Car Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Car Price Prediction (ML Regression)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In this notebook, I have made an attempt to develop a model to predict the car prices using ML Regression. The dataset for the project is taken from Kaggle. Here, I have implemented various ML regression models and finally selected Voting Regressor as it gave me best scores for efficient model deployment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Libraries and the Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(301, 9)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the dataset\n",
    "data=pd.read_csv('car data.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the dataset preview, Selling_Price is the dependent variable and the rest are independent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking if there are any missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking cardinality of independent categorical variables in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique elements in Seller_Type are ['Dealer' 'Individual']\n",
      "Unique elements in Fuel_Type are ['Petrol' 'Diesel' 'CNG']\n",
      "Unique elements in Transmission are ['Manual' 'Automatic']\n",
      "Unique elements in Owner are [0 1 3]\n",
      "Unique elements in Year are [2014 2013 2017 2011 2018 2015 2016 2009 2010 2012 2003 2008 2006 2005\n",
      " 2004 2007]\n"
     ]
    }
   ],
   "source": [
    "print('Unique elements in Seller_Type are',data['Seller_Type'].unique())\n",
    "print('Unique elements in Fuel_Type are',data['Fuel_Type'].unique())\n",
    "print('Unique elements in Transmission are',data['Transmission'].unique())\n",
    "print('Unique elements in Owner are',data['Owner'].unique())\n",
    "print('Unique elements in Year are',data['Year'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique elements in Car_Name are 98\n"
     ]
    }
   ],
   "source": [
    "print('Unique elements in Car_Name are',data['Car_Name'].nunique())\n",
    "#98 unique elements \n",
    "#so, rather than encoding it, we can just drop this columbn as it doesn' make sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the Car_Name Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=data[['Year','Selling_Price','Present_Price','Kms_Driven','Fuel_Type','Seller_Type','Transmission','Owner']]\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's make a feature variable 'Present_Year' which has all the element values as 2020. On subtracting 'Present_Year' and 'Year', we can make another feature variable as 'Number_of_Years_Old', which gives us idea about how old the car is.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Present_Year</th>\n",
       "      <th>Number_of_Years_Old</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Present_Year  Number_of_Years_Old  \n",
       "0       Manual      0          2020                    6  \n",
       "1       Manual      0          2020                    7  \n",
       "2       Manual      0          2020                    3  \n",
       "3       Manual      0          2020                    9  \n",
       "4       Manual      0          2020                    6  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Present_Year']=2020\n",
    "dataset['Number_of_Years_Old']=dataset['Present_Year']- dataset['Year']\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we can now safely drop 'Year' and 'Present_Year' columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Number_of_Years_Old</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Number_of_Years_Old  \n",
       "0       Manual      0                    6  \n",
       "1       Manual      0                    7  \n",
       "2       Manual      0                    3  \n",
       "3       Manual      0                    9  \n",
       "4       Manual      0                    6  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop(labels=['Year', 'Present_Year'],axis=1,inplace=True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding the Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Number_of_Years_Old</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  Number_of_Years_Old  \\\n",
       "0           3.35           5.59       27000      0                    6   \n",
       "1           4.75           9.54       43000      0                    7   \n",
       "2           7.25           9.85        6900      0                    3   \n",
       "3           2.85           4.15        5200      0                    9   \n",
       "4           4.60           6.87       42450      0                    6   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select categorical variables from then dataset, and then implement categorical encoding for nominal variables\n",
    "Fuel_Type=dataset[['Fuel_Type']]\n",
    "Fuel_Type=pd.get_dummies(Fuel_Type, drop_first=True)\n",
    "\n",
    "Seller_Type=dataset[['Seller_Type']]\n",
    "Seller_Type=pd.get_dummies(Seller_Type, drop_first=True)\n",
    "\n",
    "Transmission=dataset[['Transmission']]\n",
    "Transmission=pd.get_dummies(Transmission, drop_first=True)\n",
    "\n",
    "dataset=pd.concat([dataset,Fuel_Type, Seller_Type, Transmission], axis=1)\n",
    "\n",
    "dataset.drop(labels=['Fuel_Type', 'Seller_Type', 'Transmission'], axis=1, inplace=True)\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Selling_Price', 'Present_Price', 'Kms_Driven', 'Owner',\n",
       "       'Number_of_Years_Old', 'Fuel_Type_Diesel', 'Fuel_Type_Petrol',\n",
       "       'Seller_Type_Individual', 'Transmission_Manual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the encoding has been done "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Number_of_Years_Old</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Selling_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878983</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>-0.088344</td>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.552339</td>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.367128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Price</th>\n",
       "      <td>0.878983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.348715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kms_Driven</th>\n",
       "      <td>0.029187</td>\n",
       "      <td>0.203647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>-0.162510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Owner</th>\n",
       "      <td>-0.088344</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.089216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number_of_Years_Old</th>\n",
       "      <td>-0.236141</td>\n",
       "      <td>0.047584</td>\n",
       "      <td>0.524342</td>\n",
       "      <td>0.182104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <td>0.552339</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>-0.098643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "Number_of_Years_Old         -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                        Number_of_Years_Old  Fuel_Type_Diesel  \\\n",
       "Selling_Price                     -0.236141          0.552339   \n",
       "Present_Price                      0.047584          0.473306   \n",
       "Kms_Driven                         0.524342          0.172515   \n",
       "Owner                              0.182104         -0.053469   \n",
       "Number_of_Years_Old                1.000000         -0.064315   \n",
       "Fuel_Type_Diesel                  -0.064315          1.000000   \n",
       "Fuel_Type_Petrol                   0.059959         -0.979648   \n",
       "Seller_Type_Individual             0.039896         -0.350467   \n",
       "Transmission_Manual               -0.000394         -0.098643   \n",
       "\n",
       "                        Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "Selling_Price                  -0.540571               -0.550724   \n",
       "Present_Price                  -0.465244               -0.512030   \n",
       "Kms_Driven                     -0.172874               -0.101419   \n",
       "Owner                           0.055687                0.124269   \n",
       "Number_of_Years_Old             0.059959                0.039896   \n",
       "Fuel_Type_Diesel               -0.979648               -0.350467   \n",
       "Fuel_Type_Petrol                1.000000                0.358321   \n",
       "Seller_Type_Individual          0.358321                1.000000   \n",
       "Transmission_Manual             0.091013                0.063240   \n",
       "\n",
       "                        Transmission_Manual  \n",
       "Selling_Price                     -0.367128  \n",
       "Present_Price                     -0.348715  \n",
       "Kms_Driven                        -0.162510  \n",
       "Owner                             -0.050316  \n",
       "Number_of_Years_Old               -0.000394  \n",
       "Fuel_Type_Diesel                  -0.098643  \n",
       "Fuel_Type_Petrol                   0.091013  \n",
       "Seller_Type_Individual             0.063240  \n",
       "Transmission_Manual                1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset Correlation\n",
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization and Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlations of features in dataset\n",
    "corrmat = data.corr()\n",
    "top_corr_features = corrmat.index\n",
    "plt.figure(figsize=(10,10))\n",
    "#Plot heat map\n",
    "sns.heatmap(data[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Number_of_Years_Old</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <th>Selling_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  Number_of_Years_Old  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0                    6                 0   \n",
       "1           9.54       43000      0                    7                 1   \n",
       "2           9.85        6900      0                    3                 0   \n",
       "3           4.15        5200      0                    9                 0   \n",
       "4           6.87       42450      0                    6                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \\\n",
       "0                 1                       0                    1   \n",
       "1                 0                       0                    1   \n",
       "2                 1                       0                    1   \n",
       "3                 1                       0                    1   \n",
       "4                 0                       0                    1   \n",
       "\n",
       "   Selling_Price  \n",
       "0           3.35  \n",
       "1           4.75  \n",
       "2           7.25  \n",
       "3           2.85  \n",
       "4           4.60  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sell=dataset['Selling_Price']\n",
    "dataset.drop(['Selling_Price'], axis=1, inplace=True)\n",
    "dataset=dataset.join(sell)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset.iloc[:,:-1]\n",
    "y=dataset.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.38070792 0.0388182  0.00141403 0.07783784 0.21634906 0.01264464\n",
      " 0.13435614 0.13787217]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### To determine important features, make use of ExtraTreesRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)\n",
    "\n",
    "print(model.feature_importances_)\n",
    "\n",
    "#plot graph of feature importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Owner' has zero feature importance i.e. nil on the dependent variable, 'Selling_Price'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset.iloc[:,:-1].values\n",
    "y=dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "#from sklearn.model_selection import RandomizedSearchCV\n",
    "#from sklearn.model_selection import GridSearchCV\n",
    "#from sklearn.model_selection import StratifiedKFold\n",
    "#kfold = StratifiedKFold(n_splits=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Score on Training set is 1.0\n",
      "Decision Tree Score on Test Set is 0.916727846049827\n",
      "[0.91735978 0.84528972 0.69863646 0.97123705 0.91636849]\n",
      "Accuracy: 86.98 %\n",
      "Standard Deviation: 9.45 %\n",
      "Mean Absolute Error: 0.8543956043956044\n",
      "Mean Squared Error: 2.490341758241758\n",
      "RMSE: 1.578081670333243\n",
      "The r2_score is 0.916727846049827\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/allyson/anaconda3/envs/carpred/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Decision Tree Regressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt_reg = DecisionTreeRegressor(random_state = 0)\n",
    "dt_reg.fit(X_train, y_train)\n",
    "y_pred=dt_reg.predict(X_test)\n",
    "\n",
    "print(\"Decision Tree Score on Training set is\",dt_reg.score(X_train, y_train))#Training Accuracy\n",
    "print(\"Decision Tree Score on Test Set is\",dt_reg.score(X_test, y_test))#Testing Accuracy\n",
    "\n",
    "accuracies = cross_val_score(dt_reg, X_train, y_train, cv = 5)\n",
    "print(accuracies)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))\n",
    "\n",
    "mae=mean_absolute_error(y_pred, y_test)\n",
    "print(\"Mean Absolute Error:\" , mae)\n",
    "\n",
    "mse=mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\" , mse)\n",
    "\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "print('The r2_score is', metrics.r2_score(y_test, y_pred))\n",
    "\n",
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Score on Training set is 0.9104320740012177\n",
      "Random Forest Score on Test Set is 0.8678402516106084\n",
      "[0.94990549 0.81561043 0.62520279 0.8216952  0.92504121]\n",
      "Accuracy: 82.75 %\n",
      "Standard Deviation: 11.45 %\n",
      "Mean Absolute Error: 0.8814436121117477\n",
      "Mean Squared Error: 3.952376929864946\n",
      "RMSE: 1.9880585831068827\n",
      "The r2_score is 0.8678402516106084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/allyson/anaconda3/envs/carpred/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random Forest Regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_reg = RandomForestRegressor(n_estimators=400,min_samples_split=15,min_samples_leaf=2,\n",
    "max_features='auto', max_depth=30)\n",
    "rf_reg.fit(X_train, y_train)\n",
    "y_pred=rf_reg.predict(X_test)\n",
    "\n",
    "print(\"Random Forest Score on Training set is\",rf_reg.score(X_train, y_train))#Training Accuracy\n",
    "print(\"Random Forest Score on Test Set is\",rf_reg.score(X_test, y_test))#Testing Accuracy\n",
    "\n",
    "accuracies = cross_val_score(rf_reg, X_train, y_train, cv = 5)\n",
    "print(accuracies)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))\n",
    "\n",
    "mae=mean_absolute_error(y_pred, y_test)\n",
    "print(\"Mean Absolute Error:\" , mae)\n",
    "\n",
    "mse=mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\" , mse)\n",
    "\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "print('The r2_score is', metrics.r2_score(y_test, y_pred))\n",
    "\n",
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Voting Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Voting Regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset to average the individual predictions to form a final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting Regresssor Score on Training set is 0.9775533206532837\n",
      "Voting Regresssor Score on Test Set is 0.9002781581447105\n",
      "[0.959475   0.83804688 0.69366135 0.93356877 0.94260353]\n",
      "Accuracy: 87.35 %\n",
      "Standard Deviation: 9.94 %\n",
      "Mean Absolute Error: 0.850636044089248\n",
      "Mean Squared Error: 2.9822870575631644\n",
      "RMSE: 1.7269299515507757\n",
      "The r2_score is 0.9002781581447105\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/allyson/anaconda3/envs/carpred/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor\n",
    "vot_reg = VotingRegressor([('DecisionTree', dt_reg), ('RandomForestRegressor', rf_reg)])\n",
    "vot_reg.fit(X_train, y_train)\n",
    "y_pred=vot_reg.predict(X_test)\n",
    "\n",
    "print(\"Voting Regresssor Score on Training set is\",vot_reg.score(X_train, y_train))#Training Accuracy\n",
    "print(\"Voting Regresssor Score on Test Set is\",vot_reg.score(X_test, y_test))#Testing Accuracy\n",
    "\n",
    "accuracies = cross_val_score(vot_reg, X_train, y_train, cv = 5)\n",
    "print(accuracies)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))\n",
    "\n",
    "mae=mean_absolute_error(y_pred, y_test)\n",
    "print(\"Mean Absolute Error:\" , mae)\n",
    "\n",
    "mse=mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\" , mse)\n",
    "\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "print('The r2_score is', metrics.r2_score(y_test, y_pred))\n",
    "\n",
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the XGBoost Regressor gave us best scores on the Training Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dump the model selected as a Pickle File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.35336698])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "pickle.dump(vot_reg, open(\"vot_reg.pkl\", \"wb\"))\n",
    "\n",
    "# load model from file\n",
    "model = pickle.load(open(\"vot_reg.pkl\", \"rb\"))\n",
    "\n",
    "model.predict([[9.85, 6900, 0, 3, 0, 1, 0, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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