BIG-MART+SALES+PREDICTION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train=pd.read_csv(r'C:\\Users\\naveen chauhan\\Desktop\\mldata\\mlp\\Big Mart Sale Prediction\\Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test=pd.read_csv(r'C:\\Users\\naveen chauhan\\Desktop\\mldata\\mlp\\Big Mart Sale Prediction\\Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
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       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDW58</td>\n",
       "      <td>20.750</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.007565</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.8622</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDW14</td>\n",
       "      <td>8.300</td>\n",
       "      <td>reg</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NCN55</td>\n",
       "      <td>14.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>Others</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDQ58</td>\n",
       "      <td>7.315</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDW58       20.750          Low Fat         0.007565  Snack Foods   \n",
       "1           FDW14        8.300              reg         0.038428        Dairy   \n",
       "2           NCN55       14.600          Low Fat         0.099575       Others   \n",
       "3           FDQ58        7.315          Low Fat         0.015388  Snack Foods   \n",
       "4           FDY38          NaN          Regular         0.118599        Dairy   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  107.8622            OUT049                       1999      Medium   \n",
       "1   87.3198            OUT017                       2007         NaN   \n",
       "2  241.7538            OUT010                       1998         NaN   \n",
       "3  155.0340            OUT017                       2007         NaN   \n",
       "4  234.2300            OUT027                       1985      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  \n",
       "0               Tier 1  Supermarket Type1  \n",
       "1               Tier 2  Supermarket Type1  \n",
       "2               Tier 3      Grocery Store  \n",
       "3               Tier 2  Supermarket Type1  \n",
       "4               Tier 3  Supermarket Type3  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5681, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                   976\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  1606\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "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",
       "      <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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253bbcc390>"
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     "execution_count": 11,
     "metadata": {},
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HMPZuvvO2tkNQY9XdP5jTV99tu+3o581rLrHF+89u5X2w4q0ZNSdkXgxcMZekW5Ik6QGz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\nevD/TQyeFNg0USQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253bb927b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now plot correlation matrix\n",
    "correl=train.corr()\n",
    "ax=plt.subplots(figsize=(15,9))\n",
    "sns.heatmap(correl,vmax=0.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5089\n",
       "Regular    2889\n",
       "LF          316\n",
       "reg         117\n",
       "low fat     112\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Fat_Content.value_counts()  #need to optimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: Item_Type, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OUT027    935\n",
       "OUT013    932\n",
       "OUT046    930\n",
       "OUT049    930\n",
       "OUT035    930\n",
       "OUT045    929\n",
       "OUT018    928\n",
       "OUT017    926\n",
       "OUT010    555\n",
       "OUT019    528\n",
       "Name: Outlet_Identifier, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Outlet_Identifier.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium    2793\n",
       "Small     2388\n",
       "High       932\n",
       "Name: Outlet_Size, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Outlet_Size.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tier 3    3350\n",
       "Tier 2    2785\n",
       "Tier 1    2388\n",
       "Name: Outlet_Location_Type, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Outlet_Location_Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Supermarket Type1    5577\n",
       "Grocery Store        1083\n",
       "Supermarket Type3     935\n",
       "Supermarket Type2     928\n",
       "Name: Outlet_Type, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Outlet_Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253bc9f3c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9h3mX3Zbq1KHuWyPieNM2sK3DvCO57EiHmj/RVO/RiHikw7wjWdcRcVFEfD4inoyIJyLi\nXVX7WG/XfcvMIm7AUWDTMs9vAz4DBHAF8OVR19xS3wuA/6RxXGxz+yxw/4hrex1wKfB4U9ufAnuq\n6T3AB9vMtxH4enW/oZreMMKarwLOqaY/2K7mXralEdR9K/D7PWw/TwO/ALwIeBS4ZFQ1tzx/O/C+\ncVrXwGbg0mr6xcC/07hcy1hv1/3eitmj78F24O5seAhYHxGbR11UkyuBpzPzG6MupFVmfgH4Tkvz\ndmBfNb0PuLbNrK8HHszM72Tmc8CDwNW1FdqkXc2Z+bnMPFs9fIjG+R1jpcO67sXILjuyXM0REcCb\ngXuGUUuvMvNEZj5cTX8feJLG2ftjvV33q6SgT+BzEXG4Ouu21bhfluF6On8YfjUiHo2Iz0TEK4ZZ\n1DKmMvMEND40wAVt+ozzOn87jX/htdNtWxqFW6ohpzs7DCeM67r+DeBkZj7V4fmRr+uImAZeA3yZ\nyd+u2yop6F+bmZfSuGLmzRHxupbno808Y3HIUXVS2RuBf2jz9MM0hnNeBfwl8I/DrG2VxnKdR8R7\ngbPAxzt06bYtDdtHgV8EXg2coDEU0mos1zVwA8vvzY90XUfEOuCTwLsz83u9ztambRzWdUfFBH1m\nPlPdnwI+TeOfss26XpZhhK4BHs7Mk61PZOb3MvNMNX0AeGFEbBp2gW2cXBr6qu5Ptekzduu8+uHs\nt4AbsxpwbdXDtjRUmXkyM3+Umf8LfKxDPeO4rs8Bfhv4RKc+o1zXEfFCGiH/8cz8VNU8kdt1N0UE\nfUScFxEvXpqm8aPb4y3d9gNvrY6+uQI4vfRPtDHQca8nIn6uGuckIi6n8Z59e4i1dbIfWDraYAdw\nX5s+nwWuiogN1XDDVVXbSETE1cB7gDdm5g879OllWxqqlt+S3kT7esbxsiO/CXw1M4+1e3KU67r6\nTN0BPJmZH2p6auK2656M+tfgQdxoHGnwaHV7Anhv1f4O4B3VdND4z1CeBhaBmVHXXdX1MzSC+/ym\ntua6b6n+pkdp/ID4ayOo8R4aQwb/Q2Nv5ibgZ4GDwFPV/caq7wzwN03zvh04Ut3eNuKaj9AYW32k\nuv111felwIHltqUR1/231Tb7GI0g2txad/V4G42jR54eZt3taq7a71rajpv6jsW6Bn6dxnDLY03b\nw7Zx3677vXlmrCQVroihG0lSZwa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mF+z/+2WEA\nHz7I7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253bea6d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.Item_Weight.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253bbe8be0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B04BdVXVgnGOQJPWN+xIQVbUX2Duml1uRS0nScTi/tJpWfX6lqhZuJUk65fhVEJLUqJMu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\nkhplAEhSowwASWqUASBJjTIAJKlR/wfm0X0dotEO8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253bf07198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.Outlet_Size.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium    2793\n",
       "Small     2388\n",
       "High       932\n",
       "Name: Outlet_Size, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Outlet_Size.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Item_Sales=train.Item_Outlet_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=train.append(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14204, 12)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253c280b00>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8DHh7/bkh6dmSXgSsTvWZPSjphVTJPiKWQfZIz1sTun4Dr5uf9gK+JOlfqRLa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253bf07d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation=data.corr()\n",
    "sns.heatmap(correlation,vmax=.8,square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content                 5\n",
       "Item_Identifier               1559\n",
       "Item_MRP                      8052\n",
       "Item_Outlet_Sales             3494\n",
       "Item_Type                       16\n",
       "Item_Visibility              13006\n",
       "Item_Weight                    416\n",
       "Outlet_Establishment_Year        9\n",
       "Outlet_Identifier               10\n",
       "Outlet_Location_Type             3\n",
       "Outlet_Size                      4\n",
       "Outlet_Type                      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x:len(x.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Fat_Content              object\n",
       "Item_Identifier               object\n",
       "Item_MRP                     float64\n",
       "Item_Outlet_Sales            float64\n",
       "Item_Type                     object\n",
       "Item_Visibility              float64\n",
       "Item_Weight                  float64\n",
       "Outlet_Establishment_Year      int64\n",
       "Outlet_Identifier             object\n",
       "Outlet_Location_Type          object\n",
       "Outlet_Size                   object\n",
       "Outlet_Type                   object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Fat_Content', 'Item_Identifier', 'Item_MRP', 'Item_Outlet_Sales',\n",
       "       'Item_Type', 'Item_Visibility', 'Item_Weight',\n",
       "       'Outlet_Establishment_Year', 'Outlet_Identifier',\n",
       "       'Outlet_Location_Type', 'Outlet_Size', 'Outlet_Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Fat_Content',\n",
       " 'Item_Identifier',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns=[x for x in data.dtypes.index if data.dtypes[x]=='object']\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Type']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns=[x for x in categorical_columns if x not in ['Item_Identifier','Outlet_Identifier']]\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frequency of categories for variable\n",
      "Low Fat    8485\n",
      "Regular    4824\n",
      "LF          522\n",
      "reg         195\n",
      "low fat     178\n",
      "Name: Item_Fat_Content, dtype: int64\n",
      "frequency of categories for variable\n",
      "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\n",
      "frequency of categories for variable\n",
      "Tier 3    5583\n",
      "Tier 2    4641\n",
      "Tier 1    3980\n",
      "Name: Outlet_Location_Type, dtype: int64\n",
      "frequency of categories for variable\n",
      "Medium    4655\n",
      "Small     3980\n",
      "High      1553\n",
      "Name: Outlet_Size, dtype: int64\n",
      "frequency of categories for variable\n",
      "Supermarket Type1    9294\n",
      "Grocery Store        1805\n",
      "Supermarket Type3    1559\n",
      "Supermarket Type2    1546\n",
      "Name: Outlet_Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#print frequencies of these categories\n",
    "for col in categorical_columns:\n",
    "    print('frequency of categories for variable')\n",
    "    print(data[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.Item_Weight.fillna(data.Item_Weight.mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import mode function\n",
    "from scipy.stats import mode\n",
    "\n",
    "#determining the mode of each \n",
    "data.Outlet_Size=data.Outlet_Size.map({'Small':0,'Medium':1,'High':2})\n",
    "outlet_size_mode = data.pivot_table(values='Outlet_Size', columns='Outlet_Type',aggfunc=(lambda x:mode(x).mode[0]) )\n",
    "miss_bool = data['Outlet_Size'].isnull() \n",
    "data.loc[miss_bool,'Outlet_Size'] = data.loc[miss_bool,'Outlet_Type'].apply(lambda x: outlet_size_mode[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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                     0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Size                     0\n",
       "Outlet_Type                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Fat_Content \n",
      " Low Fat    8485\n",
      "Regular    4824\n",
      "LF          522\n",
      "reg         195\n",
      "low fat     178\n",
      "Name: Item_Fat_Content, dtype: int64\n",
      "Item_Type \n",
      " 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\n",
      "Outlet_Establishment_Year \n",
      " 1985    2439\n",
      "1987    1553\n",
      "1999    1550\n",
      "1997    1550\n",
      "2004    1550\n",
      "2002    1548\n",
      "2009    1546\n",
      "2007    1543\n",
      "1998     925\n",
      "Name: Outlet_Establishment_Year, dtype: int64\n",
      "Outlet_Identifier \n",
      " OUT027    1559\n",
      "OUT013    1553\n",
      "OUT046    1550\n",
      "OUT049    1550\n",
      "OUT035    1550\n",
      "OUT045    1548\n",
      "OUT018    1546\n",
      "OUT017    1543\n",
      "OUT010     925\n",
      "OUT019     880\n",
      "Name: Outlet_Identifier, dtype: int64\n",
      "Outlet_Location_Type \n",
      " Tier 3    5583\n",
      "Tier 2    4641\n",
      "Tier 1    3980\n",
      "Name: Outlet_Location_Type, dtype: int64\n",
      "Outlet_Size \n",
      " 0.0    7996\n",
      "1.0    4655\n",
      "2.0    1553\n",
      "Name: Outlet_Size, dtype: int64\n",
      "Outlet_Type \n",
      " Supermarket Type1    9294\n",
      "Grocery Store        1805\n",
      "Supermarket Type3    1559\n",
      "Supermarket Type2    1546\n",
      "Name: Outlet_Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in data.dtypes.index:\n",
    "    if len(data[i].value_counts())<30:\n",
    "        print(i,\"\\n\",data[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Grocery Store</th>\n",
       "      <td>339.828500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <td>2316.181148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <td>1995.498739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket Type3</th>\n",
       "      <td>3694.038558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Item_Outlet_Sales\n",
       "Outlet_Type                         \n",
       "Grocery Store             339.828500\n",
       "Supermarket Type1        2316.181148\n",
       "Supermarket Type2        1995.498739\n",
       "Supermarket Type3        3694.038558"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(index='Outlet_Type',values='Item_Outlet_Sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253c36ccf8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ic9R0vnR+zdJDXFP6dfIF3dCoQ9cDICIuBm4FjgO+lJnrul2DdCyaTmB0e9sGxtzW1QCI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\nSaqUASBJlTIAJKlS/w9z8GiWpHsBEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253c2e84e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.Item_Visibility.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06595278007399345"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Visibility.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.loc[data['Item_Visibility']==0,'Item_Visibility']=data.Item_Visibility.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FD    10201\n",
       "NC     2686\n",
       "DR     1317\n",
       "Name: Item_Type_Combined, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Type_Combined']=data.Item_Identifier.apply(lambda x:x[0:2])\n",
    "data['Item_Type_Combined'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Item_Type_Combined']=data.Item_Type_Combined.map({'FD':'Food and Drinks','NC':'Non-Consumable','DR':'Drinks'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food and Drinks    10201\n",
       "Non-Consumable      2686\n",
       "Drinks              1317\n",
       "Name: Item_Type_Combined, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Item_Type_Combined'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14204.000000\n",
       "mean        15.169319\n",
       "std          8.371664\n",
       "min          4.000000\n",
       "25%          9.000000\n",
       "50%         14.000000\n",
       "75%         26.000000\n",
       "max         28.000000\n",
       "Name: Outlet_Years, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Outlet_Years']=2013-data['Outlet_Establishment_Year']\n",
    "data['Outlet_Years'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    9185\n",
       "Regular    5019\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Fat_Content=data.Item_Fat_Content.replace({'LF':'Low Fat','reg':'Regular','low fat':'Low Fat'})\n",
    "data.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['Item_Type_Combined']=='Non-Consumable','Item_Fat_Content']='Non-Edible'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat       6499\n",
       "Regular       5019\n",
       "Non-Edible    2686\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Item_Fat_Content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Item_Type_Combined</th>\n",
       "      <th>Outlet_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDA15</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>9.30</td>\n",
       "      <td>1999</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>Food and Drinks</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Regular</td>\n",
       "      <td>DRC01</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>5.92</td>\n",
       "      <td>2009</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>FDN15</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>Meat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>17.50</td>\n",
       "      <td>1999</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>Food and Drinks</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Regular</td>\n",
       "      <td>FDX07</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1998</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Food and Drinks</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>NCD19</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>8.93</td>\n",
       "      <td>1987</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>Non-Consumable</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  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       Non-Edible           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         0.065953        19.20   \n",
       "4              Household         0.065953         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 Item_Type_Combined  Outlet_Years  \n",
       "0          1.0  Supermarket Type1    Food and Drinks            14  \n",
       "1          1.0  Supermarket Type2             Drinks             4  \n",
       "2          1.0  Supermarket Type1    Food and Drinks            14  \n",
       "3          0.0      Grocery Store    Food and Drinks            15  \n",
       "4          2.0  Supermarket Type1     Non-Consumable            26  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import library \n",
    "#now import labelEncoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "lb=LabelEncoder()\n",
    "data['Outlet']=lb.fit_transform(data['Outlet_Identifier'])\n",
    "var=['Item_Fat_Content','Outlet_Location_Type','Outlet_Type','Outlet_Size','Item_Type_Combined']\n",
    "lb=LabelEncoder()\n",
    "for item in var:\n",
    "    data[item]=lb.fit_transform(data[item])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "  <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>Item_Type_Combined</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</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>Dairy</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>1</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>DRC01</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>Soft Drinks</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>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</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>Meat</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>1</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>FDX07</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1998</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>NCD19</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>Household</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>8.93</td>\n",
       "      <td>1987</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Fat_Content Item_Identifier  Item_MRP  Item_Outlet_Sales  \\\n",
       "0                 0           FDA15  249.8092          3735.1380   \n",
       "1                 2           DRC01   48.2692           443.4228   \n",
       "2                 0           FDN15  141.6180          2097.2700   \n",
       "3                 2           FDX07  182.0950           732.3800   \n",
       "4                 1           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         0.065953        19.20   \n",
       "4              Household         0.065953         8.93   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Identifier  Outlet_Location_Type  \\\n",
       "0                       1999            OUT049                     0   \n",
       "1                       2009            OUT018                     2   \n",
       "2                       1999            OUT049                     0   \n",
       "3                       1998            OUT010                     2   \n",
       "4                       1987            OUT013                     2   \n",
       "\n",
       "   Outlet_Size  Outlet_Type  Item_Type_Combined  Outlet_Years  Outlet  \n",
       "0            1            1                   1            14       9  \n",
       "1            1            2                   0             4       3  \n",
       "2            1            1                   1            14       9  \n",
       "3            0            0                   1            15       0  \n",
       "4            2            1                   2            26       1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.drop(['Outlet_Establishment_Year','Item_Type'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
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       "  <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_Visibility</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Type_Combined</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</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>0.016047</td>\n",
       "      <td>9.30</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>DRC01</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>5.92</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</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>0.016760</td>\n",
       "      <td>17.50</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>FDX07</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>19.20</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>NCD19</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>8.93</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Fat_Content Item_Identifier  Item_MRP  Item_Outlet_Sales  \\\n",
       "0                 0           FDA15  249.8092          3735.1380   \n",
       "1                 2           DRC01   48.2692           443.4228   \n",
       "2                 0           FDN15  141.6180          2097.2700   \n",
       "3                 2           FDX07  182.0950           732.3800   \n",
       "4                 1           NCD19   53.8614           994.7052   \n",
       "\n",
       "   Item_Visibility  Item_Weight Outlet_Identifier  Outlet_Location_Type  \\\n",
       "0         0.016047         9.30            OUT049                     0   \n",
       "1         0.019278         5.92            OUT018                     2   \n",
       "2         0.016760        17.50            OUT049                     0   \n",
       "3         0.065953        19.20            OUT010                     2   \n",
       "4         0.065953         8.93            OUT013                     2   \n",
       "\n",
       "   Outlet_Size  Outlet_Type  Item_Type_Combined  Outlet_Years  Outlet  \n",
       "0            1            1                   1            14       9  \n",
       "1            1            2                   0             4       3  \n",
       "2            1            1                   1            14       9  \n",
       "3            0            0                   1            15       0  \n",
       "4            2            1                   2            26       1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Item_Sales=data.Item_Outlet_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Item_Fat_Content</th>\n",
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       "      <th>Item_Weight</th>\n",
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       "      <th>Item_Type_Combined</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</th>\n",
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       "      <th>0</th>\n",
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       "      <td>FDA15</td>\n",
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       "      <td>443.4228</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>5.92</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>FDN15</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>17.50</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>9</td>\n",
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       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>FDX07</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>19.20</td>\n",
       "      <td>OUT010</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>NCD19</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>0.065953</td>\n",
       "      <td>8.93</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Fat_Content Item_Identifier  Item_MRP  Item_Outlet_Sales  \\\n",
       "0                 0           FDA15  249.8092          3735.1380   \n",
       "1                 2           DRC01   48.2692           443.4228   \n",
       "2                 0           FDN15  141.6180          2097.2700   \n",
       "3                 2           FDX07  182.0950           732.3800   \n",
       "4                 1           NCD19   53.8614           994.7052   \n",
       "\n",
       "   Item_Visibility  Item_Weight Outlet_Identifier  Outlet_Location_Type  \\\n",
       "0         0.016047         9.30            OUT049                     0   \n",
       "1         0.019278         5.92            OUT018                     2   \n",
       "2         0.016760        17.50            OUT049                     0   \n",
       "3         0.065953        19.20            OUT010                     2   \n",
       "4         0.065953         8.93            OUT013                     2   \n",
       "\n",
       "   Outlet_Size  Outlet_Type  Item_Type_Combined  Outlet_Years  Outlet  \n",
       "0            1            1                   1            14       9  \n",
       "1            1            2                   0             4       3  \n",
       "2            1            1                   1            14       9  \n",
       "3            0            0                   1            15       0  \n",
       "4            2            1                   2            26       1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=data.iloc[:8523,:]\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test=data.iloc[8523:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\naveen chauhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test.drop('Item_Outlet_Sales',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "  <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",
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       "      <th>Item_Weight</th>\n",
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       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
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       "      <td>20.750000</td>\n",
       "      <td>OUT049</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FDW14</td>\n",
       "      <td>87.3198</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NCN55</td>\n",
       "      <td>241.7538</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>FDQ58</td>\n",
       "      <td>155.0340</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>7.315000</td>\n",
       "      <td>OUT017</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>FDY38</td>\n",
       "      <td>234.2300</td>\n",
       "      <td>0.118599</td>\n",
       "      <td>12.792854</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Fat_Content Item_Identifier  Item_MRP  Item_Visibility  Item_Weight  \\\n",
       "0                 0           FDW58  107.8622         0.007565    20.750000   \n",
       "1                 2           FDW14   87.3198         0.038428     8.300000   \n",
       "2                 1           NCN55  241.7538         0.099575    14.600000   \n",
       "3                 0           FDQ58  155.0340         0.015388     7.315000   \n",
       "4                 2           FDY38  234.2300         0.118599    12.792854   \n",
       "\n",
       "  Outlet_Identifier  Outlet_Location_Type  Outlet_Size  Outlet_Type  \\\n",
       "0            OUT049                     0            1            1   \n",
       "1            OUT017                     1            0            1   \n",
       "2            OUT010                     2            0            0   \n",
       "3            OUT017                     1            0            1   \n",
       "4            OUT027                     2            1            3   \n",
       "\n",
       "   Item_Type_Combined  Outlet_Years  Outlet  \n",
       "0                   1            14       9  \n",
       "1                   1             6       2  \n",
       "2                   2            15       0  \n",
       "3                   1             6       2  \n",
       "4                   1            28       5  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A generalization function to prediction and file on sharing\n",
    "target='Item_Outlet_Sales'\n",
    "IDcol=['Item_Identifier','Outlet_Identifier']\n",
    "from sklearn import model_selection ,metrics\n",
    "def modelfit(alg,dtrain,dtest,predictor,target,IDcol,filename):\n",
    "    alg.fit(dtrain[predictor],dtrain[target])\n",
    "    prediction=alg.predict(dtrain[predictor])\n",
    "    #now cross_validation\n",
    "    cv_score=model_selection.cross_val_score(alg,dtrain[predictor],dtrain[target],cv=20,scoring='neg_mean_squared_error')\n",
    "    cv_score=np.sqrt(np.abs(cv_score))\n",
    "    print(np.sqrt(metrics.mean_squared_error(dtrain[target].values,prediction)))\n",
    "    print(\"CV_SCORE : mean - %.4g | std - %.4g | max - %.4g | min - %.4g\" % (np.mean(cv_score),np.std(cv_score),np.max(cv_score),np.min(cv_score)))\n",
    "    dtest[target]=alg.predict(dtest[predictor])\n",
    "    \n",
    "    #now export on submission file \n",
    "    IDcol.append(target)\n",
    "    submission=pd.DataFrame({x:dtest[x] for x in IDcol})\n",
    "    submission.to_csv(\"C:\\\\Users\\\\naveen chauhan\\\\Desktop\\\\mldata\\\\mlp\\\\Big Mart Sale Prediction\\\\\"+filename,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1202.09312885\n",
      "CV_SCORE : mean - 1203 | std - 41.54 | max - 1282 | min - 1153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\naveen chauhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "#Linear Regression on training set\n",
    "from sklearn.linear_model import LinearRegression , Ridge,Lasso\n",
    "predictor=[x for x in train.columns if x not in [target]+IDcol]\n",
    "alg1=LinearRegression()\n",
    "modelfit(alg1,train,test,predictor,target,IDcol,'alg1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1203.63398626\n",
      "CV_SCORE : mean - 1204 | std - 42.83 | max - 1288 | min - 1151\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\naveen chauhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253caf4160>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TG5kn6b3Axiw8NLrZhNMZrgPGOpFvWJ2PcJ+9Gmz/dPi5pLNsj/P28MzbfjPj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\nZ/o7KDvVtfAj4EZgLeCjQ+X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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253caf4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target]+IDcol]\n",
    "alg2 = Ridge(alpha=0.05,normalize=True)\n",
    "modelfit(alg2, train, test, predictors, target, IDcol, 'alg2.csv')\n",
    "coef2 = pd.Series(alg2.coef_, predictors).sort_values()\n",
    "coef2.plot(kind='bar', title='Model Coefficients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1059.29558737\n",
      "CV_SCORE : mean - 1093 | std - 45.4 | max - 1182 | min - 1012\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\naveen chauhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253e39c400>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AVcpgf4TlN3F/BrzV9tXjarOLyX2ct+Rb3bi1p5p++AXgw1Wdm/+itodrg8ND\nH+o7rtfubvqSsNWVoTXflPR826c02Wi1WOm9lCm6v6H8rm4OHCvp57bvWtXXd9BQN28lbW/7onEH\nQ+n4HE3p+AC8pDo3tqGpLg7LrDTPeISv/TgPsZ1eA+OJvXZeDryHMiOhl9wbGx6aJNVsqqOBpwG/\no1oZ6rJxdZNxLKXM4FnG8k3d7TEX7pL0EcoN7Tf3ZitVs5eOBO60ffA425+pCbrhObZ80dfOSouY\nxr2wqYvJvfVfmgbGE7en9Navp/wxt9FDnXKOeU9Tc8z7YrpvZWjTbbdJ0uWUlcruOz8XuNT21g3H\n84H+4an6uWE7SuPWwN9q7039UOBmys3m3tDleuO84d/FYZkvtR0A4x+S+DJwcMNz6gdpfY55dZP7\nTMoGJT8Dft52Ype0O8sXp/zQ9ljrdlfcn9irk/f01kE0bBeg/97Drr1zk5DYK+P+3lzEitUx61dQ\npkxhHotZ13Ovyg+8gZWnmjW6y/2qjPtST9IDPcQOVE0ND7Wp6hntRBmOeRqlSNaVVMneDdWUqcXz\nfsqmKV+oTu0DnGP7kDG3+zXgq7Y/13d+X+Afmvr7kPRa4HWU0r71WunrU34ekzZrp5FhmTbMxuR+\nPvBpys5D9ZuIP2otqD6TMDTUZBxV/Y53A3cC/0OZc/8m258fd9sDYnkQpUDUm4Atbc9tuP0LKMvd\n762O5wK/8JhrqlR1hb5K+Rn0aqnvSCnTsWdvMdG4VbV9HkKZsVZ/Q1va5IrQYUk60/ZODbQzh7LY\ncD4rdkqziKnmj+P8hgxjuvFESu2ZSdDUO/dzXYpD7UlZNLMX8ANg7Mld0iYs77XvWJ0+B/hn2tkr\nE0oJgl4i27CJBqvk/VRJO1Nq3Ag41fb3mmi/FsdtlCmY+1SL2ba2fZykjdVAdcxBqje+/jrqP67+\nHXtir3yd8ve4Qqd0nGZjz/0lwNbAt1mxulpjm3UMupSrV8ObFA3OBLjI9vaSjgG+Yvt/JJ1v+88b\naPteSuG0jwBfcsP1wgfEsw/wfsqbmyhj74faXmnVZpdJOhxYAGzrUrF0E8rPp7GFbVUcH6DcvLyY\n5YvK3PQwrqQLbT++yTZnY8/98ZSe8c7Upv/RQGGk+nhidfndsz7lZt6kaWpe/jckXUoZEnidpHlA\nUwXdnk6ZK7wn8BaVvXbPqD4WDXNvYpRc6oj8kHIVIeDttv+3yRgmxJ6sWLH0eklt3Hh/IeUNptHf\ngwFO0/IN5RsxG3vulwI7tNFDm7TxxEmabqayccnt1eyM9YAN2khq1UrA3SizEjaz3Ui5XUl/A6xv\n+8t9518K3GT7O03EMSmqhVNGqgCmAAAPLElEQVRP6V09VvdCzmj66lbSqcBebn9jnz0o+8uasgai\nt3H5wI3ER9LmLEzuXwTeYPumluNYYTyR8ofd6Hhi28NDvZ6Ipiim1tRKWZVa6r1x96dT3oDPoMzO\nOLKhGM4EdrO9pO/8Iym7hnV61lI/Sf+XMny6C6UztD9wgu2PNRzHVyg3+L/HisO4jW4kI+lKyv4L\n/RNBxrbl3mwclnkEcKmks1nxh9XYGFp9PJFS/XBtys3DRsYTJ2h46JmU1bG7DXjMlNkbYyXpt5Qy\nA72CXe+3fcW42x1gvf7EDmD7f6te6/2K7SMl7UIpz70tcFhLVy8Lq4+2XU6ZNdVYb3o29tyfOeh8\nk1MhJZ1HNZ7o5RUIm+wxT8TwkKRH2L6xqfamiGHDaobGdM8bW7XQ6vV/BWxn++6+8w+gbL3Y6ArR\nWE7S2pQd2wAucwt1diQdR5kGeQordkrHNvNvzrheeFyqJH418IDq87Opbto0aFn1DtwrUtVoz8z2\nbbavdtmAYXNgZ5caKnOqRV5NOV/SdyTtX73hNG6YxF7Za6yBlKuUY+q/C9Xnn6CBK5hJIWmppNtr\n/95eP24hnmdRes1HAf8J/EoDtiFswGLgp5RtF+fVPsZmNvbcXwUcCDzU9laStgY+YfvZDcYwKeOJ\nrU43qxboPIey/eHzKePcJwILbd/ZRAzDaqCGyFqUhVyvpGzcDmWXrE8D/9JGbzFA0jnAS2xfVh1v\nQymD3MiG5QPiWRegib+P2Zjcz6PsC3lWbUik8Tmk1Xjicyl3vU9rYzyx7eGhvljWptQO2Rv4a+B7\ntl/adBxTaXDO/7rA/6kOr+j/I5a0y/1h5oyk422/bLpzDcSx0t9DG38jkrYDPkvZflCUnvx+ti9Z\n5Reugdl4Q/VPtpep2pSh6jE1/g5V/YG2/Ue6zF5hg+zWbtxVP5OLgUso9V22ayuWKTQy579K5heu\n4ikfoP3fmyZsXz+o/k7b6C0vkvRp4Pjq+KWUFcxNOxp4R++NXdJzgGOAZ4yrwVk35g78SGWPynWr\n3vOXgG800fCkjScCJ6vsgLRRNVz1XcovTGMkPVrS2ySdC3yTsgvQHuMcAllNk1AtFJpbWNYKSYeq\n1LTfof73AdxIWYLftNdSKjO+kbL24WLgNS3EsX79is32dxlzNdXZOCwzBziAFYdEGk1ok6TN4SGV\n/UI3pSTOk2wvaqrtAbFMfLVQ6HYVwjpJ77N9aNtxTApJX6eUpu5dQewLPG2cv5+zMbkfbPuj050b\ncwwTMZ7Ytmpa6o+nm7s77mmIVRsTXy0U7lfJfeCMFFcFuxpo/2Tb/yDpQgYM27Yw5v4w4N9YPgzz\nY+Bw2zePrc1ZmNwHrcpstMRufwzVeOIFthsZZ64uc3sbANR/gL0lzWPd0m2mmkhoGvOWZTOIY6Va\n+/Vzkr5qe+CK3i6RVB8qXYcyCeIc22OvAVW1/yjbN0jaYtDjbmj7RUkPBB7cn8RVVrUv7f9dGaVZ\nc0NVpdreS4AtJdVXnK1P2b6qiRgOBXrj/b0xdlFqRRzdRAwAtlvb+Wg1NTHO/NFqamhr1UIrZwD9\nb2T3nbs/JHYA2yusWpa0OfDBBtvvbT35Og+ov8TKu0SNy0cppQ/67/k8H3gq8PpxNTxreu7VO/CW\nDFiVSek13z3wC8cTy0SMJ86W4aGGeu7vo1QL/TUrbhbeVE/xkZT7D5+ndEJ6b2gbUNZhPLaJOCaV\nyvS2C1qYstx2/aWLB13RV9+PX9refsCXjcSs6blXl1HXUMq7tu3UQWOKTY0n1kzKdLPpNNFz3xN4\njNur5/43wH7AZkB9Y/CllKu9+xVJH2P5kOEcynqM8xtsf1LqLw383a+mMI/172LWJPfaOPNKD9H8\nOPPbap/fN55IAzXlYXKGh2agiWmI51N2QGqlWqjtzwKflfT3tr/SRgwT5mLKtFhTdmY60XaTSfUE\n4FTaL8/9W0lPtr3C3HpJT2L5bl1jMWuGZSZZbzyxqvXSZLuTMjzU+jRElQ0ydqDUGmqlWmgtlhdQ\nrqruqyVv+4im42hDdfX4XkpJjt9QOh2bA8cC72yrDIOkh7Piz+M3DbW7E3AS8CmWL55aQPn+vMT2\n2LaCTHIfgRbHE1udblaLo/VpiJqAaqFVHJ8A1qOUYPgUpYb3z20f0GQcbZH0EcrQx5ttL63ObQAc\nCdxp++CG49mNMky2CeWqbgvgknGOdQ+I4ZGUzs/jqlMXAR+r3fQdT7tJ7jM3xXjiVbb3bTiOVqeb\n1eKYlGmIW1A2UPmuym5Qc3sJpsEYLrC9Q+3fBwNftf3cJuNoi6TLgW361z6oFJm71A2XPq46HjsD\n37X9REl/Dexj+8Am45hOb17+KF9z1oy5T5i2xxOB9qeb1bQ+DVG1aqHAVpSZK58AGqsWWukVCvuD\nSpXOmymzvO4vPGhRm8v2i230JO+yfbOkOZLm2P5BNRVy0oz8TS/JfQZWNZ6osmdk22VdF7P80q9J\nrW1aXvN6qmqhALYvr8ZZm/ZNSRsB/07ZZ8A0XO+nZRdLerntz9VPStoXuLSFeG6trp5+DHxB0k1A\nY9OmZ2Dkb3wZlpmBCRxPnJThodY2La/FcJbtp/ZWK1dvxOc2vcy8L6YHAut4+A1FZj1Jm1I2J7mT\ncgPRwI7AusCetq9rOJ4HVbHMoVSE3BD4wjiX/a+OcawFSXKfgQkcT3wtKw4PXdXG8JAmYNNySR8E\nbgVeTrl59TrK9nbvbKj999p+R/X5/aJm+6pI2pkyY0jARba/11IcWwI32P5jdbwu8AjbV7cRz1TG\nUUIlyX0GJP3K9jYzfWwMcUzUdLNJmIaolquF1nteTazIjeFIWkSpvrisOl4b+JntHVuIZW3g0R6w\ngbukXW2fOsr2MuY+M5MynvjvlOGhLQcMDx1JqVvdpMMbbm+QN7hUBr0voavhaqExkdaqDxe6bCqz\ndtNBVGsfPgysTamP9QRKVcg9q7hGmtghPfcZmZTxxEkbHqrabnUa4hQ1RBqrFippMeWPV8CbWbEE\nAbY/POjrYrwkfYcyp3xhdbwH8EY3uOdy1e45lJlbP3BD24Om5z4DVfJ+at944qktjCdO1HSzNqch\nagKqhVaOYfnOOvXPo12vocySOao6vpYys6tpd9m+ta+czFj/VpPcV4Pt7wPfbzGESRke6mlzGuLp\nwA3AxsCHaueXAhcM/IoxsP2vwzxPDWxcEsvZ/jWwUzUdUk0vaqu5RNI/AHOqm7wHU3ZmGpsMy8xC\nkzI8VItn4qYhTqrcbG2WpA0p94R6pTp+BBzR9PTUakrmYdRu+AP/avsPY2szyX32mqDpZq1NQ9Rk\nVQudVpP3AQIkfQX4JfDZ6tTLgD93S5umVPejbPvOaZ+8pm0luceaansa4mySnnuzJJ1n+wnTnWsg\njidRiuvNq07dCLxqnCU65ozrheN+5Q22j7G9l+0X2T5GUtPTMWeLJjYuieXulNTblBpJT2d5/Z8m\nHQe8xfZmtjcD3lqdG5sk9xiFfxxwbr+mg5glmti4JJZ7DXCUpKslXQ18HHh1C3HcYfsHvQPbPwR+\nP84GMywTq602DfEZwE9qD60P3GP7Oa0E1iJNwMYlsbJqkR+2b1cLu2VVdanWBk6k3CN6MXA7cHIV\n18hndiW5x2rTBG1aPik0ARuXxKpJ+o3tRzfc5k9W8bBtD9x4Z43aTHKPGB1NyMYlMTVJ19revOE2\nNWjh4VjbTHKP1TXbpiE2QdJLKBsvtLZxSaxaSz33K4EvAsfavryJNrNCNVab7SyxX9kkbFxyvyfp\nQqbueDyi4XCg7LXwEkophGWUCq4n2x7bTdX03CNGaBI2Lon77gdNyfY1TcXST9KzgC8AG1BuqL7b\n9lWjbic994jROh/YCGht45IYPnlLOsP2X4w7nmqh3/OAVwDbAB+lJPi/BP4H2HbUbSa5R4zWI4BL\nJbW2cUnMyDoNtXM58FNK+eEf186fJGnkM2UgwzIRIyXpmYPOZyrkZBp3OQhJB9n+uKQNmy5WlhWq\nESNUJfGrgQdUn58NZKbM/df+AE0ndkhyjxipauOSLwOfrE5tCnytvYhiGp2t9ZPkHjFarweeTlla\nTjWnuamNS6KPpA9Mc27cuzLtIOn2AR9LJd0+zoaT3CNG60/1aZDVxiW5sdWeXQac27X3ie1fjrn9\nC21vMOBj/XEv8stsmYjR+pGkdwDrStqFsnHJN1qO6X5H0msp3/vHSKoX5Vof+Fk7UTUrs2UiRigb\nl0yGanu9hzCgqJ3tWxqM4x223zvE80a+t26Se8QISTrY9kenOxfNqTbr2Nr2cZI2BtYfx4rQNTGO\nKZkZc48YrWxcMkEkHQ68HTi0OrU28Pn2IprSyGftZMw9YgRqG5dsKWlh7aH1gZvbiSqAPSlFu84F\nsH29pEkseDfyIZQk94jROB24AdgY+FDt/FJg5LvsxNCW2bYkA0h6UNsBTSE994hJVBWqugYYexGq\nmJGTJX0S2KhaYLY/MIk3uEe+t25uqEaMQDYumVzVlNT67KXvtBBD43vrJrlHRIxZG3vrJrlHROfU\nrqTEildUrVxJtbG3bpJ7RMSYtbG3bm6oRkRnSTre9sumO9eAxvfWTXKPiC7bvn5QFXJ7cgtx7Ak8\npsm9dbNCNSI6R9Kh1bh7veTuUuBG4OsthNTbW7cxGXOPiM6S9D7bh07/zLHH8UNgB8rOXI3srZvk\nHhGdNdXm032bVDcRR+N76ya5R0RnSarX0l8HeApwju2x3chcRSxbUKpTflfSesBc20vH1V5uqEZE\nZ9nerX4saXPgg03HUZU+OBB4KLAVZW/dTwDPHlebuaEaEfcni4HHtdBu43vrpuceEZ0l6WMsX6E6\nh1L+9/wWQvmT7WWSenGNfW/dJPeI6LKLgbmURHobcKLtNvZQbXxv3dxQjYjOqXrG76WU+P0NpabM\n5sCxwDtt39VwPI3vrZvkHhGdI+kjlF2w3tybkSJpA+BI4E7bBzccT+N76ya5R0TnSLoc2MZ9CU7S\nXOBS21s3HM9KG2BL+oXtJ46rzYy5R0QXuT+xVyfv6W2514Q299ZNco+ILrpY0sttf65+UtK+wKUN\nxtHa3roZlomIzpG0KfBV4E7gHMpsmR2BdYE9bV/XYniNSHKPiM6StDOl7K+Ai2x/r+H2W9tbN8k9\nIqKDUn4gIqKDktwjIjooyT0iooOS3CMiOijJPSKig/4/Vj2O/ID1iEEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253caf4c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "predictors = [x for x in train.columns if x not in [target]+IDcol]\n",
    "alg3 = DecisionTreeRegressor(max_depth=15, min_samples_leaf=100)\n",
    "modelfit(alg3, train, test, predictors, target, IDcol, 'alg3.csv')\n",
    "coef3 = pd.Series(alg3.feature_importances_, predictors).sort_values(ascending=False)\n",
    "coef3.plot(kind='bar', title='Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1070.13022666\n",
      "CV_SCORE : mean - 1097 | std - 43.03 | max - 1174 | min - 1027\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\naveen chauhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253e408630>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253e452e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = ['Item_MRP','Outlet_Type','Outlet','Outlet_Years']\n",
    "alg4 = DecisionTreeRegressor(max_depth=8, min_samples_leaf=150)\n",
    "modelfit(alg4, train, test, predictors, target, IDcol, 'alg4.csv')\n",
    "coef4 = pd.Series(alg4.feature_importances_, predictors).sort_values(ascending=False)\n",
    "coef4.plot(kind='bar', title='Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1070.13022666\n",
      "CV_SCORE : mean - 1097 | std - 43.03 | max - 1174 | min - 1027\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\naveen chauhan\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2253e46cf28>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2253e408b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = ['Item_MRP','Outlet_Type','Outlet','Outlet_Years']\n",
    "alg4 = DecisionTreeRegressor(max_depth=8, min_samples_leaf=150)\n",
    "modelfit(alg4, train, test, predictors, target, IDcol, 'alg4.csv')\n",
    "coef4 = pd.Series(alg4.feature_importances_, predictors).sort_values(ascending=False)\n",
    "coef4.plot(kind='bar', title='Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}