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": 2,
   "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": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('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": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "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": 5,
   "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": 6,
   "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": 6,
     "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": 7,
   "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": 8,
   "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": 8,
     "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": 9,
   "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": 10,
   "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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 14,
     "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": 15,
   "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": 15,
     "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": 16,
   "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": 17,
   "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": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.   , 0.126, 0.   , 0.   , 0.   , 0.   ])"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "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": 20,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Present_Price             0.367064\n",
      "Fuel_Type_Diesel          0.242885\n",
      "Transmission_Manual       0.149601\n",
      "Seller_Type_Individual    0.134595\n",
      "Car_age                   0.104367\n",
      "Owner                     0.001488\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(feat_importances.sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_no_multicolinearity.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "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": 26,
     "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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "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": 30,
   "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": 31,
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.8407655400238141\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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.9208431915625187\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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.8866842345704391\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": 35,
   "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": 36,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.2s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.1s\n",
      "[CV] n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
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      "[CV]  n_estimators=900, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=   1.1s\n",
      "[CV] n_estimators=1100, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
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      "[CV] n_estimators=1100, min_samples_split=15, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
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      "[CV] n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=700, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=5, total=   0.7s\n",
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      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.7s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.7s\n",
      "[CV] n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20 \n",
      "[CV]  n_estimators=700, min_samples_split=15, min_samples_leaf=1, max_features=auto, max_depth=20, total=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   37.5s finished\n"
     ]
    },
    {
     "data": {
      "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.8016218757946758\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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# save the model to disk\\nimport pickle\\npickle.dump(rf_random, open('rf_model', 'wb'))\\n\\n# load the model from disk\\nloaded_model = pickle.load(open('rf_model','rb'))\\n\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 42,
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.31427333])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction(25, 14, 0, 2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.49061333])"
      ]
     },
     "execution_count": 45,
     "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."
   ]
  }
 ],
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