Used Car Price Prediction 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Price of Used Cars\n",
    "*On Car Dekho Dataset*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this Project, we are going to predict the Price of Used Cars using various features like Present_Price, Selling_Price, Kms_Driven, Fuel_Type, Year etc. The data used in this project was downloaded from Kaggle.\n",
    "\n",
    "**To be able to predict used cars market value can help both buyers and sellers.**\n",
    "\n",
    "There are lots of individuals who are interested in the used car market at some points in their life because they wanted to sell their car or buy a used car. In this process, it’s a big corner to pay too much or sell less then it’s market value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_palette(\"GnBu_d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Datasets/car_price_data2.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exploring the descriptive statistics of the variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>count</th>\n",
       "      <td>301</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>city</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239</td>\n",
       "      <td>195</td>\n",
       "      <td>261</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Car_Name         Year  Selling_Price  Present_Price     Kms_Driven  \\\n",
       "count       301   301.000000     301.000000     301.000000     301.000000   \n",
       "unique       98          NaN            NaN            NaN            NaN   \n",
       "top        city          NaN            NaN            NaN            NaN   \n",
       "freq         26          NaN            NaN            NaN            NaN   \n",
       "mean        NaN  2013.627907       4.661296       7.628472   36947.205980   \n",
       "std         NaN     2.891554       5.082812       8.644115   38886.883882   \n",
       "min         NaN  2003.000000       0.100000       0.320000     500.000000   \n",
       "25%         NaN  2012.000000       0.900000       1.200000   15000.000000   \n",
       "50%         NaN  2014.000000       3.600000       6.400000   32000.000000   \n",
       "75%         NaN  2016.000000       6.000000       9.900000   48767.000000   \n",
       "max         NaN  2018.000000      35.000000      92.600000  500000.000000   \n",
       "\n",
       "       Fuel_Type Seller_Type Transmission       Owner  \n",
       "count        301         301          301  301.000000  \n",
       "unique         3           2            2         NaN  \n",
       "top       Petrol      Dealer       Manual         NaN  \n",
       "freq         239         195          261         NaN  \n",
       "mean         NaN         NaN          NaN    0.043189  \n",
       "std          NaN         NaN          NaN    0.247915  \n",
       "min          NaN         NaN          NaN    0.000000  \n",
       "25%          NaN         NaN          NaN    0.000000  \n",
       "50%          NaN         NaN          NaN    0.000000  \n",
       "75%          NaN         NaN          NaN    0.000000  \n",
       "max          NaN         NaN          NaN    3.000000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>90%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>14.790000</td>\n",
       "      <td>65000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>22.780000</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>35.960000</td>\n",
       "      <td>142000.000000</td>\n",
       "      <td>1.000000</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",
       "90%    2017.000000       9.500000      14.790000   65000.000000    0.000000\n",
       "95%    2017.000000      14.500000      22.780000   80000.000000    0.000000\n",
       "99%    2017.000000      23.000000      35.960000  142000.000000    1.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check if there are any outliers\n",
    "# Here we conclude that we don't have any outliers as the values are gradually increasing!\n",
    "df.describe(percentiles=[0.25,0.5,0.75,0.9,0.95,0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Kms_Driven     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Seller_Type    301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information of the data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check for any missing value in data set and treat it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking missing value\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new feature called total no. of years old my car,bcz It's important to know how many years old the car is.\n",
    "df['Car_age'] = 2020-df['Year']\n",
    "\n",
    "#It's time to drop the Year column after the needed info is derived.\n",
    "df.drop(labels='Year',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Car_Name</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>Car_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</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>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</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>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</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>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</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>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0     ritz           3.35           5.59       27000    Petrol      Dealer   \n",
       "1      sx4           4.75           9.54       43000    Diesel      Dealer   \n",
       "2     ciaz           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  wagon r           2.85           4.15        5200    Petrol      Dealer   \n",
       "4    swift           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Car_age  \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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration\n",
    "\n",
    "**Data Exploration & Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Seller Type, Transmission nd Fuel Type Visualization with target variable\n",
    "plt.figure(figsize=[17,5])\n",
    "plt.subplot(1,3,1)\n",
    "sns.barplot(df['Seller_Type'], df['Selling_Price'])\n",
    "plt.title('Selling Price Vs Seller Type')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "sns.barplot(df['Transmission'],df['Selling_Price'])\n",
    "plt.title('Selling Price Vs Transmission')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "sns.barplot(df['Fuel_Type'],df['Selling_Price'])\n",
    "plt.title('Selling Price Vs Fuel Type')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Selling Price of cars seems to have higher prices when sold by Dealers when compared to Individuals *\n",
    "* It can be observed that Selling Price would be higher for cars that are Automatic. *\n",
    "* Selling Price of cars with Fuel Type of Diesel is higher than Petrol and CNG *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[17,5])\n",
    "plt.subplot(1,3,1)\n",
    "sns.barplot(df['Owner'],df['Selling_Price'])\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "sns.barplot(df['Car_age'],df['Selling_Price'])\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "sns.regplot(df['Selling_Price'],df['Kms_Driven'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Selling Price is high with less Owners used Cars *\n",
    "* Selling Price of cars 2 years old would be high and gradually decreases with car of 17 years old *\n",
    "* Lesser the Kms driven higher the Selling Price * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[17,5])\n",
    "plt.subplot(1,3,1)\n",
    "sns.regplot(df['Selling_Price'],df['Present_Price'])\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "sns.distplot(np.log(df['Selling_Price']))\n",
    "plt.title('Distribution of Selling Price')\n",
    "\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "sns.distplot(np.log(df['Kms_Driven']))\n",
    "plt.title('Distribution of KMS Drived')\n",
    "\n",
    "\n",
    "plt.title('Kilometers Drived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Selling Price tends to increase with increase in the Present Price of cars. *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dealing With Categorical Variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Petrol' 'Diesel' 'CNG']\n",
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n",
      "['ritz' 'sx4' 'ciaz' 'wagon r' 'swift' 'vitara brezza' 's cross'\n",
      " 'alto 800' 'ertiga' 'dzire' 'alto k10' 'ignis' '800' 'baleno' 'omni'\n",
      " 'fortuner' 'innova' 'corolla altis' 'etios cross' 'etios g' 'etios liva'\n",
      " 'corolla' 'etios gd' 'camry' 'land cruiser' 'Royal Enfield Thunder 500'\n",
      " 'UM Renegade Mojave' 'KTM RC200' 'Bajaj Dominar 400'\n",
      " 'Royal Enfield Classic 350' 'KTM RC390' 'Hyosung GT250R'\n",
      " 'Royal Enfield Thunder 350' 'KTM 390 Duke ' 'Mahindra Mojo XT300'\n",
      " 'Bajaj Pulsar RS200' 'Royal Enfield Bullet 350'\n",
      " 'Royal Enfield Classic 500' 'Bajaj Avenger 220' 'Bajaj Avenger 150'\n",
      " 'Honda CB Hornet 160R' 'Yamaha FZ S V 2.0' 'Yamaha FZ 16'\n",
      " 'TVS Apache RTR 160' 'Bajaj Pulsar 150' 'Honda CBR 150' 'Hero Extreme'\n",
      " 'Bajaj Avenger 220 dtsi' 'Bajaj Avenger 150 street' 'Yamaha FZ  v 2.0'\n",
      " 'Bajaj Pulsar  NS 200' 'Bajaj Pulsar 220 F' 'TVS Apache RTR 180'\n",
      " 'Hero Passion X pro' 'Bajaj Pulsar NS 200' 'Yamaha Fazer '\n",
      " 'Honda Activa 4G' 'TVS Sport ' 'Honda Dream Yuga '\n",
      " 'Bajaj Avenger Street 220' 'Hero Splender iSmart' 'Activa 3g'\n",
      " 'Hero Passion Pro' 'Honda CB Trigger' 'Yamaha FZ S '\n",
      " 'Bajaj Pulsar 135 LS' 'Activa 4g' 'Honda CB Unicorn'\n",
      " 'Hero Honda CBZ extreme' 'Honda Karizma' 'Honda Activa 125' 'TVS Jupyter'\n",
      " 'Hero Honda Passion Pro' 'Hero Splender Plus' 'Honda CB Shine'\n",
      " 'Bajaj Discover 100' 'Suzuki Access 125' 'TVS Wego' 'Honda CB twister'\n",
      " 'Hero Glamour' 'Hero Super Splendor' 'Bajaj Discover 125' 'Hero Hunk'\n",
      " 'Hero  Ignitor Disc' 'Hero  CBZ Xtreme' 'Bajaj  ct 100' 'i20' 'grand i10'\n",
      " 'i10' 'eon' 'xcent' 'elantra' 'creta' 'verna' 'city' 'brio' 'amaze'\n",
      " 'jazz']\n"
     ]
    }
   ],
   "source": [
    "# Here's our categorical column\n",
    "print(df['Fuel_Type'].unique())\n",
    "print(df['Seller_Type'].unique())\n",
    "print(df['Transmission'].unique())\n",
    "print(df['Car_Name'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In Car Name column There are three hundred and twelve unique name. \n",
    "# That's something really hard to implement and a regression that would mean more than 300 dummies, so we simply drop this column\n",
    "df = df.drop(labels='Car_Name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Car_age</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  Car_age  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59       27000      0        6                 0   \n",
       "1           4.75           9.54       43000      0        7                 1   \n",
       "2           7.25           9.85        6900      0        3                 0   \n",
       "3           2.85           4.15        5200      0        9                 0   \n",
       "4           4.60           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  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dealing With Categorical Variables, creagting dummie\n",
    "clean_data = pd.get_dummies(df,drop_first=True)\n",
    "clean_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking Multicollinearity Using VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.201728</td>\n",
       "      <td>Present_Price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.892026</td>\n",
       "      <td>Kms_Driven</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.087990</td>\n",
       "      <td>Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.540393</td>\n",
       "      <td>Car_age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.714762</td>\n",
       "      <td>Fuel_Type_Diesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.341429</td>\n",
       "      <td>Fuel_Type_Petrol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.230179</td>\n",
       "      <td>Seller_Type_Individual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.368965</td>\n",
       "      <td>Transmission_Manual</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF                Features\n",
       "0   3.201728           Present_Price\n",
       "1   2.892026              Kms_Driven\n",
       "2   1.087990                   Owner\n",
       "3   8.540393                 Car_age\n",
       "4   4.714762        Fuel_Type_Diesel\n",
       "5  13.341429        Fuel_Type_Petrol\n",
       "6   2.230179  Seller_Type_Individual\n",
       "7   8.368965     Transmission_Manual"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "variables = clean_data[['Present_Price','Kms_Driven','Owner','Car_age','Fuel_Type_Diesel',\n",
    "                        'Fuel_Type_Petrol','Seller_Type_Individual','Transmission_Manual']]\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif[\"Features\"] = variables.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Car_age nd fuel_type_petrol feature has high VIF\n",
    "data_no_multicolinearity = clean_data.drop(['Kms_Driven','Fuel_Type_Petrol'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.456996</td>\n",
       "      <td>Present_Price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.084141</td>\n",
       "      <td>Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.810633</td>\n",
       "      <td>Car_age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.658039</td>\n",
       "      <td>Fuel_Type_Diesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.864471</td>\n",
       "      <td>Seller_Type_Individual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.964982</td>\n",
       "      <td>Transmission_Manual</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF                Features\n",
       "0  2.456996           Present_Price\n",
       "1  1.084141                   Owner\n",
       "2  4.810633                 Car_age\n",
       "3  1.658039        Fuel_Type_Diesel\n",
       "4  1.864471  Seller_Type_Individual\n",
       "5  3.964982     Transmission_Manual"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cheking again after removing some correlated feature\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "variables = clean_data[['Present_Price','Owner','Car_age','Fuel_Type_Diesel',\n",
    "                        'Seller_Type_Individual','Transmission_Manual']]\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF\"] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif[\"Features\"] = variables.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Now its look good, We have VIF < 5 and hence there is no Multicollinearity occurrence in our model.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature selection simplified models, improves speed and prevent a series of unwanted issues arising from having many features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation heat map\n",
    "plt.figure(figsize=[15,7])\n",
    "sns.heatmap(data_no_multicolinearity.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***P-Value*** - P values are one of the best ways to determine if a variable is redundant(usefull or not) but they provide no information. \n",
    "\n",
    "If a variable has a p-values >0.05, we can disregard it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.   , 0.126, 0.   , 0.   , 0.   , 0.   ])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#F-regression create simple linear regression of each feature\n",
    "#This method would calculate the F statistic for each of those regressions and return the respective p values\n",
    "from sklearn.feature_selection import f_regression,SelectKBest\n",
    "\n",
    "X = data_no_multicolinearity.drop('Selling_Price',axis=1)\n",
    "y = data_no_multicolinearity['Selling_Price']\n",
    "\n",
    "f_regression(X,y)\n",
    "\n",
    "p_values = f_regression(X,y)[1]\n",
    "\n",
    "p_values.round(3) # This output in scincetific notation array lets convert them using 'round' method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Features</th>\n",
       "      <th>p-values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Present_Price</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Car_age</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fuel_Type_Diesel</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seller_Type_Individual</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Features  p-values\n",
       "0           Present_Price     0.000\n",
       "1                   Owner     0.126\n",
       "2                 Car_age     0.000\n",
       "3        Fuel_Type_Diesel     0.000\n",
       "4  Seller_Type_Individual     0.000"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating summary table\n",
    "model_summry = pd.DataFrame(data=['Present_Price', 'Owner', 'Car_age', 'Fuel_Type_Diesel',\n",
    "                              'Seller_Type_Individual', 'Transmission_Manual'], columns=['Features'])\n",
    "model_summry['p-values'] = p_values.round(3)\n",
    "model_summry.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * Dropping the \"Owner\" feature which has p-value>0.05 resulting in insigificant. It means that Owner variable is not contributing that much in target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Importance**\n",
    "\n",
    "*Feature importance gives you a score for each feature of your data, the higher the score more important or relevant is the feature towards our Target variable.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pcePGpe+Z+O6770pn7tSpk44ePVpmm6effloffPCBgoODNXDgwDKvHT58WDExMaXbXq5cWJYlSYqIiNCf//xn/eMf/1BYWJg8Hk+FcgGqm+joaF+PUGlcLle1Pr/KQm72kJt9ZGcPudnjb7ldqQhds1zs379f7733npYvX67g4GBFRkaqVq1auu2225SWlqYmTZpox44dys/Pv+z2kZGRl13P6bT3QVVNmzZVeHi4VqxYoaeffrp0+d69e3Xfffdp586duuuuuxQVFaVWrVopPT1dDodDa9asUZs2bSp0DIfDUW5Zw4YNdfDgQbVs2VJfffVV6aNNP+vbt69Gjhwpp9OpVatWlXmtZcuW2rVrl2JjY8u8Gd7j8aioqEhBQUE6cOCAJGn16tXq0KGDhg0bpu3bt2vr1q0VzgYAAADwpWuWi8cee0wHDx7U008/rdtuu02WZWnq1Klq1KiREhMT5fF45HA4NHfuXOXl5ZXbPjk5uULrXY/BgwcrJSVFCxYsKF2WnZ2tLVu2yOv16rXXXlOzZs3UtWtXDR06VCUlJYqJiVHDhg1tH3P27NmaOnWqwsLCFBoaWq5chIaGqm3btvJ4PAoLCyvzWlJSkhITE7Vq1SrVq1dPISEhkqQRI0ZoyJAhuuOOO9SkSRNJ0iOPPKKUlBR99NFHCg8PV0BAQLlPnAIAAACqIof18/M4fmTz5s3Kzc3VxIkTJf30w3vfvn0VGxvr48l8y+Vy6dOL3/p6DOCGvRTT3dcjVCp/u/VdVZCbPeRmH9nZQ272+FtuV5r3mncuKtvSpUuVk5NTbnlqaqqaNWtWbvmiRYuUk5OjFStWXPextmzZojVr1pRbPmLECD366KPXvT8AAAAA/8/n5WL8+PEaP358hddPSEgot2zevHkV2rZnz57q2bNnhY8FAAAAoOLsvasaAAAAAH6FcgEAAADACMoFAAAAACMoFwAAAACMoFwAAAAAMIJyAQAAAMAIn38ULczxWla1/+VjuDV4vJcU6Azw9RgAAOA6ceeiGilxu309gl9yuVy+HsFvVVZ2FAsAAPwT5QIAAACAEZQLAAAAAEZQLgAAAAAYQbkAAAAAYATlAgAAAIARlAsAAAAARlAuAAAAABhBuQAAAABgBOUCAAAAgBGUCwAAAABGUC4AAAAAGEG5AAAAAGAE5QIAAACAEZQLAAAAAEZQLgAAAAAYQbkAAAAAYATlAgAAAIARlAsAAAAARlAuAAAAABhBuQAAAABgBOUCAAAAgBGUCwAAAABGUC4AAAAAGEG5AAAAAGAE5QIAAACAEZSLaiQ4JMTXI/il6OhoX4/gt26V7DzeS74eAQAAvxDo6wFgjtPh0LI9n/l6DKDaeSmmu69HAADAL3DnAgAAAIARlAsAAAAARlAuAAAAABhBuQAAAABgBOUCAAAAgBGUCwAAAABGUC4AAAAAGEG5AAAAAGAE5QIAAACAEZQLAAAAAEZUuFzMmzdPcXFx6tOnj7p37664uDhNmDChMmcrJysrS1u2bLmubcaPH2/s+HFxcXr88cfLLPvHP/6hNm3a6Pjx48aOczndunWr1P0DAAAANyqwoismJSVJ+ukH/EOHDmnKlCmVNtSVDBw48Lq3Wbp0qfE5XC6XoqOjJUmbNm1S06ZNjR8DAAAA8DcVLheXk5SUpIKCAhUUFGj58uV6/fXXdfr0aeXl5alHjx6Kj49XUlKSgoODdeLECeXl5WnevHlq166dXnnlFR05ckTFxcUaMWKEfvvb36p///667777tH//fkVFRen222/Xl19+qeDgYK1cuVIrVqxQRESEevfurUmTJsmyLLndbs2aNUtRUVGaOHGiCgsLdeHCBcXHx+vBBx9Ut27dtG3bNu3bt09z5sxRQECAQkJCNGfOHHm9Xk2ePFmNGjXSsWPH9Jvf/EazZs266jn369dPGzduVHR0tH744Qe53W5FRERIkk6fPq3k5GS53W7l5+dr0qRJ6tWrl/r3769/+Zd/0f79++VwOPTmm29q3759WrdunRYvXixJpXPm5uZq3rx5unTpks6ePavk5GR16tTpRv4zATDA5XIZ21dxcbHR/d0qyM0ecrOP7OwhN3uqS243VC4kqUuXLho5cqSOHz+uDh06aNCgQXK73YqNjVV8fLwkqUmTJpo9e7bWr1+v9957T1OnTtUXX3yh9evXS5K2bdsmSSoqKtITTzyhmTNnqk+fPnrllVcUHx+v4cOH68CBA6XH3LNnj+rUqaP58+frwIEDOn/+vI4ePaqCggKlp6fr+++/1+HDh8vMOX36dM2dO1fR0dH65JNPNG/ePE2dOlWHDx/WqlWrVLNmTfXq1Uv5+fmqX7/+Fc+3R48eSkxM1JQpU/Txxx+rT58+Wrt2rSTp0KFDeu6559S5c2ft3LlTS5YsUa9evVRUVKR+/fppxowZmjx5srKzs0sLya8dOHBAiYmJatOmjT788ENlZWVRLoAq4Oe7lSb88u4nKo7c7CE3+8jOHnKzx99yu1IRuuFyERkZKUmqU6eOvvrqK23fvl1hYWEqKSkpXefnoBo1aqSdO3cqLCxM06ZN04wZM1RYWKgBAwaUrtuuXTtJUq1atdSyZcvSr91ud+k6sbGxOnz4sF588UUFBgbqhRde0F133aUhQ4YoISFBHo9HcXFxZebMy8srneP+++/XwoULJUnNmzdXWFiYJKl+/fpljnM5ISEhio6O1q5du/TJJ59o0aJFpeWifv36Wr58ud5//305HA55PJ7S7e6++25JUuPGjS97DMuyJEkNGjTQm2++qRo1aqioqKh0NgAAAKCqu+FPi3I4HJJ+ei9GeHi4Fi5cqFGjRqm4uLj0B+af1/lZXl6e9u7dq2XLlmnlypVasGBB6Q/iv173cnJyctSgQQOtXr1aL7zwghYtWqT9+/erqKhIK1eu1Lx58zRnzpwy2zRo0EDffPONJOmLL75QixYtKny8X3viiSe0Zs0a1apVS6GhoaXL//3f/11PPvmkFixYoM6dO5ee/+WOExISovz8fEnSiRMndO7cOUnS3LlzNWHCBKWlpal169Zl9gEAAABUZTd85+JnXbt21eTJk7V7924FBwfrzjvvVF5e3mXXrV+/vvLz8/XMM8/I6XRq1KhRCgys+Cht27ZVQkKC3n33XXk8Hr300ktq0aKFli1bps2bN8vr9Zb7JKuUlBTNmTNHlmUpICBAqampts/1gQceUFJSkl577bUyy/v06aP58+dr5cqVatSokc6ePXvFfbRv317h4eEaNGiQWrZsqTvuuEOSNGDAAE2cOFG1atW65j4AAACAqsRh8b/Gqw2Xy6VPL37r6zGAauelmO5G9+dvz9VWFeRmD7nZR3b2kJs9/pbbleY1dueiutizZ48WLFhQbvnjjz+uYcOG+WAiAAAAwD9QLn4lJiZGGRkZvh4DAAAA8Ds3/IZuAAAAAJAoFwAAAAAMoVwAAAAAMIJyAQAAAMAIygUAAAAAI/i0qGrEa1nGP48fgOTxXlKgM8DXYwAAUOVx56IaKXG7fT2CX3K5XL4ewW/dKtlRLAAAqBjKBQAAAAAjKBcAAAAAjKBcAAAAADCCcgEAAADACMoFAAAAACMoFwAAAACMoFwAAAAAMIJyAQAAAMAIygUAAAAAIygXAAAAAIygXAAAAAAwgnIBAAAAwAjKBQAAAAAjKBcAAAAAjKBcAAAAADCCcgEAAADACMoFAAAAACMoFwAAAACMoFwAAAAAMIJyAQAAAMAIygUAAAAAIygXAAAAAIygXAAAAAAwgnIBAAAAwAjKBQAAAAAjKBcAAAAAjKBcVCPBISG+HsEvRUdH+3oEv0V29pCbPdU1N4/3kq9HAABjAn09AMxxOhxatuczX48BALgOL8V09/UIAGAMdy4AAAAAGEG5AAAAAGAE5QIAAACAEZQLAAAAAEZQLgAAAAAYQbkAAAAAYATlAgAAAIARlAsAAAAARvjkl+gdP35cAwYMULt27UqXde7cWePHj6/w9gkJCVq/fv1lX09PT9fWrVv1ww8/KC8vT61atZIkrVmzRgEBATd+Ar/So0cPNW7cWE6nU263W+3atVNSUpJCQkIUHx+vtLQ0BQcH39AxlixZooiICA0dOtTQ1AAAAIBZPvsN3a1atVJGRkal7Hv06NEaPXq0cnJytG7dOi1evLhSjvNLq1evVkhIiCRp+fLlWrx4sZKSkm7KsQEAAICqwGfl4td+XQS6deumbdu26dSpU5oxY4bcbrdCQkI0Z84cW/v3er3q3bu3MjMzVadOHa1du1ZFRUU6ePCgLMvSqVOndP78eaWlpally5bKyMjQxo0b5XA41LdvX40YMaLCx3ruuefUt29fJSUlqUePHtq8ebPOnDlT7jzq1auniRMnqrCwUBcuXFB8fLwefPBBbd68WWvWrJHT6dS9996rKVOm2DpnAAAA4GbyWbk4cOCA4uLiSv88aNCgy66XlpamuLg4Pfzww/r888/1+uuvKz4+/rqP53Q61b9/f23atEnPPvusNmzYoKVLl+r1119Xs2bNlJaWpq1bt2rBggWaMmWKPvroI61du1bST2XhwQcfVFRUVIWOVaNGDbnd7muex/PPP6+CggKlp6fr+++/1+HDh1VQUKAlS5bob3/7m2rWrKmXX35Z27Ztu+7zBQD4D5fLVWn7Li4urtT9V2dkZw+52VNdcqsyj0Xl5OSUed2yLElSbm6u3nrrLaWnp8uyLAUG2h/5d7/7nRISEnT//fcrIiJCERERkqQuXbpIkjp27KjU1FTl5ubq5MmTGjlypCTp3LlzOnLkSIXLRWFhoUJDQ8ssu9x53HXXXRoyZIgSEhLk8XgUFxeno0eP6syZMxo7dqwkqaioSEePHrV9zgCAqi86OrrS9u1yuSp1/9UZ2dlDbvb4W25XKkJV5rGokJAQ5efnS5JOnDihc+fOSZKioqI0atQoderUSQcPHtQXX3xh+xhNmzZVeHi4VqxYoaeffrp0+d69e3Xfffdp586duuuuuxQVFaVWrVopPT1dDodDa9asUZs2bSp8nLfffluPP/54mWWXO4/9+/erqKhIK1euVF5enp555hm9//77aty4sVavXq2goCBlZWUpOjpan3zyie3zBgAAAG6GKlMu2rdvr/DwcA0aNEgtW7bUHXfcIUlKTExUcnKy3G63iouL9eqrr97QcQYPHqyUlBQtWLCgdFl2dra2bNkir9er1157Tc2aNVPXrl01dOhQlZSUKCYmRg0bNrzqfkeNGiWn0ymv16vo6GhNnTq1zOuXO48WLVpo2bJl2rx5s7xeryZMmKB69epp5MiRiouL06VLl9S0adNyRQUAAACoihzWz88f3SI2b96s3NxcTZw4UZKUlJSkvn37KjY21seT3TiXy6VPL37r6zEAANfhpZjulbp/f3vUoiohO3vIzR5/y+1K81aZOxd2LF26tNx7NSQpNTVVzZo1K7d80aJFysnJ0YoVK677WFu2bNGaNWvKLR8xYoQeffTR694fAAAAUN3ccncuqjPuXACA/+HORdVFdvaQmz3+ltuV5nX6YBYAAAAA1RDlAgAAAIARlAsAAAAARlAuAAAAABhBuQAAAABgBOUCAAAAgBGUCwAAAABG+PUv0UNZXsuq9M9LBwCY5fFeUqAzwNdjAIAR3LmoRkrcbl+P4JdcLpevR/BbZGcPudlTXXOjWACoTigXAAAAAIygXAAAAAAwgnIBAAAAwAjKBQAAAAAjKBcAAAAAjKBcAAAAADCCcgEAAADACMoFAAAAACMoFwAAAACMoFwAAAAAMIJyAQAAAMAIygUAAAAAIygXAAAAAIygXAAAAAAwgnIBAAAAwAjKBQAAAAAjKBcAAAAAjKBcAAAAADCCcgEAAADACMoFAAAAACMoFwAAAACMoFwAAAAAMIJyAQAAAMAIygUAAAAAIygXAAAAAIxwWJZl+XoImLF33z61u/tuX48BAACASubxXlKgM8Bnx3e5XIqOji63PNAHs6CSOB0OLdvzma/HAAAAQCV7Kaa7r0e4LB6LAgAAAGAE5QIAAACAEZQLAAAAAEZQLgAAAAAYQbkAAAAAYATlAgAAAIARlAsAAAAARlAuAAAAABhBuQAAAABgBOUCAAAAgBGBvh7ArpycHE2aNEmtWrWSJLndbvXv319xcXGVfuyTJ0/qm2++UY8ePa64Tvv27dWxY0dJksfjUcuWLZWcnKzAwP+PPD8/X8uWLVNycnJljwwAAABUOr8tF5LUpUsXLV68WJJUUlKiPn366Mknn1StWrUq9bjbt2/XoUOHrlouateurYyMjNI/T5o0SVu3blXPnj1Ll9WvX59iAQAAgGrDr8vFLxUWFsrpdGrkyJFq1qyZzp07p5UrVyo5OVlHjhyR1+vVpEmT1LlzZy1evFg5OTnyeDx67LHHNHbsWO3fv18pKSmSpDp16ig1NVX79u3T22+/raCgIB0/flx9+/bV2LFjtXLlShUXF6tjx45lysKVXLx4UefPn9dtt92mJUuWaNeuXTp//rzmzp2rV155RevXr9d//ud/aunSpbIsS+3atdOsWbP05ZdfavHixQoICFCzZs00e/ZsBQUFVXaUAAAA8AMul8vXI5Tj1+Vi+/btiouLk8PhUFBQkGbMmKH09HQ98cQTevTRR7V27VrVrVtXqampOnv2rIYPH65Nmzbpww8/1F/+8hc1aNBAWVlZkqQZM2YoNTVVrVq1UmZmptLT0/XAAw/o5MmT2rBhg0pKSvTQQw/phRde0NixY3Xo0KGrFotz586VPqLlcDgUGxurrl276ssvv1RUVJSmT5+u48ePS/rpsak5c+YoMzNTt99+u95++22dOnVKM2bM0Nq1a3X77bfr3/7t3/Qf//EfGjx4cOUHCwAAgCovOjraZ8e+UrHx63Lxy8eifpaenq7IyEhJUm5urnbs2KE9e/ZI+umH+DNnzmjBggVauHChvvvuOz300EOSpIMHD2rWrFmSfrrT0KJFC0lS69atFRgYqMDAQNWoUaPCs/36sahf+nm+n509e1a1atXS7bffLkkaM2aMvv/+e+Xl5WnSpEmSpOLiYj3wwAMVPj4AAABws/l1ubgSh8MhSYqKilKjRo30/PPPq7i4WMuXL1dYWJj+/ve/a9GiRZKkvn37ql+/foqMjFRaWpqaNGmiHTt2KD8/v8y+fsnpdMrr9dqez+ks+yFdt99+u3744QcVFBSoTp06SklJ0YABA9SoUSO9+eabCg8P15YtW3TbbbfZPiYAAABQ2aplufjZM888o+nTp2v48OEqLCzUsGHDFBwcrNq1a2vw4MGqUaOGunXrpiZNmig5OVmJiYnyeDxyOByaO3eu8vLyLrvf1q1ba/ny5WrXrp369et3w3M6nU7NnDlT48aNk9Pp1N13363f/OY3evXVVzV27FhZlqXQ0FDNnz//ho8FAAAAVBaHZVmWr4eAGS6XS59e/NbXYwAAAKCSvRTT3afHd7lcl33PR7W+c1HZ3nvvPW3cuLHc8oSEhNLfcQEAAADcKigXN2DIkCEaMmSIr8cAAAAAqgTntVcBAAAAgGujXAAAAAAwgnIBAAAAwAjKBQAAAAAjKBcAAAAAjODToqoRr2X5/DOPAQAAUPk83ksKdAb4eoxyuHNRjZS43b4ewS+5XC5fj+C3yM4ecrOH3OwhN/vIzh5ys+d6c6uKxUKiXAAAAAAwhHIBAAAAwAjKBQAAAAAjKBcAAAAAjKBcAAAAADCCcgEAAADACMoFAAAAACMoFwAAAACMoFwAAAAAMIJyAQAAAMAIygUAAAAAIygXAAAAAIxwWJZl+XoImLF7926FhIT4egwAAABUc263Wx06dCi3nHIBAAAAwAgeiwIAAABgBOUCAAAAgBGUCwAAAABGUC4AAAAAGEG5AAAAAGBEoK8HQMV4vV4lJydr//79Cg4OVkpKiu68887S19evX69169YpMDBQL7zwgh555BGdOXNGU6ZMUXFxsRo0aKDXXntNNWvW9OFZ3Hx2cisoKFDv3r3VunVrSVKvXr30+9//3len4BPXyk2Szpw5o6FDh2rDhg0KCQlRcXGxXn75ZX3//fcKDQ1VWlqa6tWr56Mz8B072VmWpdjYWLVo0UKS1KFDB02ePNkH0/vOtXJbs2aNNm3aJEl6+OGHNX78eK452cuN6+3auf31r39VVlaWHA6HRo0apb59+3K9/ZOd7LjmKva9wev1auzYserZs6eGDh3qv9ecBb/w8ccfW4mJiZZlWdauXbus559/vvS1vLw864knnrDcbrf1ww8/lH49Z84c629/+5tlWZb11ltvWX/60598MbpP2clt27Zt1uzZs301cpVwtdwsy7Kys7OtJ5980urYsaNVXFxsWZZlrV692nrjjTcsy7KsjRs3WnPmzLm5Q1cRdrI7fPiwNW7cuJs+a1VytdyOHj1qPfXUU5bH47G8Xq81ZMgQy+Vycc1Z9nLjert6bt9//73Vr18/q6SkxPrxxx+t2NhYy+v1cr39k53suOau/b3Bsixr4cKF1qBBg6y1a9daluW/31d5LMpP7NixQw899JCknxr/119/Xfranj171LFjRwUHBys8PFzNmzfXN998U2ab2NhY/fd//7dPZvclO7l9/fXX2rt3r4YPH64JEyYoLy/PV+P7zNVykySn06k//elPqlOnzmW3iY2N1eeff37T5q1K7GS3d+9effvtt4qLi9OYMWN06NChmzlylXC13Bo1aqT09HQFBATI4XDI4/EoJCSEa072cuN6u3pu9erV0wcffKCgoCB99913CgkJkcPh4Hr7JzvZcc1d+3vD3//+dzkcjtJ1fr2NP11zlAs/UVhYqLCwsNI/BwQEyOPxlL4WHh5e+lpoaKgKCwvLLA8NDdWPP/54c4euAuzkFhUVpQkTJuidd95Rr169lJKSctPn9rWr5SZJ3bp1U926dcttc6tfb5K97OrXr6+xY8cqIyND48aN08svv3zT5q0qrpZbUFCQ6tWrJ8uylJaWprvvvluRkZFcc7KXG9fbtf+eBgYG6p133tGQIUM0YMCA0m1u9etNspcd19zVc8vNzdXGjRs1ceLEctv44zXHey78RFhYmIqKikr/7PV6FRgYeNnXioqKFB4eXrq8Ro0aKioqUq1atW763L5mJ7eYmJjS96Y8+uijeuONN27u0FXA1XKryDa36vUm2cuuffv2CggIkCTdd999ysvLk2VZcjgclTprVXKt3Nxut6ZNm6bQ0FDNnDmz3Da36jVnJzeut4r9PR0+fLgGDx6sMWPGaPv27Vxv/2Qnu3vuuYdr7iq5ffDBB/r222/1+9//XidOnFBQUJCaNm3qt9ccdy78RKdOnZSdnS1J2r17d+mbjSUpJiZGO3bskNvt1o8//qiDBw+qdevW6tSpk7Zu3SpJys7O1r333uuT2X3JTm7Tp0/Xxx9/LEn6/PPP1a5dO5/M7ktXy+1q29zq15tkL7ulS5fqz3/+syTpm2++UePGjW+pb7rS1XOzLEsvvvii2rRpo9mzZ5f+kMI1Zy83rrer53bo0KHSN74HBQUpODhYTqeT6+2f7GTHNXf13KZOnarMzExlZGToqaee0siRIxUbG+u315zDsizL10Pg2n7+lIHc3FxZlqXU1FRlZ2erefPm6tmzp9avX6/33ntPlmVp3Lhx6t27t7777jslJiaqqKhIdevW1cKFC3Xbbbf5+lRuKju5HTt2TNOmTZMk1axZUykpKWrQoIGPz+TmulZuP+vRo4c2b96skJAQXbhwQYmJicrPz1dQUJAWLlyo+vXr+/AsfMNOdufOndPLL7+s8+fPKyAgQP/6r/+qli1b+vAsbr6r5eb1epWQkKAOHTqUrp+QkKC2bdve8tecndyioqK43q7x93Tp0qXKzs4ufQZ+/Pjx/Bv3T3ay49+4in9vWLJkiSIiIjR06FC/veYoFwAAAACM4LEoAAAAAEZQLgAAAAAYQbkAAAAAYATlAgAAAIARlAsAAAAARlAuAAAAABhBuQAAAABgBOUCAAAAgBH/B0iDWVzgaWAjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot graph of feature importances for better visualization\n",
    "plt.figure(figsize=[12,6])\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(6).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Present_Price             0.407490\n",
      "Fuel_Type_Diesel          0.234346\n",
      "Transmission_Manual       0.137330\n",
      "Seller_Type_Individual    0.114018\n",
      "Car_age                   0.106204\n",
      "Owner                     0.000611\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(feat_importances.sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Selling_Price', 'Present_Price', 'Owner', 'Car_age',\n",
       "       'Fuel_Type_Diesel', 'Seller_Type_Individual', 'Transmission_Manual'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_multicolinearity.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting useful features.\n",
    "final_df = data_no_multicolinearity[['Selling_Price', 'Present_Price', 'Car_age',\n",
    "       'Fuel_Type_Diesel', 'Seller_Type_Individual', 'Transmission_Manual']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Car_age</th>\n",
       "      <th>Fuel_Type_Diesel</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>6</td>\n",
       "      <td>0</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>7</td>\n",
       "      <td>1</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>3</td>\n",
       "      <td>0</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>9</td>\n",
       "      <td>0</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>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Car_age  Fuel_Type_Diesel  \\\n",
       "0           3.35           5.59        6                 0   \n",
       "1           4.75           9.54        7                 1   \n",
       "2           7.25           9.85        3                 0   \n",
       "3           2.85           4.15        9                 0   \n",
       "4           4.60           6.87        6                 1   \n",
       "\n",
       "   Seller_Type_Individual  Transmission_Manual  \n",
       "0                       0                    1  \n",
       "1                       0                    1  \n",
       "2                       0                    1  \n",
       "3                       0                    1  \n",
       "4                       0                    1  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Declaring dependent and independent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = final_df.drop('Selling_Price', axis=1)\n",
    "y = final_df['Selling_Price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature scallng on training data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X[['Present_Price','Car_age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_scaled = scaler.transform(X[['Present_Price','Car_age']])\n",
    "scaled_data = pd.DataFrame(input_scaled, columns=['Present_Price','Car_age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled =scaled_data.join(X.drop(['Present_Price','Car_age'],axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X_scaled,y,test_size=0.2, random_state=365)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.8407655400238144\n"
     ]
    }
   ],
   "source": [
    "# Model Building\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "\n",
    "# Training Model\n",
    "lr.fit(x_train,y_train)\n",
    "\n",
    "# Model Summary\n",
    "y_pred_lr = lr.predict(x_test)\n",
    "\n",
    "r_squared = r2_score(y_test,y_pred_lr)\n",
    "rmse = np.sqrt(mean_squared_error(y_test,y_pred_lr))\n",
    "print(\"R_squared :\",r_squared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Regressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.914701703817708\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "# Training Model\n",
    "rf.fit(x_train,y_train)\n",
    "\n",
    "# Model Summary\n",
    "y_pred_rf = rf.predict(x_test)\n",
    "\n",
    "r_squared = r2_score(y_test,y_pred_rf)\n",
    "rmse = np.sqrt(mean_squared_error(y_test,y_pred_rf))\n",
    "print(\"R_squared :\",r_squared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GBT Regressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.8757563709327358\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "gbt = GradientBoostingRegressor()\n",
    "\n",
    "# Training Model\n",
    "gbt.fit(x_train,y_train)\n",
    "\n",
    "# Model Summary\n",
    "y_pred_gbt = gbt.predict(x_test)\n",
    "\n",
    "r_squared = r2_score(y_test,y_pred_gbt)\n",
    "rmse = np.sqrt(mean_squared_error(y_test,y_pred_gbt))\n",
    "print(\"R_squared :\",r_squared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***So as we try diferent Regression Alorithms and found that \"Random Forest Regressor Model\" is giving better accuracy compare to other.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter optimization using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "#Randomized Search CV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]\n",
    "\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   1.0s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.9s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.3s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.4s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.5s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.6s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.8s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.7s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.6s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [5, 10, 15, 20, 25, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 5, 10],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 1000,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.8091158861314913\n"
     ]
    }
   ],
   "source": [
    "r_squared = r2_score(y_test,predictions)\n",
    "rmse = np.sqrt(mean_squared_error(y_test,predictions))\n",
    "print(\"R_squared :\",r_squared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# save the model to disk\n",
    "import pickle\n",
    "pickle.dump(rf_random, open('rf_model', 'wb'))\n",
    "\n",
    "# load the model from disk\n",
    "loaded_model = pickle.load(open('rf_model','rb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test the model by giving input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(PresentPrice, CarAge, FuelType,SellerType, Transmissionmanual):\n",
    "    # feature scaling on present price and car age\n",
    "    scaled_value = scaler.transform([[float(PresentPrice), int(CarAge)]])\n",
    "    \n",
    "    price = scaled_value[0,0]\n",
    "    age = scaled_value[0,1]\n",
    "    \n",
    "    fuel = int(FuelType)\n",
    "    Seller = int(SellerType)\n",
    "    trans = int(Transmissionmanual)\n",
    "    \n",
    "    return rf_random.predict([[price, age, fuel, Seller, trans]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.79525])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction(25, 14, 0, 2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.42486])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction(23.73, 14, 0, 1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Present price of a car plays an important role in predicting Selling Price, One increases the other gradually increases.\n",
    "* Car age is effecting negatively as older the car lesser the Selling Price.\n",
    "* Selling Price of cars with Fuel type Diesel is higher.\n",
    "* Car of Manual type is of less priced whereas of Automatic type is high.\n",
    "* Cars sold by Individual tend to get less Selling Price when sold by Dealers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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