2. Trying Different Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Everyone, The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. So now your aim is to build a predictive model and predict the sales of each product at a particular outlet.\n",
    "\n",
    "Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales.\n",
    "\n",
    "Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly. \n",
    "\n",
    "Although we are attaching the required documents in the attached zip folder but You can also use the below mentioned link and register here to access the problem statement and dataset: \n",
    "\n",
    "* https://datahack.analyticsvidhya.com/contest/practice-problem-big-mart-sales-iii/\n",
    "\n",
    "The submission file generated by this code is attached in the same folder, Please use the solution checker tab to upload the submission file, the public score would be generated immediately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Step 1: Importing the Relevant Libraries__\n",
    "    \n",
    "* __Step 2: Data Inspection__\n",
    "    \n",
    "* __Step 3: Data Cleaning__\n",
    "    \n",
    "* __Step 4: Exploratory Data Analysis__\n",
    "    \n",
    "* __Step 5: Building Model__\n",
    "    \n",
    "* __How to Make a Submission?__\n",
    "* __Guidelines for Final Submission__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Importing the Relevant Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('always')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Data Inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train_v9rqX0R.csv\")\n",
    "test = pd.read_csv(\"test_AbJTz2l.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8523, 12), (5681, 11))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __We have 8523 rows and 12 columns in Train set whereas Test set has 5681 rows and 11 columns.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               0.000000\n",
       "Item_Weight                  17.165317\n",
       "Item_Fat_Content              0.000000\n",
       "Item_Visibility               0.000000\n",
       "Item_Type                     0.000000\n",
       "Item_MRP                      0.000000\n",
       "Outlet_Identifier             0.000000\n",
       "Outlet_Establishment_Year     0.000000\n",
       "Outlet_Size                  28.276428\n",
       "Outlet_Location_Type          0.000000\n",
       "Outlet_Type                   0.000000\n",
       "Item_Outlet_Sales             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ratio of null values\n",
    "train.isnull().sum()/train.shape[0] *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier               0.000000\n",
       "Item_Weight                  17.180074\n",
       "Item_Fat_Content              0.000000\n",
       "Item_Visibility               0.000000\n",
       "Item_Type                     0.000000\n",
       "Item_MRP                      0.000000\n",
       "Outlet_Identifier             0.000000\n",
       "Outlet_Establishment_Year     0.000000\n",
       "Outlet_Size                  28.269671\n",
       "Outlet_Location_Type          0.000000\n",
       "Outlet_Type                   0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ratio of null values\n",
    "test.isnull().sum()/test.shape[0] *100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __We have 17% and 28% of missing values in Item weight and Outlet_Size columns respectively.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Features in Train Set: 7\n",
      "Numerical Features in Train Set: 5\n"
     ]
    }
   ],
   "source": [
    "#categorical features\n",
    "categorical = train.select_dtypes(include =[np.object])\n",
    "print(\"Categorical Features in Train Set:\",categorical.shape[1])\n",
    "\n",
    "#numerical features\n",
    "numerical= train.select_dtypes(include =[np.float64,np.int64])\n",
    "print(\"Numerical Features in Train Set:\",numerical.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Features in Test Set: 7\n",
      "Numerical Features in Test Set: 4\n"
     ]
    }
   ],
   "source": [
    "#categorical features\n",
    "categorical = test.select_dtypes(include =[np.object])\n",
    "print(\"Categorical Features in Test Set:\",categorical.shape[1])\n",
    "\n",
    "#numerical features\n",
    "numerical= test.select_dtypes(include =[np.float64,np.int64])\n",
    "print(\"Numerical Features in Test Set:\",numerical.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why missing values treatment is required?\n",
    "Missing data in the training data set can reduce the power / fit of a model or can lead to a biased model because we have not analysed the behavior and relationship with other variables correctly. It can lead to wrong prediction."
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "* Item_Weight and Outlet_Size have some missing values in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__3.1 Item Weight__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc15b643fd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0Xb40s/dDL1m0zoVJXlRVRw6P9cxlvs+LklxUVZdm9or0ljXc90VJrvIhIliZw5nBFldVp44xjvwf1gOZHZtz/5yHBZue9ztg6/u5qvq9zLb3zyZ5/nyHA1uDV6AwR1X1yiTPWrL4nWOMP5jHeIC1E1AAaPAhIgBoEFAAaBBQAGgQUABoEFAAaPg/KzIGMcpB72gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.boxplot('Item_Weight',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc157395890>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.boxplot('Item_Weight',data=test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__The Box Plots above clearly show no \"Outliers\" and hence we can impute the missing values with \"Mean\"__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputing with Mean\n",
    "train['Item_Weight']= train['Item_Weight'].fillna(train['Item_Weight'].mean())\n",
    "test['Item_Weight']= test['Item_Weight'].fillna(test['Item_Weight'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Item_Weight'].isnull().sum(),test['Item_Weight'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have succesfully imputed the missing values from the column Item_Weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__3.2 : Outlet Size__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2410, 1606)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Outlet_Size'].isnull().sum(),test['Outlet_Size'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medium    2793\n",
      "Small     2388\n",
      "High       932\n",
      "Name: Outlet_Size, dtype: int64\n",
      "******************************************\n",
      "Medium    1862\n",
      "Small     1592\n",
      "High       621\n",
      "Name: Outlet_Size, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train['Outlet_Size'].value_counts())\n",
    "print('******************************************')\n",
    "print(test['Outlet_Size'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Since the outlet_size is a categorical column, we can impute the missing values by \"Mode\"(Most Repeated Value) from the column__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputing with Mode\n",
    "train['Outlet_Size']= train['Outlet_Size'].fillna(train['Outlet_Size'].mode()[0])\n",
    "test['Outlet_Size']= test['Outlet_Size'].fillna(test['Outlet_Size'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Outlet_Size'].isnull().sum(),test['Outlet_Size'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__We have succesfully imputed the missing values from the column Outlet_Size.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
       "       'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
       "       'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
       "       'Outlet_Type', 'Item_Outlet_Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Medium</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      Medium               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": [
       "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Item_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__We see there are some irregularities in the column and it is needed to fix them.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Item_Fat_Content'].replace(['low fat','LF','reg'],['Low Fat','Low Fat','Regular'],inplace = True)\n",
    "test['Item_Fat_Content'].replace(['low fat','LF','reg'],['Low Fat','Low Fat','Regular'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Item_Fat_Content']= train['Item_Fat_Content'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc15b643790>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot('Item_Fat_Content',data=train,palette='ocean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__The Items bought are more of Low Fat.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc15b9d5f50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,7))\n",
    "sns.countplot('Item_Type',data=train,palette='spring')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Fruits and Vegetables are largely sold as people tend to use them on daily purpose.__\n",
    "* __Snack Foods too have good sales.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc15746f290>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot('Outlet_Size',data=train,palette='summer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __The Outlets are more of Medium Size__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc15b9c23d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot('Outlet_Location_Type',data=train,palette='autumn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __The Outlets are maximum in number in Tier 3 Cities__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc15c1abc10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot('Outlet_Type',data=train,palette='twilight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __The Outlets are more of Supermarket Type1__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc15c1b3250>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(y='Item_Type',x='Item_Outlet_Sales',data=train,palette='flag')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __The products available were Fruits-Veggies and Snack Foods but the sales of Seafood and Starchy Foods seems higher and hence the sales can be improved with having stock of products that are most bought by customers.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>Medium</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      Medium               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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Labelencoding\n",
    "le = LabelEncoder()\n",
    "var_mod = train.select_dtypes(include='object').columns\n",
    "for i in var_mod:\n",
    "    train[i] = le.fit_transform(train[i])\n",
    "    \n",
    "for i in var_mod:\n",
    "    test[i] = le.fit_transform(test[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Encoding the required columns from training and test dataset__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
       "       'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
       "       'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
       "       'Outlet_Type', 'Item_Outlet_Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate Features and Target\n",
    "X= train.drop(columns = ['Item_Outlet_Sales'], axis=1)\n",
    "y= train['Item_Outlet_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 20% data as validation set\n",
    "X_train,X_valid,y_train,y_valid = train_test_split(X,y,test_size=0.2,random_state=22)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ML models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso, LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "algos = [LinearRegression(),  Ridge(), Lasso(),\n",
    "          KNeighborsRegressor(), DecisionTreeRegressor()]\n",
    "\n",
    "names = ['Linear Regression', 'Ridge Regression', 'Lasso Regression',\n",
    "         'K Neighbors Regressor', 'Decision Tree Regressor']\n",
    "\n",
    "rmse_list = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in algos:\n",
    "    model = name\n",
    "    model.fit(X_train,y_train)\n",
    "    y_pred = model.predict(X_valid)\n",
    "    MSE= metrics.mean_squared_error(y_valid,y_pred)\n",
    "    rmse = np.sqrt(MSE)\n",
    "    rmse_list.append(rmse)\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation = pd.DataFrame({'Model': names,\n",
    "                           'RMSE': rmse_list})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>1246.171773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>1246.061549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>1245.732959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K Neighbors Regressor</td>\n",
       "      <td>1569.728502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree Regressor</td>\n",
       "      <td>1581.764719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model         RMSE\n",
       "0        Linear Regression  1246.171773\n",
       "1         Ridge Regression  1246.061549\n",
       "2         Lasso Regression  1245.732959\n",
       "3    K Neighbors Regressor  1569.728502\n",
       "4  Decision Tree Regressor  1581.764719"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As we can clearly see Lasso performs slighlty better than Ridge and Linear regression. KNN Regressor and Decision Tree Regressor do not improve the score so we can select Lasso for making our final predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('sample_submission_8RXa3c6.csv')\n",
    "model = Lasso()\n",
    "model.fit(X, y)\n",
    "final_predictions = model.predict(test)\n",
    "submission['Item_Outlet_Sales'] = final_predictions\n",
    "#only positive predictions for the target variable\n",
    "submission['Item_Outlet_Sales'] = submission['Item_Outlet_Sales'].apply(lambda x: 0 if x<0 else x)\n",
    "submission.to_csv('my_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Make a Submission?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"700\" height=\"380\" src=\"https://www.youtube.com/embed/zevnI9TgTtA\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('<iframe width=\"700\" height=\"380\" src=\"https://www.youtube.com/embed/zevnI9TgTtA\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')    \n"
   ]
  }
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