3GvlRNLeVS6oyXg9rTSiojZqWNoJl9jMTMbYpJuJL95t1ZEHJIgnMJ5xFISVe9oQ83taSX39zWPtwL+CuhOFEvhnFjKo9IdLQG3p5VWRCzuU/VbSTcnCaYJnFhKouodbai5Pa3MJI2tKY4A9gEmJgqncE4sJVH1jjbU3J5WcovJTtWKbCT9IHBC0ogK5MRSHpXuaAm4Pa20ImJq6hiaybPCzMyGiKR9gUd6FpyU9DGy638PAWdFxMqU8RXF+7EkJmnffJXTnvLHJF0laU6f0zk2AG5PK7nzgZ5lXA4AzgV+CDwHzE0YV6GcWNIbFh1tCLk9rcxG1oxK/g8wNyJ+HhH/HzAtYVyF8jWW9Op2NODnku5MGFercntamY2UNCoiuoFDgRNrnqvM72OPWNIbKamnQx1Ktsx7j8p0tCHk9rQyuwy4OV/D7n+B3wBImkY2qq4E/0dLr6ejdVHhjjaE3J5WWhFxTr5k/i5ku5r2zJ4aAZycLrJieVZYCUjan1c72gt53R7AdhFxR9LgWpDb0ywtJxYzMyuUr7GYmVmhnFjMzKxQTixmZlYoJxazjZA0Ob9rf5mkP0n6tqQtNvGaM/qUV2/i+DGSPtPP82+WdGf+tVLSg/nj6xr7acyGji/em9UhScCtwPcj4mJJI8nu3F8ZEV/s53WrI2K7jZXrHL87cE1E7DmAmC7Jj71ywD+IWQIesZjVdwjwYkRcDBAR64AvAJ+U9BlJ3+k5UNI1kg6SdC6wdT6i+HHfN5T0RUm3S1oqqWfP83OB1+ev+XojAUq6TNJ7aso/lXSUpL+R9EtJ8yXdJ+kfa45pk3Rb/nnfk+TfAVY4dyqz+t5EtvT+KyLieeBhNnJjcUScBvxvRLw1Ij5a+5ykmcB0YD/grcA++VpmpwF/yl+z0ZHQRvwA+ET+/jsC+wLz8+f2A44F9gY+IumtkvYEPgC8IyLemv8cxzb4mWab5DvvzeoT2X4uA63flJn515K8vB1Zonl4UNFlbgDOkzQOOA64IiLWZWfxmB8RzwBI+g/gXWT/3/cFFuXHbA08shmfb1aXE4tZffeQ7ZPxCkk7AFPIloapHe1vNYD3E/DPEXF+n/fcfbABRkTkp9w+Anw8//7K030Pz2O4KF9J16xpfCrMrL7rgW3yjZjIL97/K3AJ8ADwVkkjJE0hO+3U42VJo+u833yy6zPb5e83SdJOwCpg+82I82Lgi2TXg+6rqZ+ZzzjbBjga+C1wHfBhSePzGMZJ2nUzPtusLicWszryxQE/AHxI0jLgfuBF4AyyX9IPAncB3wBq1x+bCyzte/E+IhYAPwFukXQXcCWwfUQ8DfxW0t2NXrzP3/exPLaL+zz13/nnLQEui4g7I+IuYDZwnaSlwAJg50Y/02xTPN3YrIVJ2pYswb0lIlbldX8D7BkRn08anA1bHrGYtShJRwB/BL7Vk1TMysAjFrOSkPRm4NI+1Wsj4m0p4jEbLCcWMzMrlE+FmZlZoZxYzMysUE4sZmZWKCcWMzMr1P8P8Oy+YCywmQ0AAAAASUVORK5CYII=\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 = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary - Bivariate Analysis\n",
    "\n",
    "1. Low Fat Items have a higher sales than the Regular Items\n",
    "\n",
    "2. Seafood has a high MRP and Low Demand where as the products that have made money over 8K are ['Snack Foods', 'Dairy', 'Fruits and Vegetables', 'Frozen Foods',\n",
    "       'Starchy Foods', 'Breads', 'Health and Hygiene', 'Soft Drinks',\n",
    "       'Canned', 'Household', 'Breakfast', 'Meat']\n",
    "       \n",
    "3. 1985 Store is the oldest store and is really making a good money. Suggestion: Create Brand Perception in the eyes of Customer.\n",
    "\n",
    "4. Tier 3 City, Medium Size Outlet with Theme of OUT027 and S3 will be a better combination if we are planning to operate business in a new city.\n",
    "\n",
    "\n",
    "### Missing Values Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>8521</th>\n",
       "      <td>FDN46</td>\n",
       "      <td>7.21</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.145221</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>103.1332</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>1845.5976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>DRG01</td>\n",
       "      <td>14.80</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.044878</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>75.4670</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>1997</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>765.6700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "8521           FDN46         7.21          Regular         0.145221   \n",
       "8522           DRG01        14.80          Low Fat         0.044878   \n",
       "\n",
       "        Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "8521  Snack Foods  103.1332            OUT018                       2009   \n",
       "8522  Soft Drinks   75.4670            OUT046                       1997   \n",
       "\n",
       "     Outlet_Size Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "8521      Medium               Tier 3  Supermarket Type2          1845.5976  \n",
       "8522       Small               Tier 1  Supermarket Type1           765.6700  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Weight    1463\n",
       "Outlet_Size    2410\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()[train.isnull().sum()!=0]"
   ]
  },
  {
   "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>Item_Type</th>\n",
       "      <th>Baking Goods</th>\n",
       "      <th>Breads</th>\n",
       "      <th>Breakfast</th>\n",
       "      <th>Canned</th>\n",
       "      <th>Dairy</th>\n",
       "      <th>Frozen Foods</th>\n",
       "      <th>Fruits and Vegetables</th>\n",
       "      <th>Hard Drinks</th>\n",
       "      <th>Health and Hygiene</th>\n",
       "      <th>Household</th>\n",
       "      <th>Meat</th>\n",
       "      <th>Others</th>\n",
       "      <th>Seafood</th>\n",
       "      <th>Snack Foods</th>\n",
       "      <th>Soft Drinks</th>\n",
       "      <th>Starchy Foods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>536.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>566.000000</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>1019.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>988.000000</td>\n",
       "      <td>374.000000</td>\n",
       "      <td>130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.277108</td>\n",
       "      <td>11.346936</td>\n",
       "      <td>12.768202</td>\n",
       "      <td>12.305705</td>\n",
       "      <td>13.426069</td>\n",
       "      <td>12.867061</td>\n",
       "      <td>13.224769</td>\n",
       "      <td>11.400328</td>\n",
       "      <td>13.142314</td>\n",
       "      <td>13.384736</td>\n",
       "      <td>12.817344</td>\n",
       "      <td>13.853285</td>\n",
       "      <td>12.552843</td>\n",
       "      <td>12.987880</td>\n",
       "      <td>11.847460</td>\n",
       "      <td>13.690731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.773622</td>\n",
       "      <td>4.440540</td>\n",
       "      <td>5.038131</td>\n",
       "      <td>4.586564</td>\n",
       "      <td>4.686532</td>\n",
       "      <td>4.507383</td>\n",
       "      <td>4.575275</td>\n",
       "      <td>4.239144</td>\n",
       "      <td>4.512313</td>\n",
       "      <td>4.998845</td>\n",
       "      <td>4.670812</td>\n",
       "      <td>4.225534</td>\n",
       "      <td>5.473830</td>\n",
       "      <td>4.531256</td>\n",
       "      <td>4.403699</td>\n",
       "      <td>4.010061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.880000</td>\n",
       "      <td>4.635000</td>\n",
       "      <td>6.425000</td>\n",
       "      <td>4.615000</td>\n",
       "      <td>4.805000</td>\n",
       "      <td>4.555000</td>\n",
       "      <td>5.460000</td>\n",
       "      <td>4.610000</td>\n",
       "      <td>5.175000</td>\n",
       "      <td>5.030000</td>\n",
       "      <td>5.150000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.365000</td>\n",
       "      <td>5.095000</td>\n",
       "      <td>4.590000</td>\n",
       "      <td>6.695000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.235000</td>\n",
       "      <td>7.128750</td>\n",
       "      <td>8.060000</td>\n",
       "      <td>8.115000</td>\n",
       "      <td>9.277500</td>\n",
       "      <td>8.935000</td>\n",
       "      <td>9.195000</td>\n",
       "      <td>8.260000</td>\n",
       "      <td>9.695000</td>\n",
       "      <td>8.695000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>10.650000</td>\n",
       "      <td>7.420000</td>\n",
       "      <td>9.195000</td>\n",
       "      <td>8.260000</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.650000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>10.695000</td>\n",
       "      <td>12.150000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>12.850000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>12.150000</td>\n",
       "      <td>13.150000</td>\n",
       "      <td>12.350000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>11.650000</td>\n",
       "      <td>13.150000</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>13.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.750000</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>18.350000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>17.750000</td>\n",
       "      <td>17.750000</td>\n",
       "      <td>16.850000</td>\n",
       "      <td>15.350000</td>\n",
       "      <td>16.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.850000</td>\n",
       "      <td>20.850000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>21.350000</td>\n",
       "      <td>20.700000</td>\n",
       "      <td>20.850000</td>\n",
       "      <td>21.350000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Item_Type  Baking Goods      Breads  Breakfast      Canned       Dairy  \\\n",
       "count        536.000000  204.000000  89.000000  539.000000  566.000000   \n",
       "mean          12.277108   11.346936  12.768202   12.305705   13.426069   \n",
       "std            4.773622    4.440540   5.038131    4.586564    4.686532   \n",
       "min            4.880000    4.635000   6.425000    4.615000    4.805000   \n",
       "25%            8.235000    7.128750   8.060000    8.115000    9.277500   \n",
       "50%           11.650000   10.600000  10.695000   12.150000   13.350000   \n",
       "75%           15.750000   14.850000  17.250000   16.250000   17.600000   \n",
       "max           20.850000   20.850000  21.100000   21.350000   20.700000   \n",
       "\n",
       "Item_Type  Frozen Foods  Fruits and Vegetables  Hard Drinks  \\\n",
       "count        718.000000            1019.000000   183.000000   \n",
       "mean          12.867061              13.224769    11.400328   \n",
       "std            4.507383               4.575275     4.239144   \n",
       "min            4.555000               5.460000     4.610000   \n",
       "25%            8.935000               9.195000     8.260000   \n",
       "50%           12.850000              13.100000    10.100000   \n",
       "75%           17.000000              17.100000    14.850000   \n",
       "max           20.850000              21.350000    19.700000   \n",
       "\n",
       "Item_Type  Health and Hygiene   Household        Meat      Others    Seafood  \\\n",
       "count              430.000000  759.000000  337.000000  137.000000  51.000000   \n",
       "mean                13.142314   13.384736   12.817344   13.853285  12.552843   \n",
       "std                  4.512313    4.998845    4.670812    4.225534   5.473830   \n",
       "min                  5.175000    5.030000    5.150000    5.500000   5.365000   \n",
       "25%                  9.695000    8.695000    9.300000   10.650000   7.420000   \n",
       "50%                 12.150000   13.150000   12.350000   14.500000  11.650000   \n",
       "75%                 17.600000   18.350000   17.200000   17.750000  17.750000   \n",
       "max                 21.250000   21.250000   21.250000   20.500000  20.750000   \n",
       "\n",
       "Item_Type  Snack Foods  Soft Drinks  Starchy Foods  \n",
       "count       988.000000   374.000000     130.000000  \n",
       "mean         12.987880    11.847460      13.690731  \n",
       "std           4.531256     4.403699       4.010061  \n",
       "min           5.095000     4.590000       6.695000  \n",
       "25%           9.195000     8.260000      11.500000  \n",
       "50%          13.150000    11.800000      13.175000  \n",
       "75%          16.850000    15.350000      16.700000  \n",
       "max          21.250000    20.750000      21.200000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Item Type\n",
    "train.groupby(\"Item_Type\")[\"Item_Weight\"].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "544     14.8\n",
       "1218    14.8\n",
       "3005     NaN\n",
       "5974     NaN\n",
       "8522    14.8\n",
       "Name: Item_Weight, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Item_Identifier == \"DRG01\"][\"Item_Weight\"]\n",
    "\n",
    "# Products are kept in box and then the box is labeled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Item Weight Basis the Item ID. - Loop and Lambda\n",
    "df = train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lambda - Single One Time Loop Function that is created without the need of saving it.\n",
    "# Use Lambda to fill the values using Mean\n",
    "# Transform the Column -impute the missing values...\n",
    "\n",
    "# Group all the IDs & Fill the values basis the ID\n",
    "df[\"Item_Weight\"] = df.groupby(\"Item_Identifier\")[\"Item_Weight\"].transform(lambda x:x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Item_Weight.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Location where the Missing Value Exists in the Data\n",
    "locs = train[train.Item_Weight.isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ID of these locations\n",
    "ids = train[train.Item_Weight.isnull()][\"Item_Identifier\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>7</th>\n",
       "      <td>FDP10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.127470</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.7622</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>4022.7636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DRI11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.034238</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>113.2834</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>2303.6680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FDW12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.035400</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>144.5444</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>4064.0432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FDC37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.057557</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>107.6938</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>214.3876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>FDC14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.072222</td>\n",
       "      <td>Canned</td>\n",
       "      <td>43.6454</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>125.8362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>FDV20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.059512</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>128.0678</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>2797.6916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>FDX10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.123111</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>36.9874</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>388.1614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>FDB34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.026481</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>87.6198</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>2180.4950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>FDS02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.255395</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>196.8794</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>780.3176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>FDI26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.061082</td>\n",
       "      <td>Canned</td>\n",
       "      <td>180.0344</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>892.1720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>FDF09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.012090</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>38.2848</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>484.7024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>FDY40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.150286</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>51.0692</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>147.8076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>FDY45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.026016</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>255.8356</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>2543.3560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>FDN48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.113720</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>89.9804</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>643.1628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>NCL18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.293418</td>\n",
       "      <td>Household</td>\n",
       "      <td>194.6136</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>583.2408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>FDR12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.031382</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>171.3764</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>3091.9752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>FDQ49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.039058</td>\n",
       "      <td>Breakfast</td>\n",
       "      <td>155.9630</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>3285.7230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>FDU04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.009715</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>120.0414</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>487.3656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>NCP18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.028460</td>\n",
       "      <td>Household</td>\n",
       "      <td>149.9708</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>4363.6532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>FDD10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.045798</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>178.5344</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>2854.9504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>DRE60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.278974</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>225.3720</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>679.1160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>FDW39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.064625</td>\n",
       "      <td>Meat</td>\n",
       "      <td>176.9370</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>176.4370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>FDX34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.071637</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>121.7098</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>4097.3332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>FDY25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.033810</td>\n",
       "      <td>Canned</td>\n",
       "      <td>180.5976</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>7968.2944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>NCR53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.144338</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>224.4404</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>6976.2524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>FDH35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.059957</td>\n",
       "      <td>Starchy Foods</td>\n",
       "      <td>165.4526</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>5262.4832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>DRK12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.041683</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>31.2900</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>898.8300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>FDR07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.077367</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>97.0094</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>1808.9786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>NCB30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.025579</td>\n",
       "      <td>Household</td>\n",
       "      <td>198.8084</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>5555.4352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>DRY23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.191014</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>42.1112</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>42.6112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8349</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016653</td>\n",
       "      <td>Meat</td>\n",
       "      <td>139.5180</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>2936.1780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8351</th>\n",
       "      <td>NCE43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.102941</td>\n",
       "      <td>Household</td>\n",
       "      <td>171.2448</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>4602.0096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8353</th>\n",
       "      <td>NCX41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.031024</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>210.5244</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>1482.0708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8356</th>\n",
       "      <td>FDN45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.117531</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>222.7088</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>4026.7584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8358</th>\n",
       "      <td>FDK27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015664</td>\n",
       "      <td>Meat</td>\n",
       "      <td>122.2756</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>121.1756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8362</th>\n",
       "      <td>FDY37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.026440</td>\n",
       "      <td>Canned</td>\n",
       "      <td>143.6470</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>2862.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8367</th>\n",
       "      <td>FDA32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.052691</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>216.3192</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>215.7192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8372</th>\n",
       "      <td>FDA01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.054115</td>\n",
       "      <td>Canned</td>\n",
       "      <td>58.4904</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>1054.6272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8373</th>\n",
       "      <td>FDX44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.042758</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>88.4172</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>2051.9956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8375</th>\n",
       "      <td>NCM05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.104784</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>266.0226</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>528.6452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8383</th>\n",
       "      <td>NCQ54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.012482</td>\n",
       "      <td>Household</td>\n",
       "      <td>168.3474</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>5221.8694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8390</th>\n",
       "      <td>NCQ05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.037829</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>151.0708</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>451.4124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8404</th>\n",
       "      <td>DRH39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>75.9670</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>153.1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8405</th>\n",
       "      <td>FDB09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.100493</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>123.1046</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>124.5046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8422</th>\n",
       "      <td>FDD08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.035183</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>37.8506</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>417.4566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8435</th>\n",
       "      <td>FDT48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>196.5084</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>793.6336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8442</th>\n",
       "      <td>FDX40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.173324</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>39.9164</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>38.6164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8444</th>\n",
       "      <td>FDO28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.071948</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>121.6098</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>482.0392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8449</th>\n",
       "      <td>FDG02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.019717</td>\n",
       "      <td>Canned</td>\n",
       "      <td>191.9188</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>571.2564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8454</th>\n",
       "      <td>NCH54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.127234</td>\n",
       "      <td>Household</td>\n",
       "      <td>158.3920</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>159.7920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8458</th>\n",
       "      <td>FDX20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.074518</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>227.3720</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>452.7440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8463</th>\n",
       "      <td>FDG59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.043025</td>\n",
       "      <td>Starchy Foods</td>\n",
       "      <td>37.6164</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>810.9444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8469</th>\n",
       "      <td>FDQ45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019114</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>182.1608</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>367.5216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8472</th>\n",
       "      <td>NCS41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.053185</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>182.9608</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>1653.8472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8480</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>154.5340</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>459.4020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8485</th>\n",
       "      <td>DRK37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.043792</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>189.0530</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>6261.8490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8487</th>\n",
       "      <td>DRG13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.037006</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>164.7526</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>4111.3150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8488</th>\n",
       "      <td>NCN14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.091473</td>\n",
       "      <td>Others</td>\n",
       "      <td>184.6608</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>2756.4120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8490</th>\n",
       "      <td>FDU44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.102296</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>162.3552</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>487.3656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8504</th>\n",
       "      <td>NCN18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.124111</td>\n",
       "      <td>Household</td>\n",
       "      <td>111.7544</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>4138.6128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1463 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "7              FDP10          NaN          Low Fat         0.127470   \n",
       "18             DRI11          NaN          Low Fat         0.034238   \n",
       "21             FDW12          NaN          Regular         0.035400   \n",
       "23             FDC37          NaN          Low Fat         0.057557   \n",
       "29             FDC14          NaN          Regular         0.072222   \n",
       "36             FDV20          NaN          Regular         0.059512   \n",
       "38             FDX10          NaN          Regular         0.123111   \n",
       "39             FDB34          NaN          Low Fat         0.026481   \n",
       "49             FDS02          NaN          Regular         0.255395   \n",
       "59             FDI26          NaN          Low Fat         0.061082   \n",
       "62             FDF09          NaN          Low Fat         0.012090   \n",
       "63             FDY40          NaN          Regular         0.150286   \n",
       "64             FDY45          NaN          Low Fat         0.026016   \n",
       "77             FDN48          NaN          Low Fat         0.113720   \n",
       "83             NCL18          NaN          Low Fat         0.293418   \n",
       "84             FDR12          NaN          Regular         0.031382   \n",
       "87             FDQ49          NaN          Regular         0.039058   \n",
       "95             FDU04          NaN          Low Fat         0.009715   \n",
       "98             NCP18          NaN          Low Fat         0.028460   \n",
       "101            FDD10          NaN          Regular         0.045798   \n",
       "108            DRE60          NaN          Low Fat         0.278974   \n",
       "112            FDW39          NaN          Regular         0.064625   \n",
       "113            FDX34          NaN          Low Fat         0.071637   \n",
       "130            FDY25          NaN          Low Fat         0.033810   \n",
       "132            NCR53          NaN          Low Fat         0.144338   \n",
       "136            FDH35          NaN          Low Fat         0.059957   \n",
       "153            DRK12          NaN          Low Fat         0.041683   \n",
       "161            FDR07          NaN          Low Fat         0.077367   \n",
       "168            NCB30          NaN          Low Fat         0.025579   \n",
       "178            DRY23          NaN          Regular         0.191014   \n",
       "...              ...          ...              ...              ...   \n",
       "8349           FDN15          NaN          Low Fat         0.016653   \n",
       "8351           NCE43          NaN          Low Fat         0.102941   \n",
       "8353           NCX41          NaN          Low Fat         0.031024   \n",
       "8356           FDN45          NaN          Low Fat         0.117531   \n",
       "8358           FDK27          NaN          Low Fat         0.015664   \n",
       "8362           FDY37          NaN          Regular         0.026440   \n",
       "8367           FDA32          NaN          Low Fat         0.052691   \n",
       "8372           FDA01          NaN          Regular         0.054115   \n",
       "8373           FDX44          NaN          Low Fat         0.042758   \n",
       "8375           NCM05          NaN          Low Fat         0.104784   \n",
       "8383           NCQ54          NaN          Low Fat         0.012482   \n",
       "8390           NCQ05          NaN          Low Fat         0.037829   \n",
       "8404           DRH39          NaN          Low Fat         0.000000   \n",
       "8405           FDB09          NaN          Low Fat         0.100493   \n",
       "8422           FDD08          NaN          Low Fat         0.035183   \n",
       "8435           FDT48          NaN          Low Fat         0.000000   \n",
       "8442           FDX40          NaN          Low Fat         0.173324   \n",
       "8444           FDO28          NaN          Low Fat         0.071948   \n",
       "8449           FDG02          NaN          Low Fat         0.019717   \n",
       "8454           NCH54          NaN          Low Fat         0.127234   \n",
       "8458           FDX20          NaN          Low Fat         0.074518   \n",
       "8463           FDG59          NaN          Low Fat         0.043025   \n",
       "8469           FDQ45          NaN          Regular         0.019114   \n",
       "8472           NCS41          NaN          Low Fat         0.053185   \n",
       "8480           FDQ58          NaN          Low Fat         0.000000   \n",
       "8485           DRK37          NaN          Low Fat         0.043792   \n",
       "8487           DRG13          NaN          Low Fat         0.037006   \n",
       "8488           NCN14          NaN          Low Fat         0.091473   \n",
       "8490           FDU44          NaN          Regular         0.102296   \n",
       "8504           NCN18          NaN          Low Fat         0.124111   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "7               Snack Foods  107.7622            OUT027   \n",
       "18              Hard Drinks  113.2834            OUT027   \n",
       "21             Baking Goods  144.5444            OUT027   \n",
       "23             Baking Goods  107.6938            OUT019   \n",
       "29                   Canned   43.6454            OUT019   \n",
       "36    Fruits and Vegetables  128.0678            OUT027   \n",
       "38              Snack Foods   36.9874            OUT027   \n",
       "39              Snack Foods   87.6198            OUT027   \n",
       "49                    Dairy  196.8794            OUT019   \n",
       "59                   Canned  180.0344            OUT019   \n",
       "62    Fruits and Vegetables   38.2848            OUT027   \n",
       "63             Frozen Foods   51.0692            OUT019   \n",
       "64              Snack Foods  255.8356            OUT027   \n",
       "77             Baking Goods   89.9804            OUT019   \n",
       "83                Household  194.6136            OUT019   \n",
       "84             Baking Goods  171.3764            OUT027   \n",
       "87                Breakfast  155.9630            OUT027   \n",
       "95             Frozen Foods  120.0414            OUT019   \n",
       "98                Household  149.9708            OUT027   \n",
       "101             Snack Foods  178.5344            OUT027   \n",
       "108             Soft Drinks  225.3720            OUT019   \n",
       "112                    Meat  176.9370            OUT019   \n",
       "113             Snack Foods  121.7098            OUT027   \n",
       "130                  Canned  180.5976            OUT027   \n",
       "132      Health and Hygiene  224.4404            OUT027   \n",
       "136           Starchy Foods  165.4526            OUT027   \n",
       "153             Soft Drinks   31.2900            OUT027   \n",
       "161   Fruits and Vegetables   97.0094            OUT027   \n",
       "168               Household  198.8084            OUT027   \n",
       "178             Soft Drinks   42.1112            OUT019   \n",
       "...                     ...       ...               ...   \n",
       "8349                   Meat  139.5180            OUT027   \n",
       "8351              Household  171.2448            OUT027   \n",
       "8353     Health and Hygiene  210.5244            OUT019   \n",
       "8356            Snack Foods  222.7088            OUT027   \n",
       "8358                   Meat  122.2756            OUT019   \n",
       "8362                 Canned  143.6470            OUT027   \n",
       "8367  Fruits and Vegetables  216.3192            OUT019   \n",
       "8372                 Canned   58.4904            OUT027   \n",
       "8373  Fruits and Vegetables   88.4172            OUT027   \n",
       "8375     Health and Hygiene  266.0226            OUT019   \n",
       "8383              Household  168.3474            OUT027   \n",
       "8390     Health and Hygiene  151.0708            OUT019   \n",
       "8404                  Dairy   75.9670            OUT019   \n",
       "8405  Fruits and Vegetables  123.1046            OUT019   \n",
       "8422  Fruits and Vegetables   37.8506            OUT027   \n",
       "8435           Baking Goods  196.5084            OUT027   \n",
       "8442           Frozen Foods   39.9164            OUT019   \n",
       "8444           Frozen Foods  121.6098            OUT027   \n",
       "8449                 Canned  191.9188            OUT019   \n",
       "8454              Household  158.3920            OUT019   \n",
       "8458  Fruits and Vegetables  227.3720            OUT019   \n",
       "8463          Starchy Foods   37.6164            OUT027   \n",
       "8469            Snack Foods  182.1608            OUT019   \n",
       "8472     Health and Hygiene  182.9608            OUT027   \n",
       "8480            Snack Foods  154.5340            OUT019   \n",
       "8485            Soft Drinks  189.0530            OUT027   \n",
       "8487            Soft Drinks  164.7526            OUT027   \n",
       "8488                 Others  184.6608            OUT027   \n",
       "8490  Fruits and Vegetables  162.3552            OUT019   \n",
       "8504              Household  111.7544            OUT027   \n",
       "\n",
       "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "7                          1985      Medium               Tier 3   \n",
       "18                         1985      Medium               Tier 3   \n",
       "21                         1985      Medium               Tier 3   \n",
       "23                         1985       Small               Tier 1   \n",
       "29                         1985       Small               Tier 1   \n",
       "36                         1985      Medium               Tier 3   \n",
       "38                         1985      Medium               Tier 3   \n",
       "39                         1985      Medium               Tier 3   \n",
       "49                         1985       Small               Tier 1   \n",
       "59                         1985       Small               Tier 1   \n",
       "62                         1985      Medium               Tier 3   \n",
       "63                         1985       Small               Tier 1   \n",
       "64                         1985      Medium               Tier 3   \n",
       "77                         1985       Small               Tier 1   \n",
       "83                         1985       Small               Tier 1   \n",
       "84                         1985      Medium               Tier 3   \n",
       "87                         1985      Medium               Tier 3   \n",
       "95                         1985       Small               Tier 1   \n",
       "98                         1985      Medium               Tier 3   \n",
       "101                        1985      Medium               Tier 3   \n",
       "108                        1985       Small               Tier 1   \n",
       "112                        1985       Small               Tier 1   \n",
       "113                        1985      Medium               Tier 3   \n",
       "130                        1985      Medium               Tier 3   \n",
       "132                        1985      Medium               Tier 3   \n",
       "136                        1985      Medium               Tier 3   \n",
       "153                        1985      Medium               Tier 3   \n",
       "161                        1985      Medium               Tier 3   \n",
       "168                        1985      Medium               Tier 3   \n",
       "178                        1985       Small               Tier 1   \n",
       "...                         ...         ...                  ...   \n",
       "8349                       1985      Medium               Tier 3   \n",
       "8351                       1985      Medium               Tier 3   \n",
       "8353                       1985       Small               Tier 1   \n",
       "8356                       1985      Medium               Tier 3   \n",
       "8358                       1985       Small               Tier 1   \n",
       "8362                       1985      Medium               Tier 3   \n",
       "8367                       1985       Small               Tier 1   \n",
       "8372                       1985      Medium               Tier 3   \n",
       "8373                       1985      Medium               Tier 3   \n",
       "8375                       1985       Small               Tier 1   \n",
       "8383                       1985      Medium               Tier 3   \n",
       "8390                       1985       Small               Tier 1   \n",
       "8404                       1985       Small               Tier 1   \n",
       "8405                       1985       Small               Tier 1   \n",
       "8422                       1985      Medium               Tier 3   \n",
       "8435                       1985      Medium               Tier 3   \n",
       "8442                       1985       Small               Tier 1   \n",
       "8444                       1985      Medium               Tier 3   \n",
       "8449                       1985       Small               Tier 1   \n",
       "8454                       1985       Small               Tier 1   \n",
       "8458                       1985       Small               Tier 1   \n",
       "8463                       1985      Medium               Tier 3   \n",
       "8469                       1985       Small               Tier 1   \n",
       "8472                       1985      Medium               Tier 3   \n",
       "8480                       1985       Small               Tier 1   \n",
       "8485                       1985      Medium               Tier 3   \n",
       "8487                       1985      Medium               Tier 3   \n",
       "8488                       1985      Medium               Tier 3   \n",
       "8490                       1985       Small               Tier 1   \n",
       "8504                       1985      Medium               Tier 3   \n",
       "\n",
       "            Outlet_Type  Item_Outlet_Sales  \n",
       "7     Supermarket Type3          4022.7636  \n",
       "18    Supermarket Type3          2303.6680  \n",
       "21    Supermarket Type3          4064.0432  \n",
       "23        Grocery Store           214.3876  \n",
       "29        Grocery Store           125.8362  \n",
       "36    Supermarket Type3          2797.6916  \n",
       "38    Supermarket Type3           388.1614  \n",
       "39    Supermarket Type3          2180.4950  \n",
       "49        Grocery Store           780.3176  \n",
       "59        Grocery Store           892.1720  \n",
       "62    Supermarket Type3           484.7024  \n",
       "63        Grocery Store           147.8076  \n",
       "64    Supermarket Type3          2543.3560  \n",
       "77        Grocery Store           643.1628  \n",
       "83        Grocery Store           583.2408  \n",
       "84    Supermarket Type3          3091.9752  \n",
       "87    Supermarket Type3          3285.7230  \n",
       "95        Grocery Store           487.3656  \n",
       "98    Supermarket Type3          4363.6532  \n",
       "101   Supermarket Type3          2854.9504  \n",
       "108       Grocery Store           679.1160  \n",
       "112       Grocery Store           176.4370  \n",
       "113   Supermarket Type3          4097.3332  \n",
       "130   Supermarket Type3          7968.2944  \n",
       "132   Supermarket Type3          6976.2524  \n",
       "136   Supermarket Type3          5262.4832  \n",
       "153   Supermarket Type3           898.8300  \n",
       "161   Supermarket Type3          1808.9786  \n",
       "168   Supermarket Type3          5555.4352  \n",
       "178       Grocery Store            42.6112  \n",
       "...                 ...                ...  \n",
       "8349  Supermarket Type3          2936.1780  \n",
       "8351  Supermarket Type3          4602.0096  \n",
       "8353      Grocery Store          1482.0708  \n",
       "8356  Supermarket Type3          4026.7584  \n",
       "8358      Grocery Store           121.1756  \n",
       "8362  Supermarket Type3          2862.9400  \n",
       "8367      Grocery Store           215.7192  \n",
       "8372  Supermarket Type3          1054.6272  \n",
       "8373  Supermarket Type3          2051.9956  \n",
       "8375      Grocery Store           528.6452  \n",
       "8383  Supermarket Type3          5221.8694  \n",
       "8390      Grocery Store           451.4124  \n",
       "8404      Grocery Store           153.1340  \n",
       "8405      Grocery Store           124.5046  \n",
       "8422  Supermarket Type3           417.4566  \n",
       "8435  Supermarket Type3           793.6336  \n",
       "8442      Grocery Store            38.6164  \n",
       "8444  Supermarket Type3           482.0392  \n",
       "8449      Grocery Store           571.2564  \n",
       "8454      Grocery Store           159.7920  \n",
       "8458      Grocery Store           452.7440  \n",
       "8463  Supermarket Type3           810.9444  \n",
       "8469      Grocery Store           367.5216  \n",
       "8472  Supermarket Type3          1653.8472  \n",
       "8480      Grocery Store           459.4020  \n",
       "8485  Supermarket Type3          6261.8490  \n",
       "8487  Supermarket Type3          4111.3150  \n",
       "8488  Supermarket Type3          2756.4120  \n",
       "8490      Grocery Store           487.3656  \n",
       "8504  Supermarket Type3          4138.6128  \n",
       "\n",
       "[1463 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Item_Weight.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.iloc[27, 1] = train.loc[train.Item_Identifier==\"FDI18\", \"Item_Weight\"].mean()\n",
    "# Location and ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in locs:\n",
    "    train.iloc[i, 1] = train.loc[train.Item_Identifier==ids[i], \"Item_Weight\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Weight.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>27</th>\n",
       "      <td>DRJ59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.019356</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>39.1164</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>308.9312</td>\n",
       "    </tr>\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",
       "27             DRJ59          NaN          Low Fat         0.019356   \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",
       "27     Hard Drinks   39.1164            OUT013                       1987   \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",
       "27          High               Tier 3  Supermarket Type1           308.9312  \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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Item_Weight.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FDC48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015782</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>81.0592</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",
       "4           FDY38          NaN          Regular         0.118599   \n",
       "7           FDC48          NaN          Low Fat         0.015782   \n",
       "\n",
       "      Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "4         Dairy  234.2300            OUT027                       1985   \n",
       "7  Baking Goods   81.0592            OUT027                       1985   \n",
       "\n",
       "  Outlet_Size Outlet_Location_Type        Outlet_Type  \n",
       "4      Medium               Tier 3  Supermarket Type3  \n",
       "7      Medium               Tier 3  Supermarket Type3  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values in the Test Set\n",
    "test[test.Item_Weight.isnull()].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Row # 4, Col#1, test.iloc[4,1] = test.loc[test.Item_Identifier==\"FDY38\", \"Item_Weight\"].mean()\n",
    "\n",
    "# Location Nos/Row Index Nos\n",
    "# ITem ID.\n",
    "\n",
    "# Item IDs\n",
    "ids = test[test.Item_Weight.isnull()][\"Item_Identifier\"]\n",
    "\n",
    "# List of all the rows where Item Weight is Missing\n",
    "indexnos = test[test.Item_Weight.isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test.iloc[4,1] = test.loc[test.Item_Identifier==\"FDY38\", \"Item_Weight\"].mean()\n",
    "\n",
    "for i in indexnos:\n",
    "    test.iloc[i,1] = test.loc[test.Item_Identifier==ids[i], \"Item_Weight\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test.Item_Weight.isnull()]\n",
    "# Calculate the Mean of the Item Weight where the Type is Snack Food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "newid = test[test.Item_Weight.isnull()].index\n",
    "newitems = test[test.Item_Weight.isnull()][\"Item_Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in newid:\n",
    "    test.iloc[i,1] = test.loc[test.Item_Type==newitems[i], \"Item_Weight\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>27</th>\n",
       "      <td>DRJ59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.019356</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>39.1164</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>308.9312</td>\n",
       "    </tr>\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",
       "27             DRJ59          NaN          Low Fat         0.019356   \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",
       "27     Hard Drinks   39.1164            OUT013                       1987   \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",
       "27          High               Tier 3  Supermarket Type1           308.9312  \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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values in Train\n",
    "train[train.Item_Weight.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "newids = train[train.Item_Weight.isnull()].index\n",
    "newitem = train[train.Item_Weight.isnull()][\"Item_Type\"]\n",
    "\n",
    "for i in newids:\n",
    "    train.iloc[i,1] = train.loc[train.Item_Type==newitem[i], \"Item_Weight\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlet_Size    2410\n",
      "dtype: int64\n",
      "Outlet_Size    1606\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.isnull().sum()[train.isnull().sum()!=0])\n",
    "print(test.isnull().sum()[test.isnull().sum()!=0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlet Size\n",
    "\n",
    "What type of outlet it be, it depends on the Location & Type of City."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium    2793\n",
       "Small     2388\n",
       "High       932\n",
       "Name: Outlet_Size, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Outlet_Size.value_counts()\n",
    "\n",
    "# Mode is Medium Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>High</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Medium</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Small</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Tier 3</th>\n",
       "      <th>Tier 1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Tier 3</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Tier 1</th>\n",
       "      <th>Tier 2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Supermarket Type3</th>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <td>621</td>\n",
       "      <td>620</td>\n",
       "      <td>624</td>\n",
       "      <td>618</td>\n",
       "      <td>620</td>\n",
       "      <td>352</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Size                       High            Medium                    \\\n",
       "Outlet_Location_Type            Tier 3            Tier 1            Tier 3   \n",
       "Outlet_Type          Supermarket Type1 Supermarket Type1 Supermarket Type3   \n",
       "Outlet_Type                        621               620               624   \n",
       "\n",
       "Outlet_Size                                        Small                \\\n",
       "Outlet_Location_Type                              Tier 1                 \n",
       "Outlet_Type          Supermarket Type2 Supermarket Type1 Grocery Store   \n",
       "Outlet_Type                        618               620           352   \n",
       "\n",
       "Outlet_Size                             \n",
       "Outlet_Location_Type            Tier 2  \n",
       "Outlet_Type          Supermarket Type1  \n",
       "Outlet_Type                        620  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(test.groupby([\"Outlet_Size\", \n",
    "                            \"Outlet_Location_Type\"])[\"Outlet_Type\"].value_counts()).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rule:\n",
    "\n",
    "1. Medium Outlets are S1, S2 and S3.\n",
    "\n",
    "2. High Spaced Outlets are S1 only\n",
    "\n",
    "3. S1 is Small in Tier 1 and Tier 2 and we have Grocery Stores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "outlets = test.loc[test.Outlet_Size.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Tier 2</th>\n",
       "      <th>Tier 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Grocery Store</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <td>1236</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Location_Type            Tier 2        Tier 3\n",
       "Outlet_Type          Supermarket Type1 Grocery Store\n",
       "Outlet_Type                       1236           370"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(outlets.groupby(\"Outlet_Location_Type\")[\"Outlet_Type\"].value_counts()).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train.Outlet_Size.isnull(), \"Outlet_Size\"]=\"Small\"\n",
    "test.loc[test.Outlet_Size.isnull(), \"Outlet_Size\"]=\"Small\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove 0s from Item Visibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item Visibility Vs Sales\n",
    "plt.figure(figsize = [10, 5])\n",
    "plt.scatter(train.Item_Visibility, train.Item_Outlet_Sales, color = \"red\")\n",
    "plt.xlabel(\"Item Visibility\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lambda to fill the values\n",
    "#train.Item_Visibility.transform(x:x.replace(to_replace = 0, x.mean())) \n",
    "# Taking the Mean of Item IDs\n",
    "\n",
    "train[\"Item_Visibility\"]=train.groupby(\"Item_Identifier\")[\"Item_Visibility\"].transform(lambda x:x.replace(to_replace = 0, value = x.mean()))                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 5])\n",
    "plt.scatter(train.Item_Visibility, train.Item_Outlet_Sales, color = \"red\")\n",
    "plt.xlabel(\"Item Visibility\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "\n",
    "1. Extracting New Features from the existing data\n",
    "2. Transformation of the values\n",
    "3. Dummying the data or introducing Dummies.\n",
    "\n",
    "4. Outlet Est Years - 1985 Vintage Outlets, Outlets>1985 and Less than 2000< Medieval\n",
    "and then 2000> Latest Outlets. Outlet Age (2013 - Out Est Year)\n",
    "\n",
    "5. Drop the Outlet ID because Outlet Size is there. Another way to deal with this is that the Most Happening outlet is OUT027 & Rest as Others. The essence is to GENERALIZE the Info for ML Model.\n",
    "\n",
    "\n",
    "6. Item_Type has been categorised in Perishables and Non Perishables\n",
    "\n",
    "7. Item Identifier - FD: Food, DR - Drinks, NC - Non Consumables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047        Dairy   \n",
       "1           DRC01         5.92          Regular         0.019278  Soft Drinks   \n",
       "2           FDN15        17.50          Low Fat         0.016760         Meat   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  249.8092            OUT049                       1999      Medium   \n",
       "1   48.2692            OUT018                       2009      Medium   \n",
       "2  141.6180            OUT049                       1999      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "0               Tier 1  Supermarket Type1          3735.1380  \n",
       "1               Tier 3  Supermarket Type2           443.4228  \n",
       "2               Tier 1  Supermarket Type1          2097.2700  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlet Age\n",
    "train[\"Outlet_Age\"] = 2013 - train.Outlet_Establishment_Year\n",
    "\n",
    "test[\"Outlet_Age\"] = 2013 - test.Outlet_Establishment_Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlets(x):\n",
    "    if(x==\"OUT027\"):\n",
    "        return(\"OUT027\")\n",
    "    else:\n",
    "        return(\"Others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlet_Cat\n",
    "train[\"Outlet_Cat\"] = train.Outlet_Identifier.apply(outlets)\n",
    "\n",
    "test[\"Outlet_Cat\"] = test.Outlet_Identifier.apply(outlets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perishables & Non Perisbhables in Item_Type\n",
    "train.Item_Type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "perishables =['Dairy', 'Meat', 'Fruits and Vegetables', 'Breakfast','Breads', 'Seafood']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perish(x):\n",
    "    if(x in perishables):\n",
    "        return(\"Perishables\")\n",
    "    else:\n",
    "        return(\"Non Perishables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Item_Category\"] = train.Item_Type.apply(perish)\n",
    "\n",
    "test[\"Item_Category\"] = test.Item_Type.apply(perish)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OUT010</th>\n",
       "      <td>555.0</td>\n",
       "      <td>339.351662</td>\n",
       "      <td>271.014855</td>\n",
       "      <td>33.2900</td>\n",
       "      <td>154.13270</td>\n",
       "      <td>250.3408</td>\n",
       "      <td>455.40720</td>\n",
       "      <td>1775.6886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT013</th>\n",
       "      <td>932.0</td>\n",
       "      <td>2298.995256</td>\n",
       "      <td>1533.531664</td>\n",
       "      <td>73.2380</td>\n",
       "      <td>1072.60380</td>\n",
       "      <td>2050.6640</td>\n",
       "      <td>3166.37835</td>\n",
       "      <td>10256.6490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT017</th>\n",
       "      <td>926.0</td>\n",
       "      <td>2340.675263</td>\n",
       "      <td>1550.778025</td>\n",
       "      <td>143.8128</td>\n",
       "      <td>1195.94325</td>\n",
       "      <td>2005.0567</td>\n",
       "      <td>3096.46935</td>\n",
       "      <td>9664.7528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT018</th>\n",
       "      <td>928.0</td>\n",
       "      <td>1995.498739</td>\n",
       "      <td>1375.932889</td>\n",
       "      <td>69.2432</td>\n",
       "      <td>981.55565</td>\n",
       "      <td>1655.1788</td>\n",
       "      <td>2702.64865</td>\n",
       "      <td>6768.5228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT019</th>\n",
       "      <td>528.0</td>\n",
       "      <td>340.329723</td>\n",
       "      <td>249.979449</td>\n",
       "      <td>33.9558</td>\n",
       "      <td>153.63335</td>\n",
       "      <td>265.3213</td>\n",
       "      <td>460.73360</td>\n",
       "      <td>1482.0708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT027</th>\n",
       "      <td>935.0</td>\n",
       "      <td>3694.038558</td>\n",
       "      <td>2127.760054</td>\n",
       "      <td>241.6854</td>\n",
       "      <td>2044.33890</td>\n",
       "      <td>3364.9532</td>\n",
       "      <td>4975.52340</td>\n",
       "      <td>13086.9648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT035</th>\n",
       "      <td>930.0</td>\n",
       "      <td>2438.841866</td>\n",
       "      <td>1538.512533</td>\n",
       "      <td>113.8518</td>\n",
       "      <td>1238.88735</td>\n",
       "      <td>2109.2544</td>\n",
       "      <td>3310.35760</td>\n",
       "      <td>8479.6288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT045</th>\n",
       "      <td>929.0</td>\n",
       "      <td>2192.384798</td>\n",
       "      <td>1462.399658</td>\n",
       "      <td>99.8700</td>\n",
       "      <td>1068.60900</td>\n",
       "      <td>1834.9448</td>\n",
       "      <td>2929.52000</td>\n",
       "      <td>8994.9580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT046</th>\n",
       "      <td>930.0</td>\n",
       "      <td>2277.844267</td>\n",
       "      <td>1488.405631</td>\n",
       "      <td>101.8674</td>\n",
       "      <td>1137.18640</td>\n",
       "      <td>1945.8005</td>\n",
       "      <td>3095.63710</td>\n",
       "      <td>9779.9362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OUT049</th>\n",
       "      <td>930.0</td>\n",
       "      <td>2348.354635</td>\n",
       "      <td>1513.289464</td>\n",
       "      <td>111.8544</td>\n",
       "      <td>1182.79370</td>\n",
       "      <td>1966.1074</td>\n",
       "      <td>3281.72820</td>\n",
       "      <td>7646.0472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean          std       min         25%  \\\n",
       "Outlet_Identifier                                                          \n",
       "OUT010             555.0   339.351662   271.014855   33.2900   154.13270   \n",
       "OUT013             932.0  2298.995256  1533.531664   73.2380  1072.60380   \n",
       "OUT017             926.0  2340.675263  1550.778025  143.8128  1195.94325   \n",
       "OUT018             928.0  1995.498739  1375.932889   69.2432   981.55565   \n",
       "OUT019             528.0   340.329723   249.979449   33.9558   153.63335   \n",
       "OUT027             935.0  3694.038558  2127.760054  241.6854  2044.33890   \n",
       "OUT035             930.0  2438.841866  1538.512533  113.8518  1238.88735   \n",
       "OUT045             929.0  2192.384798  1462.399658   99.8700  1068.60900   \n",
       "OUT046             930.0  2277.844267  1488.405631  101.8674  1137.18640   \n",
       "OUT049             930.0  2348.354635  1513.289464  111.8544  1182.79370   \n",
       "\n",
       "                         50%         75%         max  \n",
       "Outlet_Identifier                                     \n",
       "OUT010              250.3408   455.40720   1775.6886  \n",
       "OUT013             2050.6640  3166.37835  10256.6490  \n",
       "OUT017             2005.0567  3096.46935   9664.7528  \n",
       "OUT018             1655.1788  2702.64865   6768.5228  \n",
       "OUT019              265.3213   460.73360   1482.0708  \n",
       "OUT027             3364.9532  4975.52340  13086.9648  \n",
       "OUT035             2109.2544  3310.35760   8479.6288  \n",
       "OUT045             1834.9448  2929.52000   8994.9580  \n",
       "OUT046             1945.8005  3095.63710   9779.9362  \n",
       "OUT049             1966.1074  3281.72820   7646.0472  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Outlet_Identifier\")[\"Item_Outlet_Sales\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>932.0</td>\n",
       "      <td>2298.995256</td>\n",
       "      <td>1533.531664</td>\n",
       "      <td>73.2380</td>\n",
       "      <td>1072.6038</td>\n",
       "      <td>2050.6640</td>\n",
       "      <td>3166.37835</td>\n",
       "      <td>10256.6490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>2793.0</td>\n",
       "      <td>2681.603542</td>\n",
       "      <td>1855.210528</td>\n",
       "      <td>69.2432</td>\n",
       "      <td>1270.3464</td>\n",
       "      <td>2251.0698</td>\n",
       "      <td>3691.19520</td>\n",
       "      <td>13086.9648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>4798.0</td>\n",
       "      <td>1867.182814</td>\n",
       "      <td>1570.580229</td>\n",
       "      <td>33.2900</td>\n",
       "      <td>583.2408</td>\n",
       "      <td>1484.0682</td>\n",
       "      <td>2758.40940</td>\n",
       "      <td>9779.9362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean          std      min        25%        50%  \\\n",
       "Outlet_Size                                                                    \n",
       "High          932.0  2298.995256  1533.531664  73.2380  1072.6038  2050.6640   \n",
       "Medium       2793.0  2681.603542  1855.210528  69.2432  1270.3464  2251.0698   \n",
       "Small        4798.0  1867.182814  1570.580229  33.2900   583.2408  1484.0682   \n",
       "\n",
       "                    75%         max  \n",
       "Outlet_Size                          \n",
       "High         3166.37835  10256.6490  \n",
       "Medium       3691.19520  13086.9648  \n",
       "Small        2758.40940   9779.9362  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Outlet_Size\")[\"Item_Outlet_Sales\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FD'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Identifier[0][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the Pattern from ITEM ID\n",
    "ids = []\n",
    "\n",
    "for i in train.Item_Identifier:\n",
    "    ids.append(i[:2])\n",
    "\n",
    "train[\"Item_ID\"] =pd.Series(ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "  <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>Outlet_Age</th>\n",
       "      <th>Outlet_Cat</th>\n",
       "      <th>Item_Category</th>\n",
       "      <th>Item_ID</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>14</td>\n",
       "      <td>Others</td>\n",
       "      <td>Perishables</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>4</td>\n",
       "      <td>Others</td>\n",
       "      <td>Non Perishables</td>\n",
       "      <td>DR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047        Dairy   \n",
       "1           DRC01         5.92          Regular         0.019278  Soft Drinks   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  249.8092            OUT049                       1999      Medium   \n",
       "1   48.2692            OUT018                       2009      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  Outlet_Age  \\\n",
       "0               Tier 1  Supermarket Type1          3735.1380          14   \n",
       "1               Tier 3  Supermarket Type2           443.4228           4   \n",
       "\n",
       "  Outlet_Cat    Item_Category Item_ID  \n",
       "0     Others      Perishables      FD  \n",
       "1     Others  Non Perishables      DR  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FD', 'DR', 'NC'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_ID.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute Non Edible where the Item ID is NC\n",
    "#train.loc[train.Item_ID==\"NC\", \"Item_Fat_Content\"]=\"Non Edible\"\n",
    "\n",
    "train.loc[train.Item_ID==\"NC\", \"Item_Fat_Content\"]=\"Non Edible\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat       3918\n",
       "Regular       3006\n",
       "Non Edible    1599\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Price Per Unit/Item\n",
    "\n",
    "train[\"Price_Per_Unit\"] = train.Item_MRP/train.Item_Weight\n",
    "\n",
    "test[\"Price_Per_Unit\"] = test.Item_MRP/test.Item_Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the Pattern from ITEM ID\n",
    "ids = []\n",
    "\n",
    "for i in test.Item_Identifier:\n",
    "    ids.append(i[:2])\n",
    "\n",
    "test[\"Item_ID\"] =pd.Series(ids)\n",
    "\n",
    "test.loc[test.Item_ID==\"NC\", \"Item_Fat_Content\"]=\"Non Edible\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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', 'Outlet_Age', 'Outlet_Cat',\n",
       "       'Item_Category', 'Item_ID', 'Price_Per_Unit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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', 'Outlet_Age', 'Outlet_Cat', 'Item_Category',\n",
       "       'Price_Per_Unit', 'Item_ID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 16)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train.columns), len(test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the Extra columns\n",
    "newtrain = train.drop(['Item_Identifier', \"Item_Type\", \"Outlet_Identifier\", \n",
    "                       \"Outlet_Establishment_Year\"], axis = 1)\n",
    "\n",
    "\n",
    "newtest = test.drop(['Item_Identifier', \"Item_Type\", \"Outlet_Identifier\", \n",
    "                       \"Outlet_Establishment_Year\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Outlet_Age</th>\n",
       "      <th>Outlet_Cat</th>\n",
       "      <th>Item_Category</th>\n",
       "      <th>Item_ID</th>\n",
       "      <th>Price_Per_Unit</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>14</td>\n",
       "      <td>Others</td>\n",
       "      <td>Perishables</td>\n",
       "      <td>FD</td>\n",
       "      <td>26.861204</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>4</td>\n",
       "      <td>Others</td>\n",
       "      <td>Non Perishables</td>\n",
       "      <td>DR</td>\n",
       "      <td>8.153581</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>14</td>\n",
       "      <td>Others</td>\n",
       "      <td>Perishables</td>\n",
       "      <td>FD</td>\n",
       "      <td>8.092457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.015274</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>15</td>\n",
       "      <td>Others</td>\n",
       "      <td>Perishables</td>\n",
       "      <td>FD</td>\n",
       "      <td>9.484115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>Non Edible</td>\n",
       "      <td>0.008082</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>26</td>\n",
       "      <td>Others</td>\n",
       "      <td>Non Perishables</td>\n",
       "      <td>NC</td>\n",
       "      <td>6.031512</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         0.015274  182.0950       Small   \n",
       "4         8.93       Non Edible         0.008082   53.8614        High   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  Outlet_Age  \\\n",
       "0               Tier 1  Supermarket Type1          3735.1380          14   \n",
       "1               Tier 3  Supermarket Type2           443.4228           4   \n",
       "2               Tier 1  Supermarket Type1          2097.2700          14   \n",
       "3               Tier 3      Grocery Store           732.3800          15   \n",
       "4               Tier 3  Supermarket Type1           994.7052          26   \n",
       "\n",
       "  Outlet_Cat    Item_Category Item_ID  Price_Per_Unit  \n",
       "0     Others      Perishables      FD       26.861204  \n",
       "1     Others  Non Perishables      DR        8.153581  \n",
       "2     Others      Perishables      FD        8.092457  \n",
       "3     Others      Perishables      FD        9.484115  \n",
       "4     Others  Non Perishables      NC        6.031512  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.38345617],\n",
       "       [0.38345617, 1.        ]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(train.Price_Per_Unit, train.Item_Outlet_Sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.44164865e+01, 5.25194827e+03],\n",
       "       [5.25194827e+03, 2.91214094e+06]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cov(train.Price_Per_Unit, train.Item_Outlet_Sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics - Hypothesis Tests\n",
    "\n",
    "1. Between the Numerical Columns, the Test that can be done - TTEST\n",
    "\n",
    "Why TTest:\n",
    "\n",
    "You need to have Population Standard Deviation which is not available.\n",
    "So, I have to go with Sample SD and hence TT test is a test that can be applied.\n",
    "\n",
    "Mean of Sales all time and SD of Sales all time - Pop Data\n",
    "\n",
    "Standard Hypothesis Test Statement - Mean of One Col is equal to the Mean of Other Col/Data\n",
    "\n",
    "#### Null Hypothesis\n",
    "Ho: The Mean of Price Per Unit is Same as Mean of Item Outlet Sales (or the avg is different)\n",
    "\n",
    "#### Alternate Hypothesis\n",
    "\n",
    "H1: The Mean of Price Per Unit is Different as Mean of Item Outlet Sales\n",
    "\n",
    "Rule: If the PValue is less than 0.05 then we reject the Ho meaning that the Test Result is Statistically Siginificant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=-117.52853782838329, pvalue=0.0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Price Per Unit Vs Item Outlet Sales\n",
    "stats.ttest_rel(newtrain.Price_Per_Unit, newtrain.Item_Outlet_Sales)\n",
    "\n",
    "# Inf: That the Mean(Price Per Unit) & Mean(TV) is not same. Its different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0907969684852738e-215\n"
     ]
    }
   ],
   "source": [
    "# Whether the Item Fat Content and Item Category are Independent of Each Other \n",
    "# Or have some relation.\n",
    "\n",
    "# Ho: That the Categories are Independent of each other meaning happening of one event does \n",
    "# not have an impact on the other.\n",
    "\n",
    "# H1: Ho is False.\n",
    "\n",
    "# Frequency Table\n",
    "array = pd.crosstab(newtrain.Item_Fat_Content, newtrain.Item_Category)\n",
    "\n",
    "# Apply Chi Square - chi2_Contingency\n",
    "teststats, pvalue, dof, exp_freq = stats.chi2_contingency(array)\n",
    "\n",
    "print(pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Weight', 'Item_Fat_Content', 'Item_Visibility', 'Item_MRP',\n",
       "       'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type',\n",
       "       'Item_Outlet_Sales', 'Outlet_Age', 'Outlet_Cat', 'Item_Category',\n",
       "       'Item_ID', 'Price_Per_Unit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categories vs Target Variable (Numerical) - Anova\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "model = ols(\"Item_Outlet_Sales~Item_Fat_Content+Outlet_Size+Outlet_Location_Type+Outlet_Type+Outlet_Cat+Item_Category+Item_ID\",\n",
    "           data = newtrain).fit()\n",
    "\n",
    "anova_tbl = sm.stats.anova_lm(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <td>2.0</td>\n",
       "      <td>9.201502e+06</td>\n",
       "      <td>4.600751e+06</td>\n",
       "      <td>2.080158</td>\n",
       "      <td>1.249739e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.184389e+09</td>\n",
       "      <td>5.921943e+08</td>\n",
       "      <td>267.751503</td>\n",
       "      <td>1.692967e-113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.419529e+09</td>\n",
       "      <td>7.097646e+08</td>\n",
       "      <td>320.909102</td>\n",
       "      <td>4.323114e-135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.343020e+09</td>\n",
       "      <td>1.114340e+09</td>\n",
       "      <td>503.831548</td>\n",
       "      <td>2.168788e-301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Cat</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.383682e+06</td>\n",
       "      <td>1.383682e+06</td>\n",
       "      <td>0.625611</td>\n",
       "      <td>4.289920e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Category</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.737764e+07</td>\n",
       "      <td>1.737764e+07</td>\n",
       "      <td>7.857030</td>\n",
       "      <td>5.073801e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_ID</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.190753e+07</td>\n",
       "      <td>1.095377e+07</td>\n",
       "      <td>4.952577</td>\n",
       "      <td>7.085557e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>8511.0</td>\n",
       "      <td>1.882404e+10</td>\n",
       "      <td>2.211731e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          df        sum_sq       mean_sq           F  \\\n",
       "Item_Fat_Content         2.0  9.201502e+06  4.600751e+06    2.080158   \n",
       "Outlet_Size              2.0  1.184389e+09  5.921943e+08  267.751503   \n",
       "Outlet_Location_Type     2.0  1.419529e+09  7.097646e+08  320.909102   \n",
       "Outlet_Type              3.0  3.343020e+09  1.114340e+09  503.831548   \n",
       "Outlet_Cat               1.0  1.383682e+06  1.383682e+06    0.625611   \n",
       "Item_Category            1.0  1.737764e+07  1.737764e+07    7.857030   \n",
       "Item_ID                  2.0  2.190753e+07  1.095377e+07    4.952577   \n",
       "Residual              8511.0  1.882404e+10  2.211731e+06         NaN   \n",
       "\n",
       "                             PR(>F)  \n",
       "Item_Fat_Content       1.249739e-01  \n",
       "Outlet_Size           1.692967e-113  \n",
       "Outlet_Location_Type  4.323114e-135  \n",
       "Outlet_Type           2.168788e-301  \n",
       "Outlet_Cat             4.289920e-01  \n",
       "Item_Category          5.073801e-03  \n",
       "Item_ID                7.085557e-03  \n",
       "Residual                        NaN  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content        False\n",
       "Outlet_Size              True\n",
       "Outlet_Location_Type     True\n",
       "Outlet_Type              True\n",
       "Outlet_Cat              False\n",
       "Item_Category            True\n",
       "Item_ID                  True\n",
       "Name: PR(>F), dtype: bool"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_tbl[\"PR(>F)\"][:-1]<0.05\n",
    "\n",
    "# Item Fat Content & Outlet_Cat are not to be included in the modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ols(\"Item_Outlet_Sales~Item_Weight+Price_Per_Unit+Item_MRP\",data = newtrain).fit()\n",
    "\n",
    "anova_tbl = sm.stats.anova_lm(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Item_Weight</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.326436e+06</td>\n",
       "      <td>4.326436e+06</td>\n",
       "      <td>2.191595</td>\n",
       "      <td>1.388025e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price_Per_Unit</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.067812e+09</td>\n",
       "      <td>6.067812e+09</td>\n",
       "      <td>3073.704755</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_MRP</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.927735e+09</td>\n",
       "      <td>1.927735e+09</td>\n",
       "      <td>976.511694</td>\n",
       "      <td>4.797552e-203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>8519.0</td>\n",
       "      <td>1.681739e+10</td>\n",
       "      <td>1.974104e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    df        sum_sq       mean_sq            F         PR(>F)\n",
       "Item_Weight        1.0  4.326436e+06  4.326436e+06     2.191595   1.388025e-01\n",
       "Price_Per_Unit     1.0  6.067812e+09  6.067812e+09  3073.704755   0.000000e+00\n",
       "Item_MRP           1.0  1.927735e+09  1.927735e+09   976.511694  4.797552e-203\n",
       "Residual        8519.0  1.681739e+10  1.974104e+06          NaN            NaN"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Weight       False\n",
       "Price_Per_Unit     True\n",
       "Item_MRP           True\n",
       "Name: PR(>F), dtype: bool"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_tbl[\"PR(>F)\"][:-1]<0.05 # Remove Item Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removed the Columns where the PValue wasn't Significant.\n",
    "finaltrain = newtrain.drop([\"Item_Weight\", \"Item_Fat_Content\", \"Outlet_Cat\"], axis = 1)\n",
    "finaltest = newtest.drop([\"Item_Weight\", \"Item_Fat_Content\", \"Outlet_Cat\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply Dummies\n",
    "dummytrain = pd.get_dummies(finaltrain)\n",
    "dummytest = pd.get_dummies(finaltest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8523, 20), (5681, 19))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummytrain.shape, dummytest.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling\n",
    "\n",
    "1. Base Model - Average. See what the RMSE it gives us. Our intent is to reduce the RMSE.\n",
    "\n",
    "2. Decision Tree Model\n",
    "\n",
    "3. Stacking of all the Models basis Cross Validation. This will tell us which is the final model to be deployed.\n",
    "\n",
    "4. Final Model and Do the Submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Outlet_Identifier</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDW58</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier Outlet_Identifier  Item_Outlet_Sales\n",
       "0           FDW58            OUT049               1000\n",
       "1           FDW14            OUT017               1000\n",
       "2           NCN55            OUT010               1000\n",
       "3           FDQ58            OUT017               1000\n",
       "4           FDY38            OUT027               1000"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = pd.read_csv(\"~/Downloads/sample_submission_8RXa3c6.csv\")\n",
    "\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base Submission - Prepare a DF and then export into the PC\n",
    "\n",
    "solution = pd.DataFrame({\"Item_Identifier\": sample.Item_Identifier, \n",
    "                        \"Outlet_Identifier\": test.Outlet_Identifier,\n",
    "                        \"Item_Outlet_Sales\": train.Item_Outlet_Sales.mean()})\n",
    "\n",
    "# Export it in csv\n",
    "solution.to_csv(\"basemodel.csv\", index = False) # 1773.82"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mukul.singh\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sample' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-79-5b89889e9ba5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# Export for submission\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m solution = pd.DataFrame({\"Item_Identifier\": sample.Item_Identifier, \n\u001b[0m\u001b[1;32m     17\u001b[0m                         \u001b[0;34m\"Outlet_Identifier\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutlet_Identifier\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m                         \"Item_Outlet_Sales\": pred})\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sample' is not defined"
     ]
    }
   ],
   "source": [
    "# Decision Tree Regressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dtree = DecisionTreeRegressor()\n",
    "\n",
    "# Fit the Model on the Data and predict it\n",
    "\n",
    "# Defined the Xtrain and YTrain\n",
    "X = dummytrain.drop(\"Item_Outlet_Sales\", axis = 1)\n",
    "y = dummytrain.Item_Outlet_Sales\n",
    "\n",
    "# Fit and Predict\n",
    "pred = dtree.fit(X,y).predict(dummytest)\n",
    "\n",
    "# Export for submission\n",
    "\n",
    "solution = pd.DataFrame({\"Item_Identifier\": sample.Item_Identifier, \n",
    "                        \"Outlet_Identifier\": test.Outlet_Identifier,\n",
    "                        \"Item_Outlet_Sales\": pred})\n",
    "\n",
    "# Export it in csv\n",
    "solution.to_csv(\"treebased.csv\", index = False) # 1573.16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stacked Model\n",
    "from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, GradientBoostingRegressor\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "from sklearn.linear_model import Lasso, Ridge, LinearRegression\n",
    "\n",
    "# Function of Models\n",
    "\n",
    "def stackedmodels():\n",
    "    models = dict()\n",
    "    models[\"LR\"] = LinearRegression()\n",
    "    models[\"Lasso\"] = Lasso()\n",
    "    models[\"Ridge\"] = Ridge()\n",
    "    models[\"Tree\"] = DecisionTreeRegressor()\n",
    "    models[\"RF\"] = RandomForestRegressor()\n",
    "    models[\"Bagg\"] = BaggingRegressor()\n",
    "    models[\"GBR\"] = GradientBoostingRegressor()\n",
    "    models[\"XGB\"] = XGBRegressor()\n",
    "    return(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time to Create the Cross Validation\n",
    "from sklearn.model_selection import RepeatedKFold, cross_val_score\n",
    "\n",
    "# Note: Cross val score for Reg generates -ve value: -neg abolsute erro\n",
    "\n",
    "def eval_models(model):\n",
    "    cross_v= RepeatedKFold(n_splits=10, random_state=1)\n",
    "    scores = -cross_val_score(model, X, y, scoring = \"neg_mean_squared_error\",\n",
    "                            cv = cross_v, error_score=\"raise\", n_jobs = -1)\n",
    "    return(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">LR 1277625 (89014.212)\n",
      ">Lasso 1278129 (89055.160)\n",
      ">Ridge 1277611 (89029.921)\n",
      ">Tree 2349390 (168714.697)\n",
      ">RF 1387870 (103478.600)\n",
      ">Bagg 1388531 (98540.134)\n",
      ">GBR 1176360 (85747.189)\n",
      ">XGB 1173554 (85809.514)\n"
     ]
    }
   ],
   "source": [
    "# Apply Loop so that scores can be generated for all the Models\n",
    "\n",
    "models = stackedmodels()\n",
    "\n",
    "# Evaluations\n",
    "result, names = list(), list()\n",
    "\n",
    "for name, model in models.items():\n",
    "    scores = eval_models(model) # Evaluation Funct that we have created\n",
    "    result.append(scores) # Save the Cross Val Score\n",
    "    names.append(name) # Save the Name of the Model\n",
    "    print(\">%s %3.f (%.3f)\" % (name, scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "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.boxplot(result, labels = names, showmeans = True)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.ylabel(\"MAE\")\n",
    "plt.xlabel(\"Models\")\n",
    "plt.title(\"Model Performance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "\n",
    "# Fit and Predict\n",
    "pred = rf.fit(X,y).predict(dummytest)\n",
    "\n",
    "# Export for submission\n",
    "\n",
    "solution = pd.DataFrame({\"Item_Identifier\": sample.Item_Identifier, \n",
    "                        \"Outlet_Identifier\": test.Outlet_Identifier,\n",
    "                        \"Item_Outlet_Sales\": pred})\n",
    "\n",
    "# Export it in csv\n",
    "solution.to_csv(\"FINAL_RF.csv\", index = False) # 1273 RMSE for GBR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final Submission\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "xg = XGBRegressor()\n",
    "\n",
    "# Defined the Xtrain and YTrain\n",
    "X = dummytrain.drop(\"Item_Outlet_Sales\", axis = 1)\n",
    "y = dummytrain.Item_Outlet_Sales\n",
    "\n",
    "# Fit and Predict\n",
    "pred_xgb = xg.fit(X,y).predict(dummytest)\n",
    "\n",
    "# Export for submission\n",
    "\n",
    "solution = pd.DataFrame({\"Item_Identifier\": test.Item_Identifier, \n",
    "                        \"Outlet_Identifier\": test.Outlet_Identifier,\n",
    "                        \"Item_Outlet_Sales\": pred_xgb})\n",
    "\n",
    "# Export it in csv\n",
    "solution.to_csv(\"XGBMODEL.csv\", index = False) # 1152.79 RMSE"
   ]
  }
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