BigMart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Step1 :Import all necessary librabries\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import sklearn.linear_model\n",
    "import sklearn.ensemble\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Item_Fat_Content Item_Identifier  Item_MRP  Item_Outlet_Sales  \\\n",
      "0          Low Fat           FDA15  249.8092          3735.1380   \n",
      "1          Regular           DRC01   48.2692           443.4228   \n",
      "2          Low Fat           FDN15  141.6180          2097.2700   \n",
      "3          Regular           FDX07  182.0950           732.3800   \n",
      "4          Low Fat           NCD19   53.8614           994.7052   \n",
      "\n",
      "               Item_Type  Item_Visibility  Item_Weight  \\\n",
      "0                  Dairy         0.016047         9.30   \n",
      "1            Soft Drinks         0.019278         5.92   \n",
      "2                   Meat         0.016760        17.50   \n",
      "3  Fruits and Vegetables         0.000000        19.20   \n",
      "4              Household         0.000000         8.93   \n",
      "\n",
      "   Outlet_Establishment_Year Outlet_Identifier Outlet_Location_Type  \\\n",
      "0                       1999            OUT049               Tier 1   \n",
      "1                       2009            OUT018               Tier 3   \n",
      "2                       1999            OUT049               Tier 1   \n",
      "3                       1998            OUT010               Tier 3   \n",
      "4                       1987            OUT013               Tier 3   \n",
      "\n",
      "  Outlet_Size        Outlet_Type  Unnamed: 11 source  \n",
      "0      Medium  Supermarket Type1          NaN  train  \n",
      "1      Medium  Supermarket Type2          NaN  train  \n",
      "2      Medium  Supermarket Type1          NaN  train  \n",
      "3         NaN      Grocery Store          NaN  train  \n",
      "4        High  Supermarket Type1          NaN  train  \n"
     ]
    }
   ],
   "source": [
    "#Load trainSet and use header to known about trainset\n",
    "data=pd.read_csv('C:/Users/Gurudeo/Desktop/Personal/BigMart/data.csv')\n",
    "print(data.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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_MRP</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14204.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>14204.000000</td>\n",
       "      <td>11765.000000</td>\n",
       "      <td>14204.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>141.004977</td>\n",
       "      <td>2181.288914</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>12.792854</td>\n",
       "      <td>1997.830681</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>62.086938</td>\n",
       "      <td>1706.499616</td>\n",
       "      <td>0.051459</td>\n",
       "      <td>4.652502</td>\n",
       "      <td>8.371664</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31.290000</td>\n",
       "      <td>33.290000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.555000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>94.012000</td>\n",
       "      <td>834.247400</td>\n",
       "      <td>0.027036</td>\n",
       "      <td>8.710000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>142.247000</td>\n",
       "      <td>1794.331000</td>\n",
       "      <td>0.054021</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>185.855600</td>\n",
       "      <td>3101.296400</td>\n",
       "      <td>0.094037</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>266.888400</td>\n",
       "      <td>13086.964800</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>21.350000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Item_MRP  Item_Outlet_Sales  Item_Visibility   Item_Weight  \\\n",
       "count  14204.000000        8523.000000     14204.000000  11765.000000   \n",
       "mean     141.004977        2181.288914         0.065953     12.792854   \n",
       "std       62.086938        1706.499616         0.051459      4.652502   \n",
       "min       31.290000          33.290000         0.000000      4.555000   \n",
       "25%       94.012000         834.247400         0.027036      8.710000   \n",
       "50%      142.247000        1794.331000         0.054021     12.600000   \n",
       "75%      185.855600        3101.296400         0.094037     16.750000   \n",
       "max      266.888400       13086.964800         0.328391     21.350000   \n",
       "\n",
       "       Outlet_Establishment_Year  Unnamed: 11  \n",
       "count               14204.000000          0.0  \n",
       "mean                 1997.830681          NaN  \n",
       "std                     8.371664          NaN  \n",
       "min                  1985.000000          NaN  \n",
       "25%                  1987.000000          NaN  \n",
       "50%                  1999.000000          NaN  \n",
       "75%                  2004.000000          NaN  \n",
       "max                  2009.000000          NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe dataset\n",
    "data.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    8485\n",
       "Regular    4824\n",
       "LF          522\n",
       "reg         195\n",
       "low fat     178\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets\n",
    "data.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fruits and Vegetables    2013\n",
       "Snack Foods              1989\n",
       "Household                1548\n",
       "Frozen Foods             1426\n",
       "Dairy                    1136\n",
       "Baking Goods             1086\n",
       "Canned                   1084\n",
       "Health and Hygiene        858\n",
       "Meat                      736\n",
       "Soft Drinks               726\n",
       "Breads                    416\n",
       "Hard Drinks               362\n",
       "Others                    280\n",
       "Starchy Foods             269\n",
       "Breakfast                 186\n",
       "Seafood                    89\n",
       "Name: Item_Type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Type.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium    4655\n",
       "Small     3980\n",
       "High      1553\n",
       "Name: Outlet_Size, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Outlet_Size.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Supermarket Type1    9294\n",
       "Grocery Store        1805\n",
       "Supermarket Type3    1559\n",
       "Supermarket Type2    1546\n",
       "Name: Outlet_Type, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Outlet_Type.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tier 3    5583\n",
       "Tier 2    4641\n",
       "Tier 1    3980\n",
       "Name: Outlet_Location_Type, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Outlet_Location_Type.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_Missing=data.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content                 0\n",
       "Item_Identifier                  0\n",
       "Item_MRP                         0\n",
       "Item_Outlet_Sales             5681\n",
       "Item_Type                        0\n",
       "Item_Visibility                  0\n",
       "Item_Weight                   2439\n",
       "Outlet_Establishment_Year        0\n",
       "Outlet_Identifier                0\n",
       "Outlet_Location_Type             0\n",
       "Outlet_Size                   4016\n",
       "Outlet_Type                      0\n",
       "Unnamed: 11                  14204\n",
       "source                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_Missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x183a008048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filling value of Outlet_Size.\n",
    "#Hypothesis: Outlet_Size depends on Oulet Type and Outlet__Location_Type\n",
    "plt.scatter(data['Outlet_Size'],data['Outlet_Type'])\n",
    "plt.show()\n",
    "#from"
   ]
  },
  {
   "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",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0</td>\n",
       "      <td>1553</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>0</td>\n",
       "      <td>1550</td>\n",
       "      <td>1546</td>\n",
       "      <td>1559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>880</td>\n",
       "      <td>3100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Type  Grocery Store  Supermarket Type1  Supermarket Type2  \\\n",
       "Outlet_Size                                                        \n",
       "High                     0               1553                  0   \n",
       "Medium                   0               1550               1546   \n",
       "Small                  880               3100                  0   \n",
       "\n",
       "Outlet_Type  Supermarket Type3  \n",
       "Outlet_Size                     \n",
       "High                         0  \n",
       "Medium                    1559  \n",
       "Small                        0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twowaytable=pd.crosstab(data['Outlet_Size'],data['Outlet_Type'])\n",
    "twowaytable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#From above plot and table we can assume that Glacceray shops are of small size\n",
    "#SO map Glocessary Shop and Size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={'Grocery Store':'Small'}\n",
    "s=data.Outlet_Type.map(d)\n",
    "data.Outlet_Size=data.Outlet_Size.combine_first(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x183a01cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Outlet_Size'],data['Outlet_Location_Type'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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_Location_Type</th>\n",
       "      <th>Tier 1</th>\n",
       "      <th>Tier 2</th>\n",
       "      <th>Tier 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>1550</td>\n",
       "      <td>0</td>\n",
       "      <td>3105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>2430</td>\n",
       "      <td>1550</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Location_Type  Tier 1  Tier 2  Tier 3\n",
       "Outlet_Size                                 \n",
       "High                       0       0    1553\n",
       "Medium                  1550       0    3105\n",
       "Small                   2430    1550     925"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twowaytable=pd.crosstab(data['Outlet_Size'],data['Outlet_Location_Type'])\n",
    "twowaytable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#From plot and table we conclude that Tier 2 is of size small .\n",
    "#So map Tier 2 and Size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={'Tier 2':'Small'}\n",
    "s=data.Outlet_Location_Type.map(d)\n",
    "data.Outlet_Size=data.Outlet_Size.combine_first(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Outlet_Size.isnull().any()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill missing values of weight of Item According to means of Item Identifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Item_Weight']=data['Item_Weight'].fillna(data.groupby('Item_Identifier')['Item_Weight'].transform('mean'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Weight.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill Item_Weigth by theire corresponding Item_Type\n",
    "List=['Baking Goods','Breads','Breakfast','Canned','Dairy','Frozen Foods','Fruits and Vegetables','Hard Drinks','Health and Hygiene','Household','Meat','Others','Seafood','Snack Foods','Soft Drinks','Starchy Foods']\n",
    "Mean_values_Item_Type_data=data.groupby('Item_Type')['Item_Weight'].mean()\n",
    "for i in List:\n",
    "    d={i:Mean_values_Item_Type_data[i]}\n",
    "    s=data.Item_Type.map(d)\n",
    "    data.Item_Weight=data.Item_Weight.combine_first(s)\n",
    "Mean_values_Item_Type_data=data.groupby('Item_Type')['Item_Weight'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Weight.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000    879\n",
       "0.076856      3\n",
       "0.076841      3\n",
       "0.077290      3\n",
       "0.077169      3\n",
       "0.076975      3\n",
       "0.077011      3\n",
       "0.076792      3\n",
       "0.076483      3\n",
       "0.046899      2\n",
       "0.159844      2\n",
       "0.135708      2\n",
       "0.135944      2\n",
       "0.072298      2\n",
       "0.136008      2\n",
       "0.136896      2\n",
       "0.203401      2\n",
       "0.081788      2\n",
       "0.075049      2\n",
       "0.058369      2\n",
       "0.104784      2\n",
       "0.080131      2\n",
       "0.072411      2\n",
       "0.024635      2\n",
       "0.052069      2\n",
       "0.079968      2\n",
       "0.179192      2\n",
       "0.079806      2\n",
       "0.121767      2\n",
       "0.080625      2\n",
       "           ... \n",
       "0.013147      1\n",
       "0.098790      1\n",
       "0.073397      1\n",
       "0.137756      1\n",
       "0.017937      1\n",
       "0.105126      1\n",
       "0.044156      1\n",
       "0.141639      1\n",
       "0.094411      1\n",
       "0.074883      1\n",
       "0.039236      1\n",
       "0.044497      1\n",
       "0.051147      1\n",
       "0.175061      1\n",
       "0.039034      1\n",
       "0.077508      1\n",
       "0.078943      1\n",
       "0.077170      1\n",
       "0.042959      1\n",
       "0.013173      1\n",
       "0.069939      1\n",
       "0.011305      1\n",
       "0.069042      1\n",
       "0.012216      1\n",
       "0.064142      1\n",
       "0.209684      1\n",
       "0.019592      1\n",
       "0.013530      1\n",
       "0.008772      1\n",
       "0.066817      1\n",
       "Name: Item_Visibility, Length: 13006, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Visibility.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.016047\n",
       "1        0.019278\n",
       "2        0.016760\n",
       "3        0.000000\n",
       "4        0.000000\n",
       "5        0.000000\n",
       "6        0.012741\n",
       "7        0.127470\n",
       "8        0.016687\n",
       "9        0.094450\n",
       "10       0.000000\n",
       "11       0.045464\n",
       "12       0.100014\n",
       "13       0.047257\n",
       "14       0.068024\n",
       "15       0.069089\n",
       "16       0.008596\n",
       "17       0.069196\n",
       "18       0.034238\n",
       "19       0.102492\n",
       "20       0.138190\n",
       "21       0.035400\n",
       "22       0.025698\n",
       "23       0.057557\n",
       "24       0.025896\n",
       "25       0.099887\n",
       "26       0.066693\n",
       "27       0.019356\n",
       "28       0.161467\n",
       "29       0.072222\n",
       "           ...   \n",
       "14174    0.048645\n",
       "14175    0.087847\n",
       "14176    0.028977\n",
       "14177    0.000000\n",
       "14178    0.099375\n",
       "14179    0.158425\n",
       "14180    0.035911\n",
       "14181    0.037656\n",
       "14182    0.044073\n",
       "14183    0.026065\n",
       "14184    0.000000\n",
       "14185    0.026234\n",
       "14186    0.033516\n",
       "14187    0.045168\n",
       "14188    0.024110\n",
       "14189    0.014019\n",
       "14190    0.045270\n",
       "14191    0.288892\n",
       "14192    0.072529\n",
       "14193    0.037092\n",
       "14194    0.054463\n",
       "14195    0.036594\n",
       "14196    0.094053\n",
       "14197    0.030704\n",
       "14198    0.070411\n",
       "14199    0.013496\n",
       "14200    0.142991\n",
       "14201    0.073529\n",
       "14202    0.000000\n",
       "14203    0.104720\n",
       "Name: Item_Visibility, Length: 14204, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#From above observations therir are many zeros for item_Visiblity which not possible so \n",
    "#fill by corresponding means of Item_Identifiers\n",
    "data['Item_Visibility'].replace(0.00000,np.nan)#first fill by nam for simplicity\n",
    "data['Item_Visibility'].fillna(data.groupby('Item_Identifier')['Item_Visibility'].transform('mean'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.016047\n",
       "1        0.019278\n",
       "2        0.016760\n",
       "3        0.000000\n",
       "4        0.000000\n",
       "5        0.000000\n",
       "6        0.012741\n",
       "7        0.127470\n",
       "8        0.016687\n",
       "9        0.094450\n",
       "10       0.000000\n",
       "11       0.045464\n",
       "12       0.100014\n",
       "13       0.047257\n",
       "14       0.068024\n",
       "15       0.069089\n",
       "16       0.008596\n",
       "17       0.069196\n",
       "18       0.034238\n",
       "19       0.102492\n",
       "20       0.138190\n",
       "21       0.035400\n",
       "22       0.025698\n",
       "23       0.057557\n",
       "24       0.025896\n",
       "25       0.099887\n",
       "26       0.066693\n",
       "27       0.019356\n",
       "28       0.161467\n",
       "29       0.072222\n",
       "           ...   \n",
       "14174    0.048645\n",
       "14175    0.087847\n",
       "14176    0.028977\n",
       "14177    0.000000\n",
       "14178    0.099375\n",
       "14179    0.158425\n",
       "14180    0.035911\n",
       "14181    0.037656\n",
       "14182    0.044073\n",
       "14183    0.026065\n",
       "14184    0.000000\n",
       "14185    0.026234\n",
       "14186    0.033516\n",
       "14187    0.045168\n",
       "14188    0.024110\n",
       "14189    0.014019\n",
       "14190    0.045270\n",
       "14191    0.288892\n",
       "14192    0.072529\n",
       "14193    0.037092\n",
       "14194    0.054463\n",
       "14195    0.036594\n",
       "14196    0.094053\n",
       "14197    0.030704\n",
       "14198    0.070411\n",
       "14199    0.013496\n",
       "14200    0.142991\n",
       "14201    0.073529\n",
       "14202    0.000000\n",
       "14203    0.104720\n",
       "Name: Item_Visibility, Length: 14204, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Visibility'].fillna(data.groupby('Item_Identifier')['Item_Visibility'].transform('mean'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Visibility.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Feature Engineering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    8485\n",
       "Regular    4824\n",
       "LF          522\n",
       "reg         195\n",
       "low fat     178\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fat_Content showing redudancy of differnt types\n",
    "data.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    9185\n",
       "Regular    5019\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now replace LF by Low Fat ,low fat by lf,reg by Regular\n",
    "data['Item_Fat_Content']=data['Item_Fat_Content'].replace({'low fat':'Low Fat','reg':'Regular','LF':'Low Fat'})\n",
    "data.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#no of years outlet is working conditon \n",
    "data['Outlet_Years']=2018-data['Outlet_Establishment_Year']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mean_Visibility=data['Item_Visibility'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Item_Visibility_MeanRatio']=data.apply(lambda x:x['Item_Visibility']/Mean_Visibility,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As Item Id and Outlet Id\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert categorical into numerical \n",
    "var_mod=['Item_Fat_Content','Outlet_Location_Type','Outlet_Size','Outlet_Type','Item_Type']\n",
    "number=LabelEncoder()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Item_Identifier and outlet_Identifier are also useful for making prediction\n",
    "data['Outlet']=number.fit_transform(data['Outlet_Identifier'])\n",
    "data['Identifier']=number.fit_transform(data['Item_Identifier'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in var_mod:\n",
    "      data[i]=number.fit_transform(data[i])\n"
   ]
  },
  {
   "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_Fat_Content</th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>source</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Item_Visibility_MeanRatio</th>\n",
       "      <th>Outlet</th>\n",
       "      <th>Identifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FDA15</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>4</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>9.30</td>\n",
       "      <td>1999</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "      <td>19</td>\n",
       "      <td>0.243315</td>\n",
       "      <td>9</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>DRC01</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>14</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>5.92</td>\n",
       "      <td>2009</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "      <td>9</td>\n",
       "      <td>0.292303</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>FDN15</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>10</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>17.50</td>\n",
       "      <td>1999</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "      <td>19</td>\n",
       "      <td>0.254122</td>\n",
       "      <td>9</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>FDX07</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1998</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>NCD19</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.93</td>\n",
       "      <td>1987</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "      <td>31</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Fat_Content Item_Identifier  Item_MRP  Item_Outlet_Sales  Item_Type  \\\n",
       "0                 0           FDA15  249.8092          3735.1380          4   \n",
       "1                 1           DRC01   48.2692           443.4228         14   \n",
       "2                 0           FDN15  141.6180          2097.2700         10   \n",
       "3                 1           FDX07  182.0950           732.3800          6   \n",
       "4                 0           NCD19   53.8614           994.7052          9   \n",
       "\n",
       "   Item_Visibility  Item_Weight  Outlet_Establishment_Year Outlet_Identifier  \\\n",
       "0         0.016047         9.30                       1999            OUT049   \n",
       "1         0.019278         5.92                       2009            OUT018   \n",
       "2         0.016760        17.50                       1999            OUT049   \n",
       "3         0.000000        19.20                       1998            OUT010   \n",
       "4         0.000000         8.93                       1987            OUT013   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Size  Outlet_Type  Unnamed: 11 source  \\\n",
       "0                     0            1            1          NaN  train   \n",
       "1                     2            1            2          NaN  train   \n",
       "2                     0            1            1          NaN  train   \n",
       "3                     2            2            0          NaN  train   \n",
       "4                     2            0            1          NaN  train   \n",
       "\n",
       "   Outlet_Years  Item_Visibility_MeanRatio  Outlet  Identifier  \n",
       "0            19                   0.243315       9         156  \n",
       "1             9                   0.292303       3           8  \n",
       "2            19                   0.254122       9         662  \n",
       "3            20                   0.000000       0        1121  \n",
       "4            31                   0.000000       1        1297  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors=['Item_Weigth','Item_Fat_Content','Item_Visibility','Item_Type','Item_MRP','Outlet_Size','Outlet_Location_Type','Outlet_Type','Outlet_Years','Item_Visibility_MeanRatio'\n",
    "            'Outlet','Identifier']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[data.columns[1:]]\n",
    "y=data['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Linear_Model=LinearRegression(normalize=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}