Bigmart Sales Prediction Analysis - Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Information\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.\n",
    "\n",
    "Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.\n",
    "\n",
    "\n",
    "Variable | Description\n",
    "----------|--------------\n",
    "Item_Identifier | Unique product ID\n",
    "Item_Weight | Weight of product\n",
    "Item_Fat_Content | Whether the product is low fat or not\n",
    "Item_Visibility | The % of total display area of all products in a    store allocated to the particular product\n",
    "Item_Type | The category to which the product belongs\n",
    "Item_MRP | Maximum Retail Price (list price) of the product\n",
    "Outlet_Identifier | Unique store ID\n",
    "Outlet_Establishment_Year | The year in which store was established\n",
    "Outlet_Size | The size of the store in terms of ground area covered\n",
    "Outlet_Location_Type | The type of city in which the store is located\n",
    "Outlet_Type | Whether the outlet is just a grocery store or some sort of supermarket\n",
    "Item_Outlet_Sales | Sales of the product in the particulat store. This is the outcome variable to be predicted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7060.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>140.992782</td>\n",
       "      <td>1997.831867</td>\n",
       "      <td>2181.288914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.643456</td>\n",
       "      <td>0.051598</td>\n",
       "      <td>62.275067</td>\n",
       "      <td>8.371760</td>\n",
       "      <td>1706.499616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.290000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>33.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.773750</td>\n",
       "      <td>0.026989</td>\n",
       "      <td>93.826500</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>834.247400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>143.012800</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1794.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.850000</td>\n",
       "      <td>0.094585</td>\n",
       "      <td>185.643700</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3101.296400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>266.888400</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>13086.964800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
       "count  7060.000000      8523.000000  8523.000000                8523.000000   \n",
       "mean     12.857645         0.066132   140.992782                1997.831867   \n",
       "std       4.643456         0.051598    62.275067                   8.371760   \n",
       "min       4.555000         0.000000    31.290000                1985.000000   \n",
       "25%       8.773750         0.026989    93.826500                1987.000000   \n",
       "50%      12.600000         0.053931   143.012800                1999.000000   \n",
       "75%      16.850000         0.094585   185.643700                2004.000000   \n",
       "max      21.350000         0.328391   266.888400                2009.000000   \n",
       "\n",
       "       Item_Outlet_Sales  \n",
       "count        8523.000000  \n",
       "mean         2181.288914  \n",
       "std          1706.499616  \n",
       "min            33.290000  \n",
       "25%           834.247400  \n",
       "50%          1794.331000  \n",
       "75%          3101.296400  \n",
       "max         13086.964800  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical info\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# datatype of attributes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              1559\n",
       "Item_Weight                   416\n",
       "Item_Fat_Content                5\n",
       "Item_Visibility              7880\n",
       "Item_Type                      16\n",
       "Item_MRP                     5938\n",
       "Outlet_Identifier              10\n",
       "Outlet_Establishment_Year       9\n",
       "Outlet_Size                     4\n",
       "Outlet_Location_Type            3\n",
       "Outlet_Type                     4\n",
       "Item_Outlet_Sales            3493\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check unique values in dataset\n",
    "df.apply(lambda x: len(x.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                  1463\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Identifier',\n",
       " 'Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for categorical attributes\n",
    "cat_col = []\n",
    "for x in df.dtypes.index:\n",
    "    if df.dtypes[x] == 'object':\n",
    "        cat_col.append(x)\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col.remove('Item_Identifier')\n",
    "cat_col.remove('Outlet_Identifier')\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Fat_Content\n",
      "Item_Fat_Content\n",
      "Low Fat    5089\n",
      "Regular    2889\n",
      "LF          316\n",
      "reg         117\n",
      "low fat     112\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Item_Type\n",
      "Item_Type\n",
      "Fruits and Vegetables    1232\n",
      "Snack Foods              1200\n",
      "Household                 910\n",
      "Frozen Foods              856\n",
      "Dairy                     682\n",
      "Canned                    649\n",
      "Baking Goods              648\n",
      "Health and Hygiene        520\n",
      "Soft Drinks               445\n",
      "Meat                      425\n",
      "Breads                    251\n",
      "Hard Drinks               214\n",
      "Others                    169\n",
      "Starchy Foods             148\n",
      "Breakfast                 110\n",
      "Seafood                    64\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Outlet_Size\n",
      "Outlet_Size\n",
      "Medium    2793\n",
      "Small     2388\n",
      "High       932\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Outlet_Location_Type\n",
      "Outlet_Location_Type\n",
      "Tier 3    3350\n",
      "Tier 2    2785\n",
      "Tier 1    2388\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Outlet_Type\n",
      "Outlet_Type\n",
      "Supermarket Type1    5577\n",
      "Grocery Store        1083\n",
      "Supermarket Type3     935\n",
      "Supermarket Type2     928\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print the categorical columns\n",
    "for col in cat_col:\n",
    "    print(col)\n",
    "    print(df[col].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DRA12</th>\n",
       "      <td>11.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRA24</th>\n",
       "      <td>19.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRA59</th>\n",
       "      <td>8.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB01</th>\n",
       "      <td>7.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB13</th>\n",
       "      <td>6.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ30</th>\n",
       "      <td>6.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ41</th>\n",
       "      <td>19.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ42</th>\n",
       "      <td>10.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ53</th>\n",
       "      <td>9.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ54</th>\n",
       "      <td>14.650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight\n",
       "Item_Identifier             \n",
       "DRA12                 11.600\n",
       "DRA24                 19.350\n",
       "DRA59                  8.270\n",
       "DRB01                  7.390\n",
       "DRB13                  6.115\n",
       "...                      ...\n",
       "NCZ30                  6.590\n",
       "NCZ41                 19.850\n",
       "NCZ42                 10.500\n",
       "NCZ53                  9.600\n",
       "NCZ54                 14.650\n",
       "\n",
       "[1555 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill the missing values\n",
    "item_weight_mean = df.pivot_table(values = \"Item_Weight\", index = 'Item_Identifier')\n",
    "item_weight_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "8518    False\n",
       "8519    False\n",
       "8520    False\n",
       "8521    False\n",
       "8522    False\n",
       "Name: Item_Weight, Length: 8523, dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss_bool = df['Item_Weight'].isnull()\n",
    "miss_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, item in enumerate(df['Item_Identifier']):\n",
    "    if miss_bool[i]:\n",
    "        if item in item_weight_mean:\n",
    "            df['Item_Weight'][i] = item_weight_mean.loc[item]['Item_Weight']\n",
    "        else:\n",
    "            df['Item_Weight'][i] = np.mean(df['Item_Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Item_Weight'].isnull().sum()"
   ]
  },
  {
   "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>Outlet_Type</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <th>Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>Small</td>\n",
       "      <td>Small</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2  \\\n",
       "Outlet_Size         Small             Small            Medium   \n",
       "\n",
       "Outlet_Type Supermarket Type3  \n",
       "Outlet_Size            Medium  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlet_size_mode = df.pivot_table(values='Outlet_Size', columns='Outlet_Type', aggfunc=(lambda x: x.mode()[0]))\n",
    "outlet_size_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "miss_bool = df['Outlet_Size'].isnull()\n",
    "df.loc[miss_bool, 'Outlet_Size'] = df.loc[miss_bool, 'Outlet_Type'].apply(lambda x: outlet_size_mode[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Outlet_Size'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "526"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Item_Visibility']==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace zeros with mean\n",
    "df.loc[:, 'Item_Visibility'].replace([0], [df['Item_Visibility'].mean()], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df['Item_Visibility']==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content\n",
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine item fat content\n",
    "df['Item_Fat_Content'] = df['Item_Fat_Content'].replace({'LF':'Low Fat', 'reg':'Regular', 'low fat':'Low Fat'})\n",
    "df['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of New Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       FD\n",
       "1       DR\n",
       "2       FD\n",
       "3       FD\n",
       "4       NC\n",
       "        ..\n",
       "8518    FD\n",
       "8519    FD\n",
       "8520    NC\n",
       "8521    FD\n",
       "8522    DR\n",
       "Name: New_Item_Type, Length: 8523, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['New_Item_Type'] = df['Item_Identifier'].apply(lambda x: x[:2])\n",
    "df['New_Item_Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New_Item_Type\n",
       "Food              6125\n",
       "Non-Consumable    1599\n",
       "Drinks             799\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['New_Item_Type'] = df['New_Item_Type'].map({'FD':'Food', 'NC':'Non-Consumable', 'DR':'Drinks'})\n",
    "df['New_Item_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content\n",
       "Low Fat       3918\n",
       "Regular       3006\n",
       "Non-Edible    1599\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['New_Item_Type']=='Non-Consumable', 'Item_Fat_Content'] = 'Non-Edible'\n",
    "df['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create small values for establishment year\n",
    "df['Outlet_Years'] = 2013 - df['Outlet_Establishment_Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       14\n",
       "1        4\n",
       "2       14\n",
       "3       15\n",
       "4       26\n",
       "        ..\n",
       "8518    26\n",
       "8519    11\n",
       "8520     9\n",
       "8521     4\n",
       "8522    16\n",
       "Name: Outlet_Years, Length: 8523, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Outlet_Years']"
   ]
  },
  {
   "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",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>Food</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>Food</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>Food</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>Non-Consumable</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.066132   \n",
       "4           NCD19         8.93       Non-Edible         0.066132   \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       Small               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales   New_Item_Type  Outlet_Years  \n",
       "0  Supermarket Type1          3735.1380            Food            14  \n",
       "1  Supermarket Type2           443.4228          Drinks             4  \n",
       "2  Supermarket Type1          2097.2700            Food            14  \n",
       "3      Grocery Store           732.3800            Food            15  \n",
       "4  Supermarket Type1           994.7052  Non-Consumable            26  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Item_Weight', ylabel='Density'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_Weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Item_Visibility', ylabel='Density'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dMrPpy/3ZOH2+CmqlgGkDI6EQBHhqVHhoWAxUCgGniqpwqGE8EBERuZcOhZmMjAxH10ESEkURm36zLZKXEO642V839gwCYJshVdeJ+zYZzRYs+98pAMD4PqEI9NHanwszeCAx0hcAsDY1t9NqICIi6XToT/AePXrgpptuwqOPPopp06ZBp9M5ui7qJC3tal1UUYeM4mooFUKHB/62JD7UB0HeWpyvMuJgZok93Djaf38pQHGVEXqdCsO6BTR7fkC0L1KyS/GfowV45a4E6NTKTqmDiIik0aGWmSNHjmDAgAF44YUXEBoaiscffxwHDhxwdG3kJMcKbFs+dA/ycugveoUg4MaegQCA3aeLYbZaHfbejURRxMrdmQCAod0CoFQ0H7gcG+gFXw81KuvM9m0aiIjIfXQozPTt2xdvvfUW8vLysHLlShQWFmLUqFFISEjAW2+9hfPnpVlfhDqmMcz0CTM4/L37R/nCR6tCRZ0Zv+SWO/z9U7PLcDS3HBqVAkNiW14bRyEI6B/tCwBYm5rn8BqIiEha1zQAWKVSYcqUKfj666+xdOlSnDlzBgsXLkRkZCQefvhhFBQUOKpO6iQVtSbkltZCANA7zPHr96iUCgzvbuv62X262OEzilbuto3fmpQYfsWBywOibDO0tqefx/lKo0NrICIiaV1TmDl06BCeeuophIWF4a233sLChQtx5swZbNmyBXl5eZg0aZKj6qROcrzQ1ioT6edx1d2xO2pIrD9UCgH55XU4mOm4GUXnK4348Vfb3l2zR8Ze8dggHy0So3xhsYrYdKz1XdeJiEh+OhRm3nrrLfTr1w8jRoxAfn4+Vq1ahaysLPzlL39BXFwcRo4ciQ8//BCpqamOrpccLL2wEgDQO6zz9rDy0qowINrWMvLZLsfNhFt3OBdmq4j+Ub5ICL96F9nN8cEAgH1nSxxWAxERSa9Ds5mWL1+ORx55BHPmzLHvbn256OhofPrpp9dUHHUuqygi80INAKB7kHenftaI7gE4mFmCTccKkVNSgyh/z2t6P1EU8c0h21TrewdHtuk1wxr2m9p75gJEUYRwDascExGR6+hQy8zmzZvx0ksvNQsyoigiO9s29Vej0WDWrFnXXiF1msLyOtSaLNCoFAj37dy9i0L0OvQM9oZVBD7fk3nN73cktxyniqqgVSlwV2J4m17TP9oXWpUCxVVGnDnPDSiJiNxFh8JM9+7dUVzcfFXXkpISxMXFXXNR5BwZDTtKx/h7tjil2dFGdLdN0/7qYA6qjNe2V9K3KTkAgNv6hrZ5I0utSmnfqmHv2QvX9PlEROQ6OhRmWpuRUlVVxQX0ZKQxzHQL9HLK5/UM8Ua3IC9UGs349lBOh9+nzmTBxjTb9gv3Dopq12sbF9XbxzBDROQ22jVmZsGCBQBsGwm+/PLL8PS8OO7BYrFg//796N+/v0MLpM5hGy9jCzNxTgozCkHAIyPj8Mf1v2LlnkzMHB7boRahn34rREWdGeEGHUZ0b77i75UM7x4AbAb2n+W4GSIid9GuMHP48GEAtpaZX375BRqNxv6cRqNBYmIiFi5c6NgKqVMUVRpRU2+BWikgwu/aBuO2x9SBEfj7TyeRdaEGW04U4dY+Ie1+jy/32cZl3TckGop2hqHrIw3QqRUorqrH6aIq9HTg9g1ERCSNdoWZrVu3AgDmzJmDt99+G3p9503npc51cbyMl1PGyzTy1KjwwA3RWLH9DD7ddbbdYSb9XCUOZJZAqRBw35D2dTEBtnEzg2P8set0MfaevcAwQ0TkBjo0ZmblypUMMjLXGGZindTFdKlZI2KgVgrYd7YEBzPbt+bLl/uyAAC39g5BqKFj47Map2jvz+B6M0RE7qDNLTNTp05FcnIy9Ho9pk6desVj165de82FUecRRRFZTh4vc6kwgwfuHRyF1fuzsex/6fhy7rA2va6m3mzfW+nBYdEd/vz+DVsbHM0t6/B7EBGR62hzmDEYDPbBkgaD4zckJOepqDOjss4MhQBEdPL6Mq2ZN7YHvjmUg92nL+BARgluiGt5k8hLbUjLR6XRjJgAT4xsmObdEf0ibf/95pTUoqS6Hv5emqu8goiIXFmbw8zKlStb/J7kJ7+sFgAQ7KODRnVN23N1WISvB6YPjsKXDa0zqx+7cuuM0WzBe1tOAwBmDotp98DfSxk81IgL9EJGcTWO5pZhTMM2B0REJE8d2s6gtrYWoijap2ZnZWVh3bp16NOnD8aPH+/QAsnxckttYUaKVpnV+7Pt30f4ekApCNhz5gL+uO5X9AnXY8bQlruPvtyXjbyyWoTotXhwaMw113F9pAEZxdX4JbecYYaISOY69Gf5pEmTsGrVKgBAWVkZbrjhBrz55puYNGkSli9f7tACyfHyymz7MUX4SdPF1MjXU4ORPWzrxGxIy0NNfcurAlfWmfDeVlurzPPjroOHRnnNn90vwtbVdCS3/Jrfi4iIpNWhMJOamoobb7wRAPDtt98iNDQUWVlZWLVqFd555x2HFkiOJYoi8iRsmbncLb1DEOStRaXRjP8cLWjxmOXbzqCkuh7dg7wwbVDbNpW8msQoXwAcBExE5A46FGZqamrg42Nbn2PTpk2YOnUqFAoFhg0bhqysLIcWSI5VXmtCdb0FCgEdntrsSGqlAvcMioQAIC2nDJ/sPNtku4zV+7PxwbYzAIAXJ/SCSumYMT4J4XooBNvigYXldQ55TyIikkaHfjP06NED69evR05ODn766Sf7OJmioiKuP+Pi8hoG/4bodVA7KBhcq2h/T4yODwIA/OX741jw9RHsSD+P97acwu/X/QIAmDsqDhMS2r9acGs8NSpc17Bg3hG2zhARyVqHfpu9/PLLWLhwIWJjYzF06FAMHz4cgK2VZsCAAQ4tkBzLlbqYLnVr7xDc3i8MSoWAdYfz8PBnB/B/m9IBAHNGxuIPd/R2+D5KjeNm2NVERCRvHZrNNG3aNIwaNQoFBQVITEy0P37LLbdgypQpDiuO2u/S2UItaWyZkXrw7+UEQcCoHoF4aFg0lv54EkaTBf5eGoyND8bcG+M6ZUPI66N88U1KLo5yEDARkax1KMwAQGhoKEJDQ5s8dsMNN1xzQdR5RFG0T8uO9HXe5pLtMaJ7IDbM6/iCeO2R2LB43i955dxBm4hIxjoUZqqrq/HGG2/g559/RlFREaxWa5Pnz54965DiyLHKakyoNVmgFASE6LVSlyO5+FAfqBQCympMyC+vc7muNyIiapsOhZm5c+di+/btmDlzJsLCwvgXrUwUVthm7QTrtQ6bFSRnWpUSPYK9caKwEr/llTPMEBHJVIfCzA8//IDvv/8eI0eOdHQ91ImKGsOMD1tlGiWEG2xhJr8C4xNCr/4CIiJyOR3689zPzw/+/lffGJBcy7lKIwDbtGyySQi3LSXwW36FxJUQEVFHdSjMvPbaa3j55ZdRU1Pj6HqaMJvN+OMf/4i4uDh4eHigW7du+POf/9xsjA61zbmGlhmGmYv6NISZY/mc0UREJFcd6mZ68803cebMGYSEhCA2NhZqtbrJ86mpqQ4pbunSpVixYgU+//xzJCQk4NChQ5gzZw4MBgOee+45h3xGV2Gxiihiy0wzjWEmv7wOpdX18PPSSFwRERG1V4fCzOTJkx1cRsv27t2LSZMm4Y477gAAxMbGYs2aNTh06JBTPt+dXKg2wmIVoVEq4OupvvoLugi9To1of09kl9Tgt/wKjOrpnGnhRETkOB0KM6+88oqj62jRqFGjsGLFCqSnp+O6667DkSNHsGvXLixbtswpn+9OiipsrTLBei0UnH3WREK4viHMlDPMEBHJUIcXzSsrK8O3336LM2fO4MUXX4S/vz9SU1MREhKCiIgIhxT30ksvoby8HL169YJSqYTFYsFf//pXPPDAA62+xmg0wmg02u9XVHBgJ3DJeBkfdjFdLiFcjx9+LeQgYCIimepQmDl69CjGjRsHg8GAzMxMPPbYY/D398e6deuQlZWFVatWOaS4r776Cl988QVWr16NhIQEpKWlYf78+QgPD8esWbNafE1SUhJeffVVh3y+O7k4+JfTsi+XEG5bCfg3DgImIpKlDs1mWrBgAWbPno1Tp05Bp7v4l/7EiROxY8cOhxX34osvYtGiRbj//vvRr18/zJw5E88//zySkpJafc3ixYtRXl5uv+Xk5DisHjk7V8HBv61pnJ59trgaNfVmiashIqL26lDLzMGDB/Hhhx82ezwiIgKFhYXXXFSjmpoaKBRN85ZSqbzi1GytVgutlq0PlzJbrLhQzTDTmmC9DoHeWhRXGXG8oBKDYvykLomIiNqhQy0zOp2uxbEoJ0+eRFBQ0DUX1eiuu+7CX//6V3z//ffIzMzEunXr8NZbb3Fn7nY6X2WEVQQ81Er46Do8TMqtNbbOHCvguBkiIrnpUJiZNGkS/vznP8NkMgEABEFAdnY2Fi1ahHvuucdhxb377ruYNm0annrqKfTu3RsLFy7E448/jtdee81hn9EVXOxi0nIfrVYkcPE8IiLZ6tCf6f/3f/+H22+/HcHBwaitrcXo0aNRWFiI4cOH469//avDivPx8cGyZcs4FfsanW9YLC+IM5ladXEQMFtmiIjkpkNhRq/XY9euXdi6dStSUlJgtVoxcOBAjBs3ztH1kQMUVzWEGW+ubtuaxpaZE4WVMFmsUHNXcSIi2Wh3mLFarUhOTsbatWuRmZkJQRAQFxeH0NBQiKLIbgwX1BhmAr05MLo10f6e8NaqUGU048z5KvQK1UtdEhERtVG7/vwURRF333035s6di7y8PPTr1w8JCQnIysrC7NmzOTDXBYmiyDDTBgqFgD5hDTto57GriYhITtrVMpOcnIwdO3bg559/xtixY5s8t2XLFkyePBmrVq3Cww8/7NAiqeMq6swwWUQoBHATxavoE67HgcwS/JZfgXsGSV0NERG1VbtaZtasWYPf//73zYIMANx8881YtGgRvvzyS4cVR9eusVXG30sDpYJdgFfSOG6GKwETEclLu8LM0aNHcdttt7X6/MSJE3HkyJFrLooch11Mbdc4o+lYQQVEUZS4GiIiaqt2hZmSkhKEhIS0+nxISAhKS0uvuShynOJKhpm26hniDY1Sgco6M3JKaqUuh4iI2qhdYcZisUClan2YjVKphNnMvW1cSXFVPQCGmbZQKxW4LtQbALuaiIjkpF0DgEVRxOzZs1vd+8hoNDqkKHKci91M8hj8u3p/dqvPzRga3emfnxBmwK95FThWUIGJ/cI6/fOIiOjatSvMzJo166rHcCaT6zBbrSitYctMeyRE6IFDXAmYiEhO2hVmVq5c2Vl1UCcoqa6HVQQ0KgU3mGwjzmgiIpIfrtnuxi7Yx8touDJzG/UK1UMQbJtzNnbRERGRa2OYcWOclt1+XloV4gK8ALCriYhILhhm3Nh5TsvukD7saiIikhUOpHBjF6pt3UwB3MagTRpnUpkstgXzfvilEL4eF392zphNRURE7ceWGTdWwjDTIeEGHQAgv4wL5xERyQHDjJsyW6yoqDUBAPzZzdQuYb4eAGwtW0aTReJqiIjoahhm3FRpjQkiAI1SAS+NUupyZMVbq4K+YSp7QXmdxNUQEdHVMMy4qZLqi7tlc1p2+4U3tM7kl7OriYjI1THMuKnGwb/+HC/TIWEGW5gpKGPLDBGRq2OYcVOlDDPXJKKhZSa3rEbiSoiI6GoYZtwUW2auTZS/LcwUVRhRx0HAREQujevMuCl3nJZ9pR21Hc1Hp4afpxqlNSbkltaiR7C30z6biIjahy0zbkgURftu2WyZ6bgof08AQE4pu5qIiFwZw4wbqqwzw2QRoRAAX0+GmY6K8msIMyUMM0REroxhxg01djEZPNRQKjgtu6Oi/S+GGVEUJa6GiIhawzDjhko4+Nchwgw6KBUCqustKK0xSV0OERG1gmHGDV2cycRtDK6FSqmw79OUza4mIiKXxTDjhhoH/7rTTCapXNrVRERErolhxg1dqLJtZeDHMHPNOKOJiMj1Mcy4IXdcY0YqjWEmv6wWNfVmiashIqKWMMy4mXqzFdX1thVr/Tgt+5r5eqjh66GGVQRSskqlLoeIiFrAMONmGsfL6NQKeGiUElcjf4IgIC7QCwCw7+wFiashIqKWuHyYycvLw0MPPYSAgAB4enqif//+SElJkbosl1XWEGbYKuM43YJsWxnsPcMwQ0Tkilx6b6bS0lKMHDkSY8eOxQ8//IDg4GCcOXMGvr6+UpfmshrXQ2GYcZxuDS0zR3PLUW00w0vr0v/bEBF1OS79r/LSpUsRFRWFlStX2h+LjY2VriAZKK1ubJlRS1yJ6+noRpV+Xhr4eqpRVmNCSlYpbrouyMGVERHRtXDpbqaNGzdi8ODBuPfeexEcHIwBAwbg448/vuJrjEYjKioqmty6ksYxM9yTybG6cdwMEZHLcukwc/bsWSxfvhw9e/bETz/9hCeeeALPPvssVq1a1eprkpKSYDAY7LeoqCgnViw9djN1jrhA27gZhhkiItfj0mHGarVi4MCBeP311zFgwAA8/vjjeOyxx7B8+fJWX7N48WKUl5fbbzk5OU6sWHqNLTN+XuxmcqTLx80QEZHrcOkwExYWhj59+jR5rHfv3sjObn3sg1arhV6vb3LrKqqNZtQ0rDHj68GWGUfy89IgwtcDZquI/RlsnSEiciUuHWZGjhyJkydPNnksPT0dMTExElXk2vLKagFwjZnOMibeNvB3y4kiiSshIqJLuXSYef7557Fv3z68/vrrOH36NFavXo2PPvoI8+bNk7o0l5TbsH+QP8fLdIpbegcDALYcL4IoihJXQ0REjVw6zAwZMgTr1q3DmjVr0LdvX7z22mtYtmwZHnzwQalLc0m5pbaWGc5k6hwjugdCp1Ygv7wOJworpS6HiIgauPQ6MwBw55134s4775S6DFloDDNcY6Zz6NRKjOweiJ9PFGHLiSL0Dus647GIiFyZS7fMUPvklNi6mfy4W3anubmhq+nn4+ckroSIiBoxzLiRiy0zDDOd5eZetjBzOKcMF6qMEldDREQAw4xbaRwA7Mtupk4TZvBAnzA9RBHYevK81OUQEREYZtxGldHM1X+dZFyfEADA90fzJa6EiIgAhhm3kdfQxeShVkKn5hoznWlS/3AAwI5TxShmVxMRkeQYZtxEYxcTZzJ1vu5B3rg+0gCLVcT3RwukLoeIqMtjmHET9sG/nMnkFJP7RwAA1h3Ok7gSIiJimHETF1tmGGac4a7EcCgVAtJyypBRXC11OUREXRrDjJu4uPovu5mcIchHi5E9AgEAG9LYOkNEJCWGGTeRw5YZp5sywDYQ+JtDuTBbrBJXQ0TUdTHMuAkumOd8E/uGwd9Lg7yyWmw6xhWBiYikwjDjBirrTChrWGOG3UzOo1Mr8dDQaADAp7syJK6GiKjrYphxA3llF8fLcI0Z53poeAzUSgEpWaVIyymTuhwioi6JYcYN5JbYwkykn4fElXQ9wT463JVoGzuzcjdbZ4iIpMAw4wYap2VH+npKXEnX9MjIOADAf44WIJPTtImInI5hxg00Dv6N8mfLjBT6RhgwNj4IFquI/9t0UupyiIi6HIYZN9AYZiL92DIjld/d1guCYGud+SW3XOpyiIi6FIYZN5Bb1tDNxDEzkukdpseUhi0Olv54QuJqiIi6FpXUBdC1u7Rl5lwFd3GWSvcgbygVAnadLsbL639FrzB9k+dnNEzjJiIix2LLjMxdusZMBFtmJOXnpcHI7gEAgA1H8mE0WSSuiIioa2CYkbmLK/+q4a1lQ5vUbu4VAn8vDcprTfjpWKHU5RARdQkMMzLHwb+uRaNSYHLD2Jn9Z0s4VZuIyAkYZmTOvsYMu5hcRo9gbwyK8YMI4OtDOaitZ3cTEVFnYr+EzF1smWGY6Wyr92e3+dg7+oUho7gaJdX1+C41Fw9y8C8RUadhy4zMNbbMRPmzm8mV6NRKPDAkGkqFgGMFFdh79oLUJRERuS2GGZljy4zrivDzwMS+oQCA//5SgD2niyWuiIjIPTHMyBwHALu24d0C0D/KF1YRePLLVA4IJiLqBAwzMlZRZ0J5bcMaM75smXFFgiBgyoAIRPp5oLzWhEc/P2i/ZkRE5BgMMzKWW2JrlfH30sCLa8y4LLVSgYeGxSDMoMOZ89V4Zs1hmC1WqcsiInIbDDMyxmnZ8qHXqfHxw4OhUyuwI/08Xv8v928iInIU/jkvYxz8Ky9Hc8sxZUAk1hzIxme7M3Ch2oihcbbtD7hvExFRx7FlRsY4+Fd++kUYMK53MABgY1o+ThRWSFwREZH8ySrMJCUlQRAEzJ8/X+pSXAK7meRpbHwwBkbbVgj+14Ec5DWEUiIi6hjZhJmDBw/io48+wvXXXy91KS6D3Uzy1DjDqUewN+otVqzam2kPpkRE1H6yCDNVVVV48MEH8fHHH8PPz0/qclyGffVfdjPJjlIhYMYN0QjV61BpNGPOSk7ZJiLqKFmEmXnz5uGOO+7AuHHjpC7FZZTXmlBRZwZgW2mW5EenVmLWiFjodSqcKqrC4/88BKOZm1ISEbWXy4eZf/3rX0hNTUVSUlKbjjcajaioqGhyc0eN4ywCvDTw1HBSmlwZPNSYNSIW3loV9p0twaLvfoEoilKXRUQkKy79WzAnJwfPPfccNm3aBJ1O16bXJCUl4dVXX+3kyqS1en82juWXAwA8NMp27eZMrifM4IH3HxyIR5IPYt3hPET4emDhhHipyyIikg2XbplJSUlBUVERBg0aBJVKBZVKhe3bt+Odd96BSqWCxdK8SX7x4sUoLy+333JyciSovPOV1tjGV/h6aiSuhBxh9HVBeH1KXwDAe1tPI3l3hsQVERHJh0u3zNxyyy345Zdfmjw2Z84c9OrVCy+99BKUSmWz12i1Wmi1WmeVKJnSmnoAgJ+nWuJKyFHuGxKNgvI6LPvfKSz59zF469SYNihS6rKIiFyeS4cZHx8f9O3bt8ljXl5eCAgIaPZ4V9PYMuPHlhm30NhVGOStxYjuAdhz5gJe/OYIDmaWIDHSlysEExFdgUt3M1HrSqvZMuOOBEHA7f3CMCjGtqje1wdzkJpVKnVZREQuzaVbZlqybds2qUuQnCiKKGnoZvL3cv8uta5G0bConkIQcDCzBN+m5qJvhB6zR8ZJXRoRkUtiy4wMVddbUG+2QgDgy5YZt6QQBEzuH47h3W0bUS759zH8af2vMFusEldGROR6GGZkqKShi0nvoYZayUvorgRBwJ39wnBbQigEAfjnviw89Ol+5JdxLyciokvxN6EMNYYZfy8O/nV3giDgpuuC8OFDg+CpUWLf2RJMWLYDa1NzubgeEVEDhhkZsocZzmTqMsYnhOI/z4xCYpQvKuvMWPD1EUxbsRep2RwcTETEMCND9plMbJnpUroFeeO7J4Zj4fjroFMrkJJViqkf7MFDn+zH1hNFsFrZUkNEXZPsZjMRLpnJxDDT1aiUCjx9c09MGxSFtzafxLcpudh1uhi7Thcj0s8DUwdG4p6BEYgJ8JK6VCIip2HLjAxxzAyFGnT427REbH9xLB67MQ4+OhVyS2vxzs+nMPrv2zB9xV58dTAblXUmqUslIup0bJmRmTqTBRW1tl9QDDMU5e+JP9zRBy+Mj8dPvxXaW2oOZJbgQGYJXtn4G25LCEW4rwci/TxbfR+uMExEcsYwIzN5ZbUQAWhUCnhpmu9NRV2TTq3EpP4RmNQ/AoXldVh3OA/fpuTgzPlqrE/LBwD0DPbGuN4hiPJvPdQQEckRw4zMZJfUALDNZBIEQeJqyBWFGnR4ckx3PDG6G47klmPV3kysP5yHU0VVOFVUhUHRfpjQNxTeWv7vT0Tugf+ayUxOY5hhFxNdhSAI6B/li/5R/dEt0BtbThQhNbsUKdmlOFZQgakDI5AQbpC6TCKia8YwIzNZFxhmuqLGXbU7yt9Lg2mDInFDnD82pOWhoLwOX+7Pxg2x/rjj+jAHVUlEJA2GGZlp7GbiGjPUEdH+nnhyTHf871gRdp46jwOZJcgvr0WtyQK9rvV9vjhAmIhcGadmy0xjN1MAwwx1kEqhwG19QzF7ZCw81Erkltbig62nuecTEckWw4yMiKLYZAAw0bXoGeyDp8Z0R5CPFhV1Zny88yzOFldJXRYRUbsxzMjI+UojauotEAD4erbeJUDUVgHeWjw5ujviAr1gNFuRvDsTxwsqpC6LiKhdGGZk5GxxNQDbeBmVkpeOHEOnVmL2iFj0DvWB2Sriy/1Z3MCSiGSFvxFlJKMhzAR6s4uJHEutVGDG0BgMjPaFVQS+TcnF7tPFUpdFRNQmDDMycjHMaCWuhNyRUiFg6sBIjOweAAD4/pcCbD52DqLI3biJyLUxzMjI2fMMM9S5FIKA2/uF4dY+IQCArSeL8O+j+bBaGWiIyHUxzMhIRsNME4YZ6kyCIGBsfDDuTgyHAGDf2RI8/3UaTBar1KUREbWIYUYmzBarfVo2x8yQMwzrFoDpQ6KgEIANafl4/J8pqK23SF0WEVEzDDMykVdWC5NFhFalgN6D07LJORIjfTFzWCx0agW2nCjCw5/tR3mtSeqyiIia4HYGMtE4LTsu0AsK7pZNThQf6oMvHh2KOckHcTCzFA98tA+fP3IDgnwudndeae8oboVARJ2NLTMykXH+YpghcrbBsf746v8NR6C3FscKKjBtxR777DoiIqkxzMhERjHDDEmrT7ge3z4xHFH+Hsi6UIOpH+xGSlaJ1GURETHMyAXDDLmC2EAvrH1yJBIjDSitMeGBj/fj+6MFUpdFRF0cw4xMNIaZbkEMMyStIB8t1vy/Ybi1TwjqzVbMW52KnafOc3E9IpIMw4wM1JksyCurBQDEBXpLXA0R4KlRYcVDgzB7RCwA4IdfC7HxSD4sXFyPiCTAMCMDmRdsrTIGDzX8uFs2uQilQsCSuxPwpzv7QACwP6MEX+zLgtHMtWiIyLkYZmTgdJFt5d+4QC8InJZNLubRUXF44IZoqBQCTp6rxMc7z6KijmvREJHzMMzIQHphJQCgV6iPxJUQtaxvhAFzb+wGT40S+WV1WLHtDM5V1EldFhF1EQwzMnCiIcxcF8IwQ64r2t8TT47ujgAvDcpqTVix/QxOFlZIXRYRdQEuvQJwUlIS1q5dixMnTsDDwwMjRozA0qVLER8fL3VpTpV+zhZm4tkyQxK50gq/lwrw1uLJ0d3xxf4sZF6owaq9WQjz9cDjN3VjFykRdRqXbpnZvn075s2bh3379mHz5s0wm80YP348qqu7zsqjtfUWZDVsMMkwQ3LgqVXhkVFxGBLrBxHAGz+cwBNfpHBPJyLqNC7dMvPjjz82ub9y5UoEBwcjJSUFN910k0RVOdfpoiqIIhDgpUGgt/bqLyByASqFApP7RyDM4IEffi3AT7+dw7GCnVh23wAMivGTujwicjMuHWYuV15eDgDw9/dv9Rij0Qij0Wi/X1Eh7z77k+c4XobkSRAEDOsWgLk3xmHe6lTklNRi2oo9eHRkHF4YHw8PjbJN73O1Li5uZElELt3NdClRFLFgwQKMGjUKffv2bfW4pKQkGAwG+y0qKsqJVTpe4wBKdjGRXF0f6Yv/PHMj7hkYCVEEPtmVgVve3IZ1h3Nh5SJ7ROQAsgkzTz/9NI4ePYo1a9Zc8bjFixejvLzcfsvJyXFShZ3j5DnbGjMMMyRnBg813pyeiM9mD0a4QYf88jo8/9UR3P7OTqxNzUW92Sp1iUQkY7LoZnrmmWewceNG7NixA5GRkVc8VqvVQqt1n7El6ZyWTW7k5l4h2LIwEJ/uysDybWdworASC74+gqQfTmBSYjgmD4hAQrieM5+IqF1cOsyIoohnnnkG69atw7Zt2xAXFyd1SU5VXmNCYcPCY9eFcE8mcg86tRLzxvbAjBuisfpANj7fk4miSiM+2ZWBT3ZlIMLXA7f0DsbNvYIxrFuA1OUSkQy4dJiZN28eVq9ejQ0bNsDHxweFhYUAAIPBAA8PD4mr63yNg38jfD3go+OeTORe/Lw0mDe2Bx67sRu2nSzChrR8/O/4OeSV1WLV3iys2psFD7USsYFe6BXqg/gQH+g9+P8BETXn0mFm+fLlAIAxY8Y0eXzlypWYPXu28wtyspNcLI+6AI1KgfEJoRifEIraegv2nCnGzyeKsOV4EQor6nC8oALHC2wD4SN8PRAf6oN+EQaE6HUSV05ErsKlw4wodu2ZDo3/gDPMkJy1Z2q1h0aJW3qH4JbeIRAnizhWUIF/bD6Fk4UVyC2tRV6Z7bblRBEi/TwwJMYfUwZEtHmaNxG5J5cOM13dkZwyAEBipEHaQogkIAgCEsINuLmXbfxMZZ0J6eeq8Ft+OdLPVSK3tBa5pXnYml6EB4dG49FR3eDvpZG6bCKSAMOMi6ozWewbTCZG+UpbDJEL8NGpMSjGD4Ni/FBlNONwdin2nb2A0hoT3t96Bit3Z2LWiFg8Mbo7DBxbQ9SlMMy4qN/yy2Gxigj20SKUYwOImvDWqnBjzyCM7BGIQG8t3tt6Cr/mVWD5tjP46mAOXhh/He4fEg2lQuAKwkRdgGwWzetq0nJsWzckRvlyzQ2iVigEAbf1DcW/nx6Fj2YOQs9gb5RU1+MP637FlA9240ShvLczIaK2YcuMi2ocL9OfXUzk5q7WctIWgiBgfEIoxvYKxpf7svDW5nQczS3HXe/uwk3XBWHMdcFQKvhHAZG7YphxUWn2wb++ktZB5OouD0MalRJPje2BjWn5OFZQgZ+PF+FMURWmD46CrycHCBO5I3YzuaCS6npkl9QAAPpxJhNRu+l1ajw4NBr3DY6CVqVA5oUavLvlNE4XVUldGhF1AoYZF3QktwwA0C3Ii7MyiDpIEAQkRvni6bE9EOHrgVqTBcl7MrA/44LUpRGRgzHMuCD7eBl2MRFdswBvLf7fTd3QP8oXVhHYkJaP/xzNh8XatRflJHInDDMuyL5YHgf/EjmEWqnAvYMicWufEADAnjMX8M99magzWSSujIgcgWHGxZgtVhzKLAUADIj2lbYYIjciCALGxgfjgRuioVYKSD9XhY92nMW5hp3piUi+GGZczJHcMlQazfD1VCMhnIN/iRytX4QB/+/G7vDRqlBYUYepH+zB2fMcGEwkZwwzLmbnqWIAwMjugVwXg6iTRPh54PHR3RHgpUFeWS2mrdhrXw6BiOSHYcbF7GoIM6N6BkpcCZF78/fS4PHR3XF9pAEl1fWY8fE+bE8/L3VZRNQBDDMupKLOhMMNfx2O6sEwQ9TZvLUqrH5sGG7sGYiaegseTT6I9YfzpC6LiNqJYcaF7DtzARariNgAT0T5e0pdDlGX4K1V4dNZQ3B3YjjMVhHzv0rDJzvPSl0WEbUDw4wL2XXa1sV0Y88giSsh6lo0KgWW3dcfs0fEAgD+8v1xJP1wHKLItWiI5IBhxoVwvAyRdBQKAa/c1Qe/uy0eAPDh9rNY+M1RmCxWiSsjoqvhRpMu4uz5KpwtroZSIWB49wCpyyHqMi7fqNLXQ4OpAyKwPi0P36XmorSmHu/PGAgPjVKiConoatgy4yLWNQw6vKlnIPQ67sdEJKXBsf54cGgMVAoBW04UYcYn+1BaXS91WUTUCoYZF2C1ivYwM2VgpMTVEBEA9A7T49FRcdDrVDicXYZ7P9yL/LJaqcsiohYwzLiAg5klyC2thbdWhfENe8cQkfRiArzw7ZMjEKrX4XRRFe5Zvgfp5yqlLouILsMw4wIaW2Um9g2FTs1+eSJXcl2ID757agS6B3mhoLwO967Yax+sT0SugWFGYnUmC77/pQAAMGVghMTVENHlVu/PxvaT5/HAkGhE+XmgvNaEmZ/ux9zPD8Jq5dRtIlfAMCOx748WoLLOjHCDDsPiOIuJyFV5alWYe2M3DI7xgwjgf8dtA4NzSmqkLo2oy2OYkZDJYsU7W04BAB4aHgMFN5YkcmlqpQJTB0Zi2sBIqJUC9p0twW3LduCf+7JgYSsNkWQYZiS0NjUXWRdqEOClwazhsVKXQ0RtNDDGD8/e3BNDYv1QXW/Bn9b/irve3YV9Zy9IXRpRl8QwIxGj2YJ3fj4NAHhyTHd4abl+IZGcBHhr8a//Nxyv3NUHPjoVjhVU4P6P9uG+D/die/p5jqchciL+BpXIl/uykVdWixC9Fg8Ni5G6HCLqAKVCwJyRcbg7MRz/+F86vjqYg/0ZJdifcQCRfh6YOiAC4xNC0SdMz25kok7EMCOBX/PK8caPJwAAz9zck9OxiWQuwFuLv0zuh3lje+CTnRn46mAOcktr8c6W03hny2n4e2kwOMYPvcP06BHsjRC9DkE+WgT5aOGlUUIQGHSIrgXDjJOV15owb3Uq6s1W3NIrGDNuiJa6JCJykDCDB/50Zx8sHB+PTccK8e8j+dh75gJKquux6dg5bDp2rtlr1EoBHmoltGoldCoFdJd8nxjlCx+dCt5aFfQ6Nbx1Khg81PDz1CDAWwM/Tw00Ko4WIGKYcaJqoxnPrDmMrAs1iPD1wJvTE9n0TCRjl29SeblPZg2ByWLFkZwyHM0tx/GCCmQUV6O4yojzlUZU11tgsogwWcxAnbnZ6w9llV61Bh+dCv5eGvh7aRDQ8NWv4Xs/Tw08NSp4aGwhyVOjgodaCQ+1Ejq1AhqVAlqVEhqVAkr+W0QyJosw88EHH+Dvf/87CgoKkJCQgGXLluHGG2+Uuqx2yS+rxaOfH8LxggpoVAp88OBA+HpqpC6LiDqZWqnA4Fh/DI71b/bcyt0ZqKozo85kRZ3ZAqP9qwV1Zqv9a53J9pzRbEFNvQXV9RbUGM0QAVTWmVFZZ0bWhWtb70Yh2MYAqRQKqJQCDB5qaFUKaBrCjlapgFatgKdGCR+dGj46FXx0auh1Kvv3lz7m66mBXqeCSinvliNRtA3kZlega3P5MPPVV19h/vz5+OCDDzBy5Eh8+OGHmDhxIo4dO4boaNfvoqkzWfDFviy8v/U0SmtMCPTW4MOZg5EY5St1aUTUya7WcqNVKaH17tiYOasooq7egqp6M2qMFlTXm1Ft/2pGTb0FNfVm1JtFmCxW1FusMFmsMJmtDa1BVohN3g+wWkSYLBbAZAtJjqBV2QKQh1oJD40SHg2tQ4Nj/WDwUMPXQw1fTzX0OjW0agXUSluLkVqpgNBwnhYrYLGKDd+LsIgiTGYrjGYr6hu/WiwXv2/4arvZHm/6XMNjFiuMpsu/WlBeZ4bFaoXZIsLcMCtNIQAKQYBCYesWVCkEqJQCPDWNYU4FH626SbDz81TDz0sDX08N/D018G24z3FSjieIjbHTRQ0dOhQDBw7E8uXL7Y/17t0bkydPRlJS0lVfX1FRAYPBgPLycuj1+s4s1a60uh5pOWXYcqIIP/xaiOIqIwCgV6gPPpk1GJF+ntf0/lf7B5KI6GpEUYRVBMxWKywNv7TNVhFmi/Xi9y08Z7ysxch2s7Uo2b83WWA0W6U+RZelUSpswcZTAz8v21dfTw38L/ve19PWVeihtrWO2UKeAI1S0SXCUHt+f7t0y0x9fT1SUlKwaNGiJo+PHz8ee/bskagqmz2ni7E9/TzKa02oqDOhotaM8loTCsprUVxV3+TYCF8PPHtLD0wdGAm1zJtcicg9CIIApQAoFcpO+U1gsYqoNVlQW29p+GpGrcnWTVZbb0F0gCfKa0worzWhrNaEilqTrfXIbEW9RUSV0WSrEwKExlYRwVb35V1iqku+VyoEqJWKhucbbkpFk6+2YwQoFQp7C4vq8u8b3lepECAIAqyXtQxZRcBqFS8JdxeDXOPXGpOtdazG2PC13gKzVUS9xYqiSiOKKo0d/vlqlJeEG5UCKoWi4Zxs9Svt59L8fuN5Ky/92V1y//KfYePXSwNU47cCbN8MiPbFsG7Sbcnj0mGmuLgYFosFISEhTR4PCQlBYWFhi68xGo0wGi/+B1JeXg7AlvAcac+JHCxvWPSuJRF+OozoHoibegZhZI9AaFQK1FZXodYBn11TXemAdyEi6lwKAF4KwEsLQCvA9ivH9mtn+uCoK77260M5nV3eZcSGGwBYLj7U8K39z1Ch4QYASgBqAB6N53aVTxBFmC0iaurNqK232sNObb0FNSYzao1W23176DOjxmSF2WLF5Wsw1jXcXMXcUXHoE6h26Hs2/t5uSweSS4eZRpc3p4mi2GoTW1JSEl599dVmj0dFXfl/HEfLAbAPwFtO/VQiInl4TOoCyKFeWQa80knvXVlZCYPBcMVjXDrMBAYGQqlUNmuFKSoqatZa02jx4sVYsGCB/b7VakVJSQkCAgJk18dYUVGBqKgo5OTkOG28j9S62jl3tfMFut45d7XzBbreOXe18wWcc86iKKKyshLh4eFXPdalw4xGo8GgQYOwefNmTJkyxf745s2bMWnSpBZfo9VqodVqmzzm6+vbmWV2Or1e32X+B2nU1c65q50v0PXOuaudL9D1zrmrnS/Q+ed8tRaZRi4dZgBgwYIFmDlzJgYPHozhw4fjo48+QnZ2Np544gmpSyMiIiIX4PJh5r777sOFCxfw5z//GQUFBejbty/++9//IiaGmzMSERGRDMIMADz11FN46qmnpC7D6bRaLV555ZVm3WburKudc1c7X6DrnXNXO1+g651zVztfwPXO2eUXzSMiIiK6Eq7gRkRERLLGMENERESyxjBDREREssYw42QffPAB4uLioNPpMGjQIOzcufOKx2/fvh2DBg2CTqdDt27dsGLFimbHfPfdd+jTpw+0Wi369OmDdevWdVb57ebo801OToYgCM1udXWus7B3e865oKAAM2bMQHx8PBQKBebPn9/ice5yjdtyvu52jdeuXYtbb70VQUFB0Ov1GD58OH766admx7nLNW7L+brbNd61axdGjhyJgIAAeHh4oFevXvjHP/7R7Dh3ucZtOV+nX2ORnOZf//qXqFarxY8//lg8duyY+Nxzz4leXl5iVlZWi8efPXtW9PT0FJ977jnx2LFj4scffyyq1Wrx22+/tR+zZ88eUalUiq+//rp4/Phx8fXXXxdVKpW4b98+Z51WqzrjfFeuXCnq9XqxoKCgyc1VtPecMzIyxGeffVb8/PPPxf79+4vPPfdcs2Pc6Rq35Xzd7Ro/99xz4tKlS8UDBw6I6enp4uLFi0W1Wi2mpqbaj3Gna9yW83W3a5yamiquXr1a/PXXX8WMjAzxn//8p+jp6Sl++OGH9mPc6Rq35XydfY0ZZpzohhtuEJ944okmj/Xq1UtctGhRi8f/7ne/E3v16tXksccff1wcNmyY/f706dPF2267rckxEyZMEO+//34HVd1xnXG+K1euFA0Gg8NrdZT2nvOlRo8e3eIvd3e6xpdq7Xzd+Ro36tOnj/jqq6/a77vrNW50+fl2hWs8ZcoU8aGHHrLfd/drfPn5Ovsas5vJSerr65GSkoLx48c3eXz8+PHYs2dPi6/Zu3dvs+MnTJiAQ4cOwWQyXfGY1t7TWTrrfAGgqqoKMTExiIyMxJ133onDhw87/gQ6oCPn3BbudI3byp2vsdVqRWVlJfz9/e2PufM1bul8Afe+xocPH8aePXswevRo+2PufI1bOl/AudeYYcZJiouLYbFYmm2QGRIS0mwjzUaFhYUtHm82m1FcXHzFY1p7T2fprPPt1asXkpOTsXHjRqxZswY6nQ4jR47EqVOnOudE2qEj59wW7nSN28Ldr/Gbb76J6upqTJ8+3f6YO1/jls7XXa9xZGQktFotBg8ejHnz5mHu3Ln259zxGl/pfJ19jWWxArA7uXznblEUr7ibd0vHX/54e9/TmRx9vsOGDcOwYcPsz48cORIDBw7Eu+++i3feecdRZV+Tzrge7nSNr8adr/GaNWuwZMkSbNiwAcHBwQ55T2dw9Pm66zXeuXMnqqqqsG/fPixatAg9evTAAw88cE3v6SyOPl9nX2OGGScJDAyEUqlslnSLioqaJeJGoaGhLR6vUqkQEBBwxWNae09n6azzvZxCocCQIUNc4i+6jpxzW7jTNe4Id7nGX331FR599FF88803GDduXJPn3PEaX+l8L+cu1zguLg4A0K9fP5w7dw5Lliyx/3J3x2t8pfO9XGdfY3YzOYlGo8GgQYOwefPmJo9v3rwZI0aMaPE1w4cPb3b8pk2bMHjwYKjV6ise09p7Oktnne/lRFFEWloawsLCHFP4NejIObeFO13jjnCHa7xmzRrMnj0bq1evxh133NHseXe7xlc738u5wzW+nCiKMBqN9vvudo0vd/n5tvR8p15jpw01Jvv0t08//VQ8duyYOH/+fNHLy0vMzMwURVEUFy1aJM6cOdN+fONU5eeff148duyY+Omnnzabqrx7925RqVSKb7zxhnj8+HHxjTfecLnpfo483yVLlog//vijeObMGfHw4cPinDlzRJVKJe7fv9/p59eS9p6zKIri4cOHxcOHD4uDBg0SZ8yYIR4+fFj87bff7M+70zUWxaufr7td49WrV4sqlUp8//33m0xRLSsrsx/jTte4Lefrbtf4vffeEzdu3Cimp6eL6enp4meffSbq9XrxD3/4g/0Yd7rGbTlfZ19jhhkne//998WYmBhRo9GIAwcOFLdv325/btasWeLo0aObHL9t2zZxwIABokajEWNjY8Xly5c3e89vvvlGjI+PF9VqtdirVy/xu+++6+zTaDNHn+/8+fPF6OhoUaPRiEFBQeL48ePFPXv2OONU2qy95wyg2S0mJqbJMe50ja92vu52jUePHt3iOc+aNavJe7rLNW7L+brbNX7nnXfEhIQE0dPTU9Tr9eKAAQPEDz74QLRYLE3e012ucVvO19nXmLtmExERkaxxzAwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBEREckawwwRERHJGsMMERERyRrDDBHJmiAIWL9+fbuPzczMhCAISEtLa/X4bdu2QRAElJWVAQCSk5Ph6+trf37JkiXo379/h+omIsdhmCEizJ49G5MnTwYAjBkzBvPnz5e0npSUFAiCgF27drX4/IQJE3D33XcDAAoKCjBx4sQ2vW97jgWAESNGoKCgAAaDocXnFy5ciJ9//tl+/9KfIxE5D8MMEbmcQYMGITExEStXrmz2XE5ODv73v//h0UcfBQCEhoZCq9W26X3bcyxg21E4NDQUgiC0+Ly3tzcCAgLa/H5E1DkYZojIbvbs2di+fTvefvttCIIAQRCQmZkJADh27Bhuv/12eHt7IyQkBDNnzkRxcbH9tWPGjMEzzzyD+fPnw8/PDyEhIfjoo49QXV2NOXPmwMfHB927d8cPP/zQploeffRRfP3116iurm7yeHJyMoKCgnDHHXcAaNp1VF9fj6effhphYWHQ6XSIjY1FUlKS/bUtdUmdOHECI0aMgE6nQ0JCArZt22Z/7vJupstd2s20ZMkSfP7559iwYYP9Z7dt2zbcfPPNePrpp5u87sKFC9BqtdiyZUubfhZEdGUMM0Rk9/bbb2P48OF47LHHUFBQgIKCAkRFRaGgoACjR49G//79cejQIfz44484d+4cpk+f3uT1n3/+OQIDA3HgwAE888wzePLJJ3HvvfdixIgRSE1NxYQJEzBz5kzU1NRctZYHH3wQJpMJ33zzjf0xURSRnJyMWbNmQaVSNXvNO++8g40bN+Lrr7/GyZMn8cUXXyA2NvaKn/Piiy/ihRdewOHDhzFixAjcfffduHDhQtt+YJdYuHAhpk+fjttuu83+sxsxYgTmzp2L1atXw2g02o/98ssvER4ejrFjx7b7c4ioOYYZIrIzGAzQaDTw9PREaGgoQkNDoVQqsXz5cgwcOBCvv/46evXqhQEDBuCzzz7D1q1bkZ6ebn99YmIi/vjHP6Jnz55YvHgxPDw8EBgYiMceeww9e/bEyy+/jAsXLuDo0aNXrcXf3x+TJ09u0tW0bds2nD17Fo888kiLr8nOzkbPnj0xatQoxMTEYNSoUXjggQeu+DlPP/007rnnHvTu3RvLly+HwWDAp59+2saf2EXe3t7w8PCAVqu1/+w0Gg3uueceCIKADRs22I9duXIlZs+e3Wr3FRG1D8MMEV1VSkoKtm7dCm9vb/utV69eAIAzZ87Yj7v++uvt3yuVSgQEBKBfv372x0JCQgAARUVFbfrcRx99FDt27MDp06cBAJ999hlGjhyJ+Pj4Fo+fPXs20tLSEB8fj2effRabNm266mcMHz7c/r1KpcLgwYNx/PjxNtXXFlqtFg899BA+++wzAEBaWhqOHDmC2bNnO+wziLo6hhkiuiqr1Yq77roLaWlpTW6nTp3CTTfdZD9OrVY3eZ0gCE0ea2yJsFqtbfrccePGISYmBsnJyaioqMDatWvtA39bMnDgQGRkZOC1115DbW0tpk+fjmnTprXnVJvU6Shz587F5s2bkZubi88++wy33HILYmJiHPoZRF1Z805nIurSNBoNLBZLk8cGDhyI7777DrGxsS2OVeksgiBgzpw5+OSTTxAZGQmFQtFsnM7l9Ho97rvvPtx3332YNm0abrvtNpSUlMDf37/F4/ft22cPZGazGSkpKc0G7LZVSz87AOjXrx8GDx6Mjz/+GKtXr8a7777bofcnopaxZYaImoiNjcX+/fuRmZmJ4uJiWK1WzJs3DyUlJXjggQdw4MABnD17Fps2bcIjjzzS4i9vR5ozZw7y8/Px+9//Hvfffz+8vLxaPfYf//gH/vWvf+HEiRNIT0/HN998g9DQ0CYL3V3u/fffx7p163DixAnMmzcPpaWlrY7JuZrY2FgcPXoUJ0+eRHFxMUwmk/25uXPn4o033oDFYsGUKVM69P5E1DKGGSJqYuHChVAqlejTpw+CgoKQnZ2N8PBw7N69GxaLBRMmTEDfvn3x3HPPwWAwQKHo3H9GoqOjMW7cuDaFDG9vbyxduhSDBw/GkCFDkJmZif/+979XrPGNN97A0qVLkZiYiJ07d2LDhg0IDAzsUK2PPfYY4uPjMXjwYAQFBWH37t325x544AGoVCrMmDEDOp2uQ+9PRC0TRFEUpS6CiMjd5eTkIDY2FgcPHsTAgQOlLofIrTDMEBF1IpPJhIKCAixatAhZWVlNWmuIyDHYzUREknjiiSeaTPW+9PbEE09IXZ7D7N69GzExMUhJScGKFSukLofILbFlhogkUVRUhIqKihaf0+v1CA4OdnJFRCRXDDNEREQka+xmIiIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZY5ghIiIiWWOYISIiIlljmCEiIiJZ+/8euAzJn7vbaAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_Visibility'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Item_MRP', ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_MRP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Item_Outlet_Sales', ylabel='Density'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log transformation\n",
    "df['Item_Outlet_Sales'] = np.log(1+df['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Item_Outlet_Sales', ylabel='Density'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Item_Fat_Content'] = df['Item_Fat_Content'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Dairy'),\n",
       " Text(1, 0, 'Soft Drinks'),\n",
       " Text(2, 0, 'Meat'),\n",
       " Text(3, 0, 'Fruits and Vegetables'),\n",
       " Text(4, 0, 'Household'),\n",
       " Text(5, 0, 'Baking Goods'),\n",
       " Text(6, 0, 'Snack Foods'),\n",
       " Text(7, 0, 'Frozen Foods'),\n",
       " Text(8, 0, 'Breakfast'),\n",
       " Text(9, 0, 'Health and Hygiene'),\n",
       " Text(10, 0, 'Hard Drinks'),\n",
       " Text(11, 0, 'Canned'),\n",
       " Text(12, 0, 'Breads'),\n",
       " Text(13, 0, 'Starchy Foods'),\n",
       " Text(14, 0, 'Others'),\n",
       " Text(15, 0, 'Seafood')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(15,5))\n",
    "l = list(df['Item_Type'].unique())\n",
    "chart = sns.countplot(data=df, x=\"Item_Type\")\n",
    "chart.set_xticklabels(labels=df['Item_Type'].unique(), rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Outlet_Establishment_Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Outlet_Size', ylabel='count'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Outlet_Size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Outlet_Location_Type', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Outlet_Location_Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Outlet_Type', ylabel='count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Outlet_Type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coorelation Matrix\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_df = df.select_dtypes(include=['float64', 'int64'])\n",
    "corr = numeric_df.corr()\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>8.225808</td>\n",
       "      <td>Food</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>6.096776</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>7.648868</td>\n",
       "      <td>Food</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>6.597664</td>\n",
       "      <td>Food</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.066132</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>6.903451</td>\n",
       "      <td>Non-Consumable</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.066132   \n",
       "4           NCD19         8.93       Non-Edible         0.066132   \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       Small               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales   New_Item_Type  Outlet_Years  \n",
       "0  Supermarket Type1           8.225808            Food            14  \n",
       "1  Supermarket Type2           6.096776          Drinks             4  \n",
       "2  Supermarket Type1           7.648868            Food            14  \n",
       "3      Grocery Store           6.597664            Food            15  \n",
       "4  Supermarket Type1           6.903451  Non-Consumable            26  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['Outlet'] = le.fit_transform(df['Outlet_Identifier'])\n",
    "cat_col = ['Item_Fat_Content', 'Item_Type', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'New_Item_Type']\n",
    "for col in cat_col:\n",
    "    df[col] = le.fit_transform(df[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Onehot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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_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>Item_Outlet_Sales</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Location_Type_0</th>\n",
       "      <th>Outlet_Location_Type_1</th>\n",
       "      <th>Outlet_Location_Type_2</th>\n",
       "      <th>Outlet_Type_0</th>\n",
       "      <th>Outlet_Type_1</th>\n",
       "      <th>Outlet_Type_2</th>\n",
       "      <th>Outlet_Type_3</th>\n",
       "      <th>New_Item_Type_0</th>\n",
       "      <th>New_Item_Type_1</th>\n",
       "      <th>New_Item_Type_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.225808</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>6.096776</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>7.648868</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>6.597664</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>6.903451</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight  Item_Visibility  Item_Type  Item_MRP  \\\n",
       "0           FDA15         9.30         0.016047          4  249.8092   \n",
       "1           DRC01         5.92         0.019278         14   48.2692   \n",
       "2           FDN15        17.50         0.016760         10  141.6180   \n",
       "3           FDX07        19.20         0.066132          6  182.0950   \n",
       "4           NCD19         8.93         0.066132          9   53.8614   \n",
       "\n",
       "  Outlet_Identifier  Outlet_Establishment_Year  Item_Outlet_Sales  \\\n",
       "0            OUT049                       1999           8.225808   \n",
       "1            OUT018                       2009           6.096776   \n",
       "2            OUT049                       1999           7.648868   \n",
       "3            OUT010                       1998           6.597664   \n",
       "4            OUT013                       1987           6.903451   \n",
       "\n",
       "   Outlet_Years  Outlet  ...  Outlet_Location_Type_0  Outlet_Location_Type_1  \\\n",
       "0            14       9  ...                    True                   False   \n",
       "1             4       3  ...                   False                   False   \n",
       "2            14       9  ...                    True                   False   \n",
       "3            15       0  ...                   False                   False   \n",
       "4            26       1  ...                   False                   False   \n",
       "\n",
       "   Outlet_Location_Type_2  Outlet_Type_0  Outlet_Type_1  Outlet_Type_2  \\\n",
       "0                   False          False           True          False   \n",
       "1                    True          False          False           True   \n",
       "2                   False          False           True          False   \n",
       "3                    True           True          False          False   \n",
       "4                    True          False           True          False   \n",
       "\n",
       "   Outlet_Type_3  New_Item_Type_0  New_Item_Type_1  New_Item_Type_2  \n",
       "0          False            False             True            False  \n",
       "1          False             True            False            False  \n",
       "2          False            False             True            False  \n",
       "3          False            False             True            False  \n",
       "4          False            False            False             True  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=['Item_Fat_Content', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'New_Item_Type'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Outlet_Establishment_Year', 'Item_Identifier', 'Outlet_Identifier', 'Item_Outlet_Sales'])\n",
    "y = df['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "def train(model, X, y):\n",
    "    # train the model\n",
    "    model.fit(X, y)\n",
    "    \n",
    "    # predict the training set\n",
    "    pred = model.predict(X)\n",
    "    \n",
    "    # perform cross-validation\n",
    "    cv_score = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=5)\n",
    "    cv_score = np.abs(np.mean(cv_score))\n",
    "    \n",
    "    print(\"Model Report\")\n",
    "    print(\"MSE:\",mean_squared_error(y,pred))\n",
    "    print(\"CV Score:\", cv_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "model = LinearRegression()\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "model.fit(X_scaled, y)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "model = Ridge()\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "model.fit(X_scaled, y)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 0.7628688679102086\n",
      "CV Score: 0.7630789166281843\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Model Coefficients'}>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "model = Lasso()\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()\n",
    "coef.plot(kind='bar', title=\"Model Coefficients\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 5.5534030638578795e-34\n",
      "CV Score: 0.5744577730528413\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature Importance'}>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "model = DecisionTreeRegressor()\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 0.04247199771286015\n",
      "CV Score: 0.3107752369832421\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature Importance'}>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "MSE: 1.0418489584965893e-28\n",
      "CV Score: 0.3310897500122333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature Importance'}>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "train(model, X, y)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}