BigMart Sales.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required Libraries\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from IPython.display import display,clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the Train Data\n",
    "\n",
    "dataset= pd.read_csv(\"train_v9rqX0R.csv\")\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               object\n",
       "Item_Weight                  float64\n",
       "Item_Fat_Content              object\n",
       "Item_Visibility              float64\n",
       "Item_Type                     object\n",
       "Item_MRP                     float64\n",
       "Outlet_Identifier             object\n",
       "Outlet_Establishment_Year      int64\n",
       "Outlet_Size                   object\n",
       "Outlet_Location_Type          object\n",
       "Outlet_Type                   object\n",
       "Item_Outlet_Sales            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular', 'low fat', 'LF', 'reg'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Item_Fat_Content\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"Item_Fat_Content\"]=dataset[\"Item_Fat_Content\"].map({\"Low Fat\":\"Low Fat\",\"Regular\":\"Regular\",\"low fat\":\"Low Fat\",\"LF\":\"Low Fat\",\"reg\":\"Regular\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Item_Fat_Content\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Medium', nan, 'High', 'Small'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Outlet_Size\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Supermarket Type1', 'Supermarket Type2', 'Grocery Store',\n",
       "       'Supermarket Type3'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Outlet_Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>1999-01-01</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>2009-01-01</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>1999-01-01</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>1998-01-01</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>1987-01-01</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                1999-01-01      Medium               Tier 1   \n",
       "1                2009-01-01      Medium               Tier 3   \n",
       "2                1999-01-01      Medium               Tier 1   \n",
       "3                1998-01-01         NaN               Tier 3   \n",
       "4                1987-01-01        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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting the datatypes\n",
    "\n",
    "dataset[\"Item_Fat_Content\"]=dataset[\"Item_Fat_Content\"].astype(\"category\")\n",
    "dataset[\"Item_Type\"]=dataset[\"Item_Type\"].astype(\"category\")\n",
    "dataset[\"Outlet_Identifier\"]=dataset[\"Outlet_Identifier\"].astype(\"category\")\n",
    "dataset[\"Outlet_Establishment_Year\"]=pd.to_datetime(dataset[\"Outlet_Establishment_Year\"],format=\"%Y\")\n",
    "dataset[\"Outlet_Size\"]=dataset[\"Outlet_Size\"].astype(\"category\")\n",
    "dataset[\"Outlet_Location_Type\"]=dataset[\"Outlet_Location_Type\"].astype(\"category\")\n",
    "dataset[\"Outlet_Type\"]=dataset[\"Outlet_Type\"].astype(\"category\")\n",
    "display(dataset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                      object\n",
       "Item_Weight                         float64\n",
       "Item_Fat_Content                   category\n",
       "Item_Visibility                     float64\n",
       "Item_Type                          category\n",
       "Item_MRP                            float64\n",
       "Outlet_Identifier                  category\n",
       "Outlet_Establishment_Year    datetime64[ns]\n",
       "Outlet_Size                        category\n",
       "Outlet_Location_Type               category\n",
       "Outlet_Type                        category\n",
       "Item_Outlet_Sales                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# items=dataset[[\"Item_Identifier\",\"Item_Weight\",\"Item_Fat_Content\",\"Item_Visibility\",\"Item_Type\",\"Item_MRP\"]]\n",
    "\n",
    "# outlets=dataset[['Outlet_Identifier','Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type','Outlet_Type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              0.000000\n",
       "Item_Weight                  0.171653\n",
       "Item_Fat_Content             0.000000\n",
       "Item_Visibility              0.000000\n",
       "Item_Type                    0.000000\n",
       "Item_MRP                     0.000000\n",
       "Outlet_Identifier            0.000000\n",
       "Outlet_Establishment_Year    0.000000\n",
       "Outlet_Size                  0.282764\n",
       "Outlet_Location_Type         0.000000\n",
       "Outlet_Type                  0.000000\n",
       "Item_Outlet_Sales            0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()/dataset.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FDA15', 'DRC01', 'FDN15', ..., 'NCF55', 'NCW30', 'NCW05'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Item_Identifier.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>FDE51</td>\n",
       "      <td>[5.925]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>FDE53</td>\n",
       "      <td>[10.895]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>FDE56</td>\n",
       "      <td>[17.25]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>FDE57</td>\n",
       "      <td>[9.6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>FDE58</td>\n",
       "      <td>[18.5]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Item_Identifier Item_Weight\n",
       "350           FDE51     [5.925]\n",
       "351           FDE53    [10.895]\n",
       "352           FDE56     [17.25]\n",
       "353           FDE57       [9.6]\n",
       "354           FDE58      [18.5]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_item_weight=dataset[~dataset.Item_Weight.isnull()][[\"Item_Identifier\",\"Item_Weight\"]].groupby(\"Item_Identifier\")[\"Item_Weight\"].unique()\n",
    "dataset_item_weight=pd.DataFrame(dataset_item_weight).reset_index()\n",
    "dataset_item_weight.iloc[350:355,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DRA12': 11.6,\n",
       " 'DRA24': 19.35,\n",
       " 'DRA59': 8.27,\n",
       " 'DRB01': 7.39,\n",
       " 'DRB13': 6.115,\n",
       " 'DRB24': 8.785,\n",
       " 'DRB25': 12.3,\n",
       " 'DRB48': 16.75,\n",
       " 'DRC01': 5.92,\n",
       " 'DRC12': 17.85,\n",
       " 'DRC13': 8.26,\n",
       " 'DRC24': 17.85,\n",
       " 'DRC25': 5.73,\n",
       " 'DRC27': 13.8,\n",
       " 'DRC36': 13.0,\n",
       " 'DRC49': 8.67,\n",
       " 'DRD01': 12.1,\n",
       " 'DRD12': 6.96,\n",
       " 'DRD13': 15.0,\n",
       " 'DRD15': 10.6,\n",
       " 'DRD24': 13.85,\n",
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       " 'DRD37': 9.8,\n",
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       " 'DRD60': 15.7,\n",
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       " 'DRE03': 19.6,\n",
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       " 'DRG03': 14.5,\n",
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       " 'DRG13': 17.25,\n",
       " 'DRG15': 6.13,\n",
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       " 'DRI49': 14.15,\n",
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       " 'DRJ59': 11.65,\n",
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       " 'DRK49': 14.15,\n",
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       " 'DRL11': 10.5,\n",
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       " 'DRL60': 8.52,\n",
       " 'DRM11': 6.57,\n",
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       " 'DRM48': 15.2,\n",
       " 'DRM49': 6.11,\n",
       " 'DRM59': 5.88,\n",
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       " 'DRO59': 11.8,\n",
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       " 'FDE41': 9.195,\n",
       " 'FDE44': 14.65,\n",
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       " 'FDE57': 9.6,\n",
       " 'FDE58': 18.5,\n",
       " 'FDE59': 12.15,\n",
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       " 'FDF24': 15.5,\n",
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       " 'FDF52': 9.3,\n",
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       " 'FDG04': 13.1,\n",
       " 'FDG05': 11.0,\n",
       " 'FDG08': 13.15,\n",
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       " 'FDG21': 17.35,\n",
       " 'FDG22': 17.6,\n",
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       " 'FDG32': 19.85,\n",
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       " 'FDG34': 11.5,\n",
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       " 'FDG45': 8.1,\n",
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       " 'FDG47': 12.8,\n",
       " 'FDG50': 7.405,\n",
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       " 'FDG53': 10.0,\n",
       " 'FDG56': 13.3,\n",
       " 'FDG57': 14.7,\n",
       " 'FDG58': 10.695,\n",
       " 'FDG59': 15.85,\n",
       " 'FDG60': 20.35,\n",
       " 'FDH02': 7.27,\n",
       " 'FDH04': 6.115,\n",
       " 'FDH05': 14.35,\n",
       " 'FDH08': 7.51,\n",
       " 'FDH09': 12.6,\n",
       " 'FDH10': 21.0,\n",
       " 'FDH12': 9.6,\n",
       " 'FDH14': 17.1,\n",
       " 'FDH16': 10.5,\n",
       " 'FDH17': 16.2,\n",
       " 'FDH19': 19.35,\n",
       " 'FDH20': 16.1,\n",
       " 'FDH21': 10.395,\n",
       " 'FDH22': 6.405,\n",
       " 'FDH24': 20.7,\n",
       " 'FDH26': 19.25,\n",
       " 'FDH27': 7.075,\n",
       " 'FDH28': 15.85,\n",
       " 'FDH31': 12.0,\n",
       " 'FDH32': 12.8,\n",
       " 'FDH33': 12.85,\n",
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       " 'FDH40': 11.6,\n",
       " 'FDH41': 9.0,\n",
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       " 'FDH45': 15.1,\n",
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       " 'FDH48': 13.5,\n",
       " 'FDH50': 15.0,\n",
       " 'FDH52': 9.42,\n",
       " 'FDH53': 20.5,\n",
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       " 'FDH60': 19.7,\n",
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       " 'FDI04': 13.65,\n",
       " 'FDI05': 8.35,\n",
       " 'FDI07': 12.35,\n",
       " 'FDI08': 18.2,\n",
       " 'FDI09': 20.75,\n",
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       " 'FDI12': 9.395,\n",
       " 'FDI14': 14.1,\n",
       " 'FDI15': 13.8,\n",
       " 'FDI16': 14.0,\n",
       " 'FDI19': 15.1,\n",
       " 'FDI20': 19.1,\n",
       " 'FDI21': 5.59,\n",
       " 'FDI22': 12.6,\n",
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       " 'FDI26': 5.94,\n",
       " 'FDI27': 8.71,\n",
       " 'FDI28': 14.3,\n",
       " 'FDI32': 17.7,\n",
       " 'FDI33': 16.5,\n",
       " 'FDI34': 10.65,\n",
       " 'FDI35': 14.0,\n",
       " 'FDI36': 12.5,\n",
       " 'FDI38': 13.35,\n",
       " 'FDI40': 11.5,\n",
       " 'FDI41': 18.5,\n",
       " 'FDI44': 16.1,\n",
       " 'FDI45': 13.1,\n",
       " 'FDI46': 9.5,\n",
       " 'FDI48': 11.85,\n",
       " 'FDI50': 8.42,\n",
       " 'FDI52': 18.7,\n",
       " 'FDI53': 8.895,\n",
       " 'FDI56': 7.325,\n",
       " 'FDI57': 19.85,\n",
       " 'FDI58': 7.64,\n",
       " 'FDI60': 7.22,\n",
       " 'FDJ02': 17.2,\n",
       " 'FDJ03': 12.35,\n",
       " 'FDJ04': 18.0,\n",
       " 'FDJ07': 7.26,\n",
       " 'FDJ08': 11.1,\n",
       " 'FDJ09': 15.0,\n",
       " 'FDJ10': 5.095,\n",
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       " 'FDJ20': 20.7,\n",
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       " 'FDJ26': 15.3,\n",
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       " 'FDJ32': 10.695,\n",
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       " 'FDJ38': 8.6,\n",
       " 'FDJ40': 13.6,\n",
       " 'FDJ41': 6.85,\n",
       " 'FDJ44': 12.3,\n",
       " 'FDJ45': 17.75,\n",
       " 'FDJ46': 11.1,\n",
       " 'FDJ48': 11.3,\n",
       " 'FDJ50': 8.645,\n",
       " 'FDJ52': 7.145,\n",
       " 'FDJ53': 10.5,\n",
       " 'FDJ55': 12.8,\n",
       " 'FDJ56': 8.985,\n",
       " 'FDJ57': 7.42,\n",
       " 'FDJ58': 15.6,\n",
       " 'FDJ60': 19.35,\n",
       " 'FDK02': 12.5,\n",
       " 'FDK03': 12.6,\n",
       " 'FDK04': 7.36,\n",
       " 'FDK08': 9.195,\n",
       " 'FDK09': 15.2,\n",
       " 'FDK10': 5.785,\n",
       " 'FDK14': 6.98,\n",
       " 'FDK15': 10.8,\n",
       " 'FDK16': 9.065,\n",
       " 'FDK20': 12.6,\n",
       " 'FDK21': 7.905,\n",
       " 'FDK22': 9.8,\n",
       " 'FDK24': 9.195,\n",
       " 'FDK25': 11.6,\n",
       " 'FDK26': 5.46,\n",
       " 'FDK27': 11.0,\n",
       " 'FDK28': 5.695,\n",
       " 'FDK32': 16.25,\n",
       " 'FDK33': 17.85,\n",
       " 'FDK34': 13.35,\n",
       " 'FDK36': 7.09,\n",
       " 'FDK38': 6.65,\n",
       " 'FDK40': 7.035,\n",
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       " 'FDK43': 9.8,\n",
       " 'FDK44': 16.6,\n",
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       " 'FDK55': 18.5,\n",
       " 'FDK56': 9.695,\n",
       " 'FDK58': 11.35,\n",
       " 'FDK60': 16.5,\n",
       " 'FDL02': 20.0,\n",
       " 'FDL03': 19.25,\n",
       " 'FDL04': 19.0,\n",
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       " 'FDL10': 8.395,\n",
       " 'FDL12': 15.85,\n",
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       " 'FDL14': 8.115,\n",
       " 'FDL15': 17.85,\n",
       " 'FDL16': 12.85,\n",
       " 'FDL20': 17.1,\n",
       " 'FDL21': 15.85,\n",
       " 'FDL22': 16.85,\n",
       " 'FDL24': 10.3,\n",
       " 'FDL25': 6.92,\n",
       " 'FDL26': 18.0,\n",
       " 'FDL27': 6.17,\n",
       " 'FDL28': 10.0,\n",
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       " 'FDL34': 16.0,\n",
       " 'FDL36': 15.1,\n",
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       " 'FDL43': 10.1,\n",
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       " 'FDM08': 10.1,\n",
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       " 'FDM15': 11.8,\n",
       " 'FDM16': 8.155,\n",
       " 'FDM20': 10.0,\n",
       " 'FDM21': 20.2,\n",
       " 'FDM22': 14.0,\n",
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       " 'FDN20': 19.35,\n",
       " 'FDN21': 18.6,\n",
       " 'FDN22': 18.85,\n",
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       " 'FDN31': 11.5,\n",
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       " 'FDN34': 15.6,\n",
       " 'FDN38': 6.615,\n",
       " 'FDN39': 19.35,\n",
       " 'FDN40': 5.88,\n",
       " 'FDN44': 13.15,\n",
       " 'FDN45': 19.35,\n",
       " 'FDN46': 7.21,\n",
       " 'FDN48': 13.35,\n",
       " 'FDN49': 17.25,\n",
       " 'FDN50': 16.85,\n",
       " 'FDN51': 17.85,\n",
       " 'FDN56': 5.46,\n",
       " 'FDN57': 18.25,\n",
       " 'FDN58': 13.8,\n",
       " 'FDN60': 15.1,\n",
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       " 'FDO03': 10.395,\n",
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       " 'FDP08': 20.5,\n",
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       " 'FDP11': 15.85,\n",
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       " 'FDP13': 8.1,\n",
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       " 'FDP19': 11.5,\n",
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       " 'FDP21': 7.42,\n",
       " 'FDP22': 14.65,\n",
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       " 'FDP57': 17.5,\n",
       " 'FDP58': 11.1,\n",
       " 'FDP59': 20.85,\n",
       " 'FDP60': 17.35,\n",
       " 'FDQ01': 19.7,\n",
       " 'FDQ03': 15.0,\n",
       " 'FDQ04': 6.4,\n",
       " 'FDQ07': 15.1,\n",
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       " 'FDQ09': 7.235,\n",
       " 'FDQ10': 12.85,\n",
       " 'FDQ11': 5.695,\n",
       " 'FDQ12': 12.65,\n",
       " 'FDQ13': 11.1,\n",
       " 'FDQ14': 9.27,\n",
       " 'FDQ15': 20.35,\n",
       " 'FDQ16': 19.7,\n",
       " 'FDQ19': 7.35,\n",
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       " 'FDQ21': 21.25,\n",
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       " 'FDR02': 16.7,\n",
       " 'FDR03': 15.7,\n",
       " 'FDR04': 7.075,\n",
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       " 'FDR08': 18.7,\n",
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       " 'FDR16': 5.845,\n",
       " 'FDR19': 13.5,\n",
       " 'FDR20': 20.0,\n",
       " 'FDR21': 19.7,\n",
       " 'FDR22': 19.35,\n",
       " 'FDR23': 15.85,\n",
       " 'FDR24': 17.35,\n",
       " 'FDR25': 17.0,\n",
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       " 'FDR32': 6.78,\n",
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       " 'FDR34': 17.0,\n",
       " 'FDR35': 12.5,\n",
       " 'FDR36': 6.715,\n",
       " 'FDR37': 16.5,\n",
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       " 'FDR45': 10.8,\n",
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       " 'FDS20': 8.85,\n",
       " 'FDS21': 19.85,\n",
       " 'FDS22': 16.85,\n",
       " 'FDS23': 4.635,\n",
       " 'FDS24': 20.85,\n",
       " 'FDS25': 6.885,\n",
       " 'FDS26': 20.35,\n",
       " 'FDS27': 10.195,\n",
       " 'FDS28': 8.18,\n",
       " 'FDS31': 13.1,\n",
       " 'FDS32': 17.75,\n",
       " 'FDS33': 6.67,\n",
       " 'FDS34': 19.35,\n",
       " 'FDS35': 9.3,\n",
       " 'FDS36': 8.38,\n",
       " 'FDS37': 7.655,\n",
       " 'FDS39': 6.895,\n",
       " 'FDS40': 15.35,\n",
       " 'FDS43': 11.65,\n",
       " 'FDS44': 12.65,\n",
       " 'FDS45': 5.175,\n",
       " 'FDS46': 17.6,\n",
       " 'FDS47': 16.75,\n",
       " 'FDS48': 15.15,\n",
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       " 'FDS50': 17.0,\n",
       " 'FDS51': 13.35,\n",
       " 'FDS52': 8.89,\n",
       " 'FDS55': 7.02,\n",
       " 'FDS56': 5.785,\n",
       " 'FDS57': 15.5,\n",
       " 'FDS58': 9.285,\n",
       " 'FDS59': 14.8,\n",
       " 'FDS60': 20.85,\n",
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       " 'FDT02': 12.6,\n",
       " 'FDT03': 21.25,\n",
       " 'FDT04': 17.25,\n",
       " 'FDT07': 5.82,\n",
       " 'FDT08': 13.65,\n",
       " 'FDT09': 15.15,\n",
       " 'FDT10': 16.7,\n",
       " 'FDT11': 5.94,\n",
       " 'FDT12': 6.215,\n",
       " 'FDT13': 14.85,\n",
       " 'FDT14': 10.695,\n",
       " 'FDT15': 12.15,\n",
       " 'FDT16': 9.895,\n",
       " 'FDT19': 7.59,\n",
       " 'FDT20': 10.5,\n",
       " 'FDT21': 7.42,\n",
       " 'FDT22': 10.395,\n",
       " 'FDT23': 7.72,\n",
       " 'FDT24': 12.35,\n",
       " 'FDT25': 7.5,\n",
       " 'FDT26': 18.85,\n",
       " 'FDT27': 11.395,\n",
       " 'FDT28': 13.3,\n",
       " 'FDT31': 19.75,\n",
       " 'FDT32': 19.0,\n",
       " 'FDT33': 7.81,\n",
       " 'FDT34': 9.3,\n",
       " 'FDT35': 19.85,\n",
       " 'FDT36': 12.3,\n",
       " 'FDT37': 14.15,\n",
       " 'FDT38': 18.7,\n",
       " 'FDT39': 6.26,\n",
       " 'FDT40': 5.985,\n",
       " 'FDT43': 16.35,\n",
       " 'FDT44': 16.6,\n",
       " 'FDT45': 15.85,\n",
       " 'FDT46': 11.35,\n",
       " 'FDT47': 5.26,\n",
       " 'FDT48': 4.92,\n",
       " 'FDT49': 7.0,\n",
       " 'FDT50': 6.75,\n",
       " 'FDT51': 11.65,\n",
       " 'FDT52': 9.695,\n",
       " 'FDT55': 13.6,\n",
       " 'FDT56': 16.0,\n",
       " 'FDT57': 15.2,\n",
       " 'FDT58': 9.0,\n",
       " 'FDT59': 13.65,\n",
       " 'FDT60': 12.0,\n",
       " 'FDU01': 20.25,\n",
       " 'FDU02': 13.35,\n",
       " 'FDU03': 18.7,\n",
       " 'FDU04': 7.93,\n",
       " 'FDU07': 11.1,\n",
       " 'FDU08': 10.3,\n",
       " 'FDU09': 7.71,\n",
       " 'FDU10': 10.1,\n",
       " 'FDU11': 4.785,\n",
       " 'FDU12': 15.5,\n",
       " 'FDU13': 8.355,\n",
       " 'FDU14': 17.75,\n",
       " 'FDU15': 13.65,\n",
       " 'FDU16': 19.25,\n",
       " 'FDU19': 8.77,\n",
       " 'FDU20': 19.35,\n",
       " 'FDU21': 11.8,\n",
       " 'FDU22': 12.35,\n",
       " 'FDU23': 12.15,\n",
       " 'FDU24': 6.78,\n",
       " 'FDU25': 12.35,\n",
       " 'FDU26': 16.7,\n",
       " 'FDU27': 18.6,\n",
       " 'FDU28': 19.2,\n",
       " 'FDU31': 10.5,\n",
       " 'FDU32': 8.785,\n",
       " 'FDU33': 7.63,\n",
       " 'FDU34': 18.25,\n",
       " 'FDU35': 6.44,\n",
       " 'FDU36': 6.15,\n",
       " 'FDU37': 9.5,\n",
       " 'FDU38': 10.8,\n",
       " 'FDU39': 18.85,\n",
       " 'FDU40': 20.85,\n",
       " 'FDU43': 19.35,\n",
       " 'FDU44': 12.15,\n",
       " 'FDU45': 15.6,\n",
       " ...}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_dict={}\n",
    "\n",
    "for i,j in zip(dataset_item_weight.Item_Identifier,dataset_item_weight.Item_Weight):\n",
    "    a=list(j)\n",
    "    \n",
    "    if(len(a)==0):\n",
    "        weight_dict.update({i:np.nan})\n",
    "    else:\n",
    "        weight_dict.update({i:a[0]})\n",
    "    \n",
    "\n",
    "weight_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for a,b in weight_dict.items():\n",
    "    \n",
    "    for i,j in zip(dataset[dataset.Item_Identifier==a].index,dataset[dataset.Item_Identifier==a]):\n",
    "        dataset.loc[i,\"Item_Weight\"]=b\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have successfully managed to fill the missing values for Item_Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                     4\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.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>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.920</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.200</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.930</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8518</th>\n",
       "      <td>FDF22</td>\n",
       "      <td>6.865</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.056783</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>214.5218</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2778.3834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>FDS36</td>\n",
       "      <td>8.380</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.046982</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>108.1570</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>549.2850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8520</th>\n",
       "      <td>NCJ29</td>\n",
       "      <td>10.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.035186</td>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>85.1224</td>\n",
       "      <td>OUT035</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>1193.1136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8521</th>\n",
       "      <td>FDN46</td>\n",
       "      <td>7.210</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.145221</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>103.1332</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>1845.5976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>DRG01</td>\n",
       "      <td>14.800</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.044878</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>75.4670</td>\n",
       "      <td>OUT046</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>765.6700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8523 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0              FDA15        9.300          Low Fat         0.016047   \n",
       "1              DRC01        5.920          Regular         0.019278   \n",
       "2              FDN15       17.500          Low Fat         0.016760   \n",
       "3              FDX07       19.200          Regular         0.000000   \n",
       "4              NCD19        8.930          Low Fat         0.000000   \n",
       "...              ...          ...              ...              ...   \n",
       "8518           FDF22        6.865          Low Fat         0.056783   \n",
       "8519           FDS36        8.380          Regular         0.046982   \n",
       "8520           NCJ29       10.600          Low Fat         0.035186   \n",
       "8521           FDN46        7.210          Regular         0.145221   \n",
       "8522           DRG01       14.800          Low Fat         0.044878   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                     Dairy  249.8092            OUT049   \n",
       "1               Soft Drinks   48.2692            OUT018   \n",
       "2                      Meat  141.6180            OUT049   \n",
       "3     Fruits and Vegetables  182.0950            OUT010   \n",
       "4                 Household   53.8614            OUT013   \n",
       "...                     ...       ...               ...   \n",
       "8518            Snack Foods  214.5218            OUT013   \n",
       "8519           Baking Goods  108.1570            OUT045   \n",
       "8520     Health and Hygiene   85.1224            OUT035   \n",
       "8521            Snack Foods  103.1332            OUT018   \n",
       "8522            Soft Drinks   75.4670            OUT046   \n",
       "\n",
       "     Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                   1999-01-01      Medium               Tier 1   \n",
       "1                   2009-01-01      Medium               Tier 3   \n",
       "2                   1999-01-01      Medium               Tier 1   \n",
       "3                   1998-01-01         NaN               Tier 3   \n",
       "4                   1987-01-01        High               Tier 3   \n",
       "...                        ...         ...                  ...   \n",
       "8518                1987-01-01        High               Tier 3   \n",
       "8519                2002-01-01         NaN               Tier 2   \n",
       "8520                2004-01-01       Small               Tier 2   \n",
       "8521                2009-01-01      Medium               Tier 3   \n",
       "8522                1997-01-01       Small               Tier 1   \n",
       "\n",
       "            Outlet_Type  Item_Outlet_Sales  \n",
       "0     Supermarket Type1          3735.1380  \n",
       "1     Supermarket Type2           443.4228  \n",
       "2     Supermarket Type1          2097.2700  \n",
       "3         Grocery Store           732.3800  \n",
       "4     Supermarket Type1           994.7052  \n",
       "...                 ...                ...  \n",
       "8518  Supermarket Type1          2778.3834  \n",
       "8519  Supermarket Type1           549.2850  \n",
       "8520  Supermarket Type1          1193.1136  \n",
       "8521  Supermarket Type2          1845.5976  \n",
       "8522  Supermarket Type1           765.6700  \n",
       "\n",
       "[8523 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicates in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[OUT010, OUT045, OUT017]\n",
       "Categories (3, object): [OUT010, OUT045, OUT017]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset[\"Outlet_Size\"].isnull()][\"Outlet_Identifier\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       True\n",
       "28      True\n",
       "30      True\n",
       "45      True\n",
       "65      True\n",
       "        ... \n",
       "8400    True\n",
       "8432    True\n",
       "8473    True\n",
       "8486    True\n",
       "8509    True\n",
       "Name: Outlet_Size, Length: 555, dtype: bool"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "555\n"
     ]
    }
   ],
   "source": [
    "display(dataset[dataset.Outlet_Identifier==\"OUT010\"][\"Outlet_Size\"].isnull())\n",
    "print(dataset[dataset.Outlet_Identifier==\"OUT010\"][\"Outlet_Size\"].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8       True\n",
       "33      True\n",
       "46      True\n",
       "47      True\n",
       "56      True\n",
       "        ... \n",
       "8483    True\n",
       "8502    True\n",
       "8508    True\n",
       "8514    True\n",
       "8519    True\n",
       "Name: Outlet_Size, Length: 929, dtype: bool"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "929\n"
     ]
    }
   ],
   "source": [
    "display(dataset[dataset.Outlet_Identifier==\"OUT045\"][\"Outlet_Size\"].isnull())\n",
    "print(dataset[dataset.Outlet_Identifier==\"OUT045\"][\"Outlet_Size\"].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9       True\n",
       "25      True\n",
       "53      True\n",
       "54      True\n",
       "73      True\n",
       "        ... \n",
       "8493    True\n",
       "8494    True\n",
       "8496    True\n",
       "8500    True\n",
       "8501    True\n",
       "Name: Outlet_Size, Length: 926, dtype: bool"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "926\n"
     ]
    }
   ],
   "source": [
    "display(dataset[dataset.Outlet_Identifier==\"OUT017\"][\"Outlet_Size\"].isnull())\n",
    "print(dataset[dataset.Outlet_Identifier==\"OUT017\"][\"Outlet_Size\"].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three Outlets whose Size is not Available to us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "items_dataset=dataset[['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
    "       'Item_Type', 'Item_MRP']]\n",
    "outlet_dataset=dataset[['Outlet_Identifier',\n",
    "       'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
    "       'Outlet_Type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examining each of the Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ITEMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0                  Dairy  249.8092  \n",
       "1            Soft Drinks   48.2692  \n",
       "2                   Meat  141.6180  \n",
       "3  Fruits and Vegetables  182.0950  \n",
       "4              Household   53.8614  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:844: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:965: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Item_Group</th>\n",
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       "  </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>FDA</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>DRC</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>FDN</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>FDX</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>NCD</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 Item_Group  \n",
       "0                  Dairy  249.8092        FDA  \n",
       "1            Soft Drinks   48.2692        DRC  \n",
       "2                   Meat  141.6180        FDN  \n",
       "3  Fruits and Vegetables  182.0950        FDX  \n",
       "4              Household   53.8614        NCD  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_dataset.loc[:,\"Item_Group\"]=list(map(lambda x: x[:3],items_dataset.Item_Identifier))\n",
    "items_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item_Identifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FDW13    10\n",
       "FDG33    10\n",
       "FDD38     9\n",
       "FDQ40     9\n",
       "NCY18     9\n",
       "         ..\n",
       "FDE52     1\n",
       "FDO33     1\n",
       "FDC23     1\n",
       "DRF48     1\n",
       "FDK57     1\n",
       "Name: Item_Identifier, Length: 1559, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_dataset[\"Item_Identifier\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We have 1559 Unique Item_Identifiers** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Low Fat\n",
       "1       Regular\n",
       "2       Low Fat\n",
       "3       Regular\n",
       "4       Low Fat\n",
       "         ...   \n",
       "8518    Low Fat\n",
       "8519    Regular\n",
       "8520    Low Fat\n",
       "8521    Regular\n",
       "8522    Low Fat\n",
       "Name: Item_Fat_Content, Length: 8523, dtype: category\n",
       "Categories (2, object): [Low Fat, Regular]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_dataset[\"Item_Fat_Content\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    0.647307\n",
       "Regular    0.352693\n",
       "Name: Item_Fat_Content, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(palette=\"Set2\")\n",
    "sns.countplot(y=\"Item_Fat_Content\",data=items_dataset,)\n",
    "plt.xlim(0,6000)\n",
    "\n",
    "display(items_dataset.Item_Fat_Content.value_counts(normalize=True))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**65% of the Items are of Low Fat whereas 35% are Regular**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DRA12</th>\n",
       "      <td>11.600</td>\n",
       "      <td>0.031956</td>\n",
       "      <td>141.865400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRA24</th>\n",
       "      <td>19.350</td>\n",
       "      <td>0.048062</td>\n",
       "      <td>164.086800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRA59</th>\n",
       "      <td>8.270</td>\n",
       "      <td>0.134718</td>\n",
       "      <td>185.179900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB01</th>\n",
       "      <td>7.390</td>\n",
       "      <td>0.082126</td>\n",
       "      <td>189.586333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB13</th>\n",
       "      <td>6.115</td>\n",
       "      <td>0.008002</td>\n",
       "      <td>189.693000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ30</th>\n",
       "      <td>6.590</td>\n",
       "      <td>0.024956</td>\n",
       "      <td>120.752657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ41</th>\n",
       "      <td>19.850</td>\n",
       "      <td>0.051623</td>\n",
       "      <td>125.630400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ42</th>\n",
       "      <td>10.500</td>\n",
       "      <td>0.009044</td>\n",
       "      <td>236.644800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ53</th>\n",
       "      <td>9.600</td>\n",
       "      <td>0.027775</td>\n",
       "      <td>188.321400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ54</th>\n",
       "      <td>14.650</td>\n",
       "      <td>0.080426</td>\n",
       "      <td>162.255200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1559 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight  Item_Visibility    Item_MRP\n",
       "Item_Identifier                                          \n",
       "DRA12                 11.600         0.031956  141.865400\n",
       "DRA24                 19.350         0.048062  164.086800\n",
       "DRA59                  8.270         0.134718  185.179900\n",
       "DRB01                  7.390         0.082126  189.586333\n",
       "DRB13                  6.115         0.008002  189.693000\n",
       "...                      ...              ...         ...\n",
       "NCZ30                  6.590         0.024956  120.752657\n",
       "NCZ41                 19.850         0.051623  125.630400\n",
       "NCZ42                 10.500         0.009044  236.644800\n",
       "NCZ53                  9.600         0.027775  188.321400\n",
       "NCZ54                 14.650         0.080426  162.255200\n",
       "\n",
       "[1559 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_dataset.groupby(\"Item_Identifier\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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