car-price-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import module__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dataframe__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  carlength  carwidth  \\\n",
       "0  convertible        rwd          front       88.6      168.8      64.1   \n",
       "1  convertible        rwd          front       88.6      168.8      64.1   \n",
       "2    hatchback        rwd          front       94.5      171.2      65.5   \n",
       "3        sedan        fwd          front       99.8      176.6      66.2   \n",
       "4        sedan        4wd          front       99.4      176.6      66.4   \n",
       "\n",
       "   carheight  curbweight enginetype cylindernumber  enginesize fuelsystem  \\\n",
       "0       48.8        2548       dohc           four         130       mpfi   \n",
       "1       48.8        2548       dohc           four         130       mpfi   \n",
       "2       52.4        2823       ohcv            six         152       mpfi   \n",
       "3       54.3        2337        ohc           four         109       mpfi   \n",
       "4       54.3        2824        ohc           five         136       mpfi   \n",
       "\n",
       "   boreratio  stroke  compressionratio  horsepower  peakrpm  citympg  \\\n",
       "0       3.47    2.68               9.0         111     5000       21   \n",
       "1       3.47    2.68               9.0         111     5000       21   \n",
       "2       2.68    3.47               9.0         154     5000       19   \n",
       "3       3.19    3.40              10.0         102     5500       24   \n",
       "4       3.19    3.40               8.0         115     5500       18   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_df = pd.read_csv('CarPrice.csv')\n",
    "cp_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data preparation__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   car_ID            205 non-null    int64  \n",
      " 1   symboling         205 non-null    int64  \n",
      " 2   CarName           205 non-null    object \n",
      " 3   fueltype          205 non-null    object \n",
      " 4   aspiration        205 non-null    object \n",
      " 5   doornumber        205 non-null    object \n",
      " 6   carbody           205 non-null    object \n",
      " 7   drivewheel        205 non-null    object \n",
      " 8   enginelocation    205 non-null    object \n",
      " 9   wheelbase         205 non-null    float64\n",
      " 10  carlength         205 non-null    float64\n",
      " 11  carwidth          205 non-null    float64\n",
      " 12  carheight         205 non-null    float64\n",
      " 13  curbweight        205 non-null    int64  \n",
      " 14  enginetype        205 non-null    object \n",
      " 15  cylindernumber    205 non-null    object \n",
      " 16  enginesize        205 non-null    int64  \n",
      " 17  fuelsystem        205 non-null    object \n",
      " 18  boreratio         205 non-null    float64\n",
      " 19  stroke            205 non-null    float64\n",
      " 20  compressionratio  205 non-null    float64\n",
      " 21  horsepower        205 non-null    int64  \n",
      " 22  peakrpm           205 non-null    int64  \n",
      " 23  citympg           205 non-null    int64  \n",
      " 24  highwaympg        205 non-null    int64  \n",
      " 25  price             205 non-null    float64\n",
      "dtypes: float64(8), int64(8), object(10)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "cp_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>fuelsystem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>mpfi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>mpfi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>mpfi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>mpfi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>mpfi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    CarName fueltype aspiration doornumber      carbody  \\\n",
       "0        alfa-romero giulia      gas        std        two  convertible   \n",
       "1       alfa-romero stelvio      gas        std        two  convertible   \n",
       "2  alfa-romero Quadrifoglio      gas        std        two    hatchback   \n",
       "3               audi 100 ls      gas        std       four        sedan   \n",
       "4                audi 100ls      gas        std       four        sedan   \n",
       "\n",
       "  drivewheel enginelocation enginetype cylindernumber fuelsystem  \n",
       "0        rwd          front       dohc           four       mpfi  \n",
       "1        rwd          front       dohc           four       mpfi  \n",
       "2        rwd          front       ohcv            six       mpfi  \n",
       "3        fwd          front        ohc           four       mpfi  \n",
       "4        4wd          front        ohc           five       mpfi  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df = cp_df.select_dtypes(include = ['object']).copy()\n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alfa-romero giulia', 'alfa-romero stelvio',\n",
       "       'alfa-romero Quadrifoglio', 'audi 100 ls', 'audi 100ls',\n",
       "       'audi fox', 'audi 5000', 'audi 4000', 'audi 5000s (diesel)',\n",
       "       'bmw 320i', 'bmw x1', 'bmw x3', 'bmw z4', 'bmw x4', 'bmw x5',\n",
       "       'chevrolet impala', 'chevrolet monte carlo', 'chevrolet vega 2300',\n",
       "       'dodge rampage', 'dodge challenger se', 'dodge d200',\n",
       "       'dodge monaco (sw)', 'dodge colt hardtop', 'dodge colt (sw)',\n",
       "       'dodge coronet custom', 'dodge dart custom',\n",
       "       'dodge coronet custom (sw)', 'honda civic', 'honda civic cvcc',\n",
       "       'honda accord cvcc', 'honda accord lx', 'honda civic 1500 gl',\n",
       "       'honda accord', 'honda civic 1300', 'honda prelude',\n",
       "       'honda civic (auto)', 'isuzu MU-X', 'isuzu D-Max ',\n",
       "       'isuzu D-Max V-Cross', 'jaguar xj', 'jaguar xf', 'jaguar xk',\n",
       "       'maxda rx3', 'maxda glc deluxe', 'mazda rx2 coupe', 'mazda rx-4',\n",
       "       'mazda glc deluxe', 'mazda 626', 'mazda glc', 'mazda rx-7 gs',\n",
       "       'mazda glc 4', 'mazda glc custom l', 'mazda glc custom',\n",
       "       'buick electra 225 custom', 'buick century luxus (sw)',\n",
       "       'buick century', 'buick skyhawk', 'buick opel isuzu deluxe',\n",
       "       'buick skylark', 'buick century special',\n",
       "       'buick regal sport coupe (turbo)', 'mercury cougar',\n",
       "       'mitsubishi mirage', 'mitsubishi lancer', 'mitsubishi outlander',\n",
       "       'mitsubishi g4', 'mitsubishi mirage g4', 'mitsubishi montero',\n",
       "       'mitsubishi pajero', 'Nissan versa', 'nissan gt-r', 'nissan rogue',\n",
       "       'nissan latio', 'nissan titan', 'nissan leaf', 'nissan juke',\n",
       "       'nissan note', 'nissan clipper', 'nissan nv200', 'nissan dayz',\n",
       "       'nissan fuga', 'nissan otti', 'nissan teana', 'nissan kicks',\n",
       "       'peugeot 504', 'peugeot 304', 'peugeot 504 (sw)', 'peugeot 604sl',\n",
       "       'peugeot 505s turbo diesel', 'plymouth fury iii',\n",
       "       'plymouth cricket', 'plymouth satellite custom (sw)',\n",
       "       'plymouth fury gran sedan', 'plymouth valiant', 'plymouth duster',\n",
       "       'porsche macan', 'porcshce panamera', 'porsche cayenne',\n",
       "       'porsche boxter', 'renault 12tl', 'renault 5 gtl', 'saab 99e',\n",
       "       'saab 99le', 'saab 99gle', 'subaru', 'subaru dl', 'subaru brz',\n",
       "       'subaru baja', 'subaru r1', 'subaru r2', 'subaru trezia',\n",
       "       'subaru tribeca', 'toyota corona mark ii', 'toyota corona',\n",
       "       'toyota corolla 1200', 'toyota corona hardtop',\n",
       "       'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii',\n",
       "       'toyota corolla', 'toyota corolla liftback',\n",
       "       'toyota celica gt liftback', 'toyota corolla tercel',\n",
       "       'toyota corona liftback', 'toyota starlet', 'toyota tercel',\n",
       "       'toyota cressida', 'toyota celica gt', 'toyouta tercel',\n",
       "       'vokswagen rabbit', 'volkswagen 1131 deluxe sedan',\n",
       "       'volkswagen model 111', 'volkswagen type 3', 'volkswagen 411 (sw)',\n",
       "       'volkswagen super beetle', 'volkswagen dasher', 'vw dasher',\n",
       "       'vw rabbit', 'volkswagen rabbit', 'volkswagen rabbit custom',\n",
       "       'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245',\n",
       "       'volvo 264gl', 'volvo diesel', 'volvo 246'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df['CarName'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
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       "    .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>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>CarBrand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
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       "      <td>four</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>alfa-romero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fueltype aspiration doornumber      carbody drivewheel enginelocation  \\\n",
       "0      gas        std        two  convertible        rwd          front   \n",
       "1      gas        std        two  convertible        rwd          front   \n",
       "2      gas        std        two    hatchback        rwd          front   \n",
       "3      gas        std       four        sedan        fwd          front   \n",
       "4      gas        std       four        sedan        4wd          front   \n",
       "\n",
       "  enginetype cylindernumber fuelsystem     CarBrand  \n",
       "0       dohc           four       mpfi  alfa-romero  \n",
       "1       dohc           four       mpfi  alfa-romero  \n",
       "2       ohcv            six       mpfi  alfa-romero  \n",
       "3        ohc           four       mpfi         audi  \n",
       "4        ohc           five       mpfi         audi  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car = obj_df.CarName.str.split(expand = True) \n",
    "Brand = car[0] \n",
    "obj_df['CarBrand'] = Brand \n",
    "obj_df.drop(columns = ['CarName'],inplace = True) \n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "four    115\n",
      "two      90\n",
      "Name: doornumber, dtype: int64\n",
      "\n",
      "\n",
      "four      159\n",
      "six        24\n",
      "five       11\n",
      "eight       5\n",
      "two         4\n",
      "three       1\n",
      "twelve      1\n",
      "Name: cylindernumber, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(obj_df['doornumber'].value_counts()) \n",
    "print('\\n')\n",
    "print(obj_df['cylindernumber'].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
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       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohc</td>\n",
       "      <td>4</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>ohc</td>\n",
       "      <td>5</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>audi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fueltype aspiration  doornumber      carbody drivewheel enginelocation  \\\n",
       "0      gas        std           2  convertible        rwd          front   \n",
       "1      gas        std           2  convertible        rwd          front   \n",
       "2      gas        std           2    hatchback        rwd          front   \n",
       "3      gas        std           4        sedan        fwd          front   \n",
       "4      gas        std           4        sedan        4wd          front   \n",
       "\n",
       "  enginetype  cylindernumber fuelsystem     CarBrand  \n",
       "0       dohc               4       mpfi  alfa-romero  \n",
       "1       dohc               4       mpfi  alfa-romero  \n",
       "2       ohcv               6       mpfi  alfa-romero  \n",
       "3        ohc               4       mpfi         audi  \n",
       "4        ohc               5       mpfi         audi  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encodedoorandcyl = {\"doornumber\":     {\"four\": 4, \"two\": 2},\n",
    "                \"cylindernumber\": {\"four\": 4, \"six\": 6, \"five\": 5, \"eight\": 8,\n",
    "                                  \"two\": 2, \"three\": 3, \"twelve\":12 }}\n",
    "obj_df = obj_df.replace(encodedoorandcyl) \n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ohc</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   doornumber enginetype  cylindernumber  carbody_convertible  \\\n",
       "0           2       dohc               4                    1   \n",
       "1           2       dohc               4                    1   \n",
       "2           2       ohcv               6                    0   \n",
       "3           4        ohc               4                    0   \n",
       "4           4        ohc               5                    0   \n",
       "\n",
       "   carbody_hardtop  carbody_hatchback  carbody_sedan  carbody_wagon  \\\n",
       "0                0                  0              0              0   \n",
       "1                0                  0              0              0   \n",
       "2                0                  1              0              0   \n",
       "3                0                  0              1              0   \n",
       "4                0                  0              1              0   \n",
       "\n",
       "   drivewheel_4wd  drivewheel_fwd  drivewheel_rwd  fueltype_diesel  \\\n",
       "0               0               0               1                0   \n",
       "1               0               0               1                0   \n",
       "2               0               0               1                0   \n",
       "3               0               1               0                0   \n",
       "4               1               0               0                0   \n",
       "\n",
       "   fueltype_gas  aspiration_std  aspiration_turbo  enginelocation_front  \\\n",
       "0             1               1                 0                     1   \n",
       "1             1               1                 0                     1   \n",
       "2             1               1                 0                     1   \n",
       "3             1               1                 0                     1   \n",
       "4             1               1                 0                     1   \n",
       "\n",
       "   enginelocation_rear  fuelsystem_1bbl  fuelsystem_2bbl  fuelsystem_4bbl  \\\n",
       "0                    0                0                0                0   \n",
       "1                    0                0                0                0   \n",
       "2                    0                0                0                0   \n",
       "3                    0                0                0                0   \n",
       "4                    0                0                0                0   \n",
       "\n",
       "   fuelsystem_idi  fuelsystem_mfi  fuelsystem_mpfi  fuelsystem_spdi  \\\n",
       "0               0               0                1                0   \n",
       "1               0               0                1                0   \n",
       "2               0               0                1                0   \n",
       "3               0               0                1                0   \n",
       "4               0               0                1                0   \n",
       "\n",
       "   fuelsystem_spfi  CarBrand_Nissan  CarBrand_alfa-romero  CarBrand_audi  \\\n",
       "0                0                0                     1              0   \n",
       "1                0                0                     1              0   \n",
       "2                0                0                     1              0   \n",
       "3                0                0                     0              1   \n",
       "4                0                0                     0              1   \n",
       "\n",
       "   CarBrand_bmw  CarBrand_buick  CarBrand_chevrolet  CarBrand_dodge  \\\n",
       "0             0               0                   0               0   \n",
       "1             0               0                   0               0   \n",
       "2             0               0                   0               0   \n",
       "3             0               0                   0               0   \n",
       "4             0               0                   0               0   \n",
       "\n",
       "   CarBrand_honda  CarBrand_isuzu  CarBrand_jaguar  CarBrand_maxda  \\\n",
       "0               0               0                0               0   \n",
       "1               0               0                0               0   \n",
       "2               0               0                0               0   \n",
       "3               0               0                0               0   \n",
       "4               0               0                0               0   \n",
       "\n",
       "   CarBrand_mazda  CarBrand_mercury  CarBrand_mitsubishi  CarBrand_nissan  \\\n",
       "0               0                 0                    0                0   \n",
       "1               0                 0                    0                0   \n",
       "2               0                 0                    0                0   \n",
       "3               0                 0                    0                0   \n",
       "4               0                 0                    0                0   \n",
       "\n",
       "   CarBrand_peugeot  CarBrand_plymouth  CarBrand_porcshce  CarBrand_porsche  \\\n",
       "0                 0                  0                  0                 0   \n",
       "1                 0                  0                  0                 0   \n",
       "2                 0                  0                  0                 0   \n",
       "3                 0                  0                  0                 0   \n",
       "4                 0                  0                  0                 0   \n",
       "\n",
       "   CarBrand_renault  CarBrand_saab  CarBrand_subaru  CarBrand_toyota  \\\n",
       "0                 0              0                0                0   \n",
       "1                 0              0                0                0   \n",
       "2                 0              0                0                0   \n",
       "3                 0              0                0                0   \n",
       "4                 0              0                0                0   \n",
       "\n",
       "   CarBrand_toyouta  CarBrand_vokswagen  CarBrand_volkswagen  CarBrand_volvo  \\\n",
       "0                 0                   0                    0               0   \n",
       "1                 0                   0                    0               0   \n",
       "2                 0                   0                    0               0   \n",
       "3                 0                   0                    0               0   \n",
       "4                 0                   0                    0               0   \n",
       "\n",
       "   CarBrand_vw  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot = pd.get_dummies(data = obj_df, \n",
    "                         columns=['carbody','drivewheel','fueltype',\n",
    "                                  'aspiration','enginelocation'\n",
    "                                  ,'fuelsystem','CarBrand'])\n",
    "obj_df = one_hot \n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ohc      148\n",
       "ohcf      15\n",
       "ohcv      13\n",
       "dohc      12\n",
       "l         12\n",
       "rotor      4\n",
       "dohcv      1\n",
       "Name: enginetype, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df['enginetype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>doornumber</th>\n",
       "      <th>enginetype</th>\n",
       "      <th>cylindernumber</th>\n",
       "      <th>carbody_convertible</th>\n",
       "      <th>carbody_hardtop</th>\n",
       "      <th>carbody_hatchback</th>\n",
       "      <th>carbody_sedan</th>\n",
       "      <th>carbody_wagon</th>\n",
       "      <th>drivewheel_4wd</th>\n",
       "      <th>drivewheel_fwd</th>\n",
       "      <th>drivewheel_rwd</th>\n",
       "      <th>fueltype_diesel</th>\n",
       "      <th>fueltype_gas</th>\n",
       "      <th>aspiration_std</th>\n",
       "      <th>aspiration_turbo</th>\n",
       "      <th>enginelocation_front</th>\n",
       "      <th>enginelocation_rear</th>\n",
       "      <th>fuelsystem_1bbl</th>\n",
       "      <th>fuelsystem_2bbl</th>\n",
       "      <th>fuelsystem_4bbl</th>\n",
       "      <th>fuelsystem_idi</th>\n",
       "      <th>fuelsystem_mfi</th>\n",
       "      <th>fuelsystem_mpfi</th>\n",
       "      <th>fuelsystem_spdi</th>\n",
       "      <th>fuelsystem_spfi</th>\n",
       "      <th>CarBrand_Nissan</th>\n",
       "      <th>CarBrand_alfa-romero</th>\n",
       "      <th>CarBrand_audi</th>\n",
       "      <th>CarBrand_bmw</th>\n",
       "      <th>CarBrand_buick</th>\n",
       "      <th>CarBrand_chevrolet</th>\n",
       "      <th>CarBrand_dodge</th>\n",
       "      <th>CarBrand_honda</th>\n",
       "      <th>CarBrand_isuzu</th>\n",
       "      <th>CarBrand_jaguar</th>\n",
       "      <th>CarBrand_maxda</th>\n",
       "      <th>CarBrand_mazda</th>\n",
       "      <th>CarBrand_mercury</th>\n",
       "      <th>CarBrand_mitsubishi</th>\n",
       "      <th>CarBrand_nissan</th>\n",
       "      <th>CarBrand_peugeot</th>\n",
       "      <th>CarBrand_plymouth</th>\n",
       "      <th>CarBrand_porcshce</th>\n",
       "      <th>CarBrand_porsche</th>\n",
       "      <th>CarBrand_renault</th>\n",
       "      <th>CarBrand_saab</th>\n",
       "      <th>CarBrand_subaru</th>\n",
       "      <th>CarBrand_toyota</th>\n",
       "      <th>CarBrand_toyouta</th>\n",
       "      <th>CarBrand_vokswagen</th>\n",
       "      <th>CarBrand_volkswagen</th>\n",
       "      <th>CarBrand_volvo</th>\n",
       "      <th>CarBrand_vw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   doornumber  enginetype  cylindernumber  carbody_convertible  \\\n",
       "0           2           1               4                    1   \n",
       "1           2           1               4                    1   \n",
       "2           2           1               6                    0   \n",
       "3           4           1               4                    0   \n",
       "4           4           1               5                    0   \n",
       "\n",
       "   carbody_hardtop  carbody_hatchback  carbody_sedan  carbody_wagon  \\\n",
       "0                0                  0              0              0   \n",
       "1                0                  0              0              0   \n",
       "2                0                  1              0              0   \n",
       "3                0                  0              1              0   \n",
       "4                0                  0              1              0   \n",
       "\n",
       "   drivewheel_4wd  drivewheel_fwd  drivewheel_rwd  fueltype_diesel  \\\n",
       "0               0               0               1                0   \n",
       "1               0               0               1                0   \n",
       "2               0               0               1                0   \n",
       "3               0               1               0                0   \n",
       "4               1               0               0                0   \n",
       "\n",
       "   fueltype_gas  aspiration_std  aspiration_turbo  enginelocation_front  \\\n",
       "0             1               1                 0                     1   \n",
       "1             1               1                 0                     1   \n",
       "2             1               1                 0                     1   \n",
       "3             1               1                 0                     1   \n",
       "4             1               1                 0                     1   \n",
       "\n",
       "   enginelocation_rear  fuelsystem_1bbl  fuelsystem_2bbl  fuelsystem_4bbl  \\\n",
       "0                    0                0                0                0   \n",
       "1                    0                0                0                0   \n",
       "2                    0                0                0                0   \n",
       "3                    0                0                0                0   \n",
       "4                    0                0                0                0   \n",
       "\n",
       "   fuelsystem_idi  fuelsystem_mfi  fuelsystem_mpfi  fuelsystem_spdi  \\\n",
       "0               0               0                1                0   \n",
       "1               0               0                1                0   \n",
       "2               0               0                1                0   \n",
       "3               0               0                1                0   \n",
       "4               0               0                1                0   \n",
       "\n",
       "   fuelsystem_spfi  CarBrand_Nissan  CarBrand_alfa-romero  CarBrand_audi  \\\n",
       "0                0                0                     1              0   \n",
       "1                0                0                     1              0   \n",
       "2                0                0                     1              0   \n",
       "3                0                0                     0              1   \n",
       "4                0                0                     0              1   \n",
       "\n",
       "   CarBrand_bmw  CarBrand_buick  CarBrand_chevrolet  CarBrand_dodge  \\\n",
       "0             0               0                   0               0   \n",
       "1             0               0                   0               0   \n",
       "2             0               0                   0               0   \n",
       "3             0               0                   0               0   \n",
       "4             0               0                   0               0   \n",
       "\n",
       "   CarBrand_honda  CarBrand_isuzu  CarBrand_jaguar  CarBrand_maxda  \\\n",
       "0               0               0                0               0   \n",
       "1               0               0                0               0   \n",
       "2               0               0                0               0   \n",
       "3               0               0                0               0   \n",
       "4               0               0                0               0   \n",
       "\n",
       "   CarBrand_mazda  CarBrand_mercury  CarBrand_mitsubishi  CarBrand_nissan  \\\n",
       "0               0                 0                    0                0   \n",
       "1               0                 0                    0                0   \n",
       "2               0                 0                    0                0   \n",
       "3               0                 0                    0                0   \n",
       "4               0                 0                    0                0   \n",
       "\n",
       "   CarBrand_peugeot  CarBrand_plymouth  CarBrand_porcshce  CarBrand_porsche  \\\n",
       "0                 0                  0                  0                 0   \n",
       "1                 0                  0                  0                 0   \n",
       "2                 0                  0                  0                 0   \n",
       "3                 0                  0                  0                 0   \n",
       "4                 0                  0                  0                 0   \n",
       "\n",
       "   CarBrand_renault  CarBrand_saab  CarBrand_subaru  CarBrand_toyota  \\\n",
       "0                 0              0                0                0   \n",
       "1                 0              0                0                0   \n",
       "2                 0              0                0                0   \n",
       "3                 0              0                0                0   \n",
       "4                 0              0                0                0   \n",
       "\n",
       "   CarBrand_toyouta  CarBrand_vokswagen  CarBrand_volkswagen  CarBrand_volvo  \\\n",
       "0                 0                   0                    0               0   \n",
       "1                 0                   0                    0               0   \n",
       "2                 0                   0                    0               0   \n",
       "3                 0                   0                    0               0   \n",
       "4                 0                   0                    0               0   \n",
       "\n",
       "   CarBrand_vw  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[\"enginetype\"] = np.where(obj_df[\"enginetype\"].str.contains(\"ohc\"), 1, 0) # setiap string pada data kolom engine type yang mengandung ohc menjadi 1, yang tidak menjadi 0\n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doornumber              int64\n",
       "enginetype              int64\n",
       "cylindernumber          int64\n",
       "carbody_convertible     uint8\n",
       "carbody_hardtop         uint8\n",
       "carbody_hatchback       uint8\n",
       "carbody_sedan           uint8\n",
       "carbody_wagon           uint8\n",
       "drivewheel_4wd          uint8\n",
       "drivewheel_fwd          uint8\n",
       "drivewheel_rwd          uint8\n",
       "fueltype_diesel         uint8\n",
       "fueltype_gas            uint8\n",
       "aspiration_std          uint8\n",
       "aspiration_turbo        uint8\n",
       "enginelocation_front    uint8\n",
       "enginelocation_rear     uint8\n",
       "fuelsystem_1bbl         uint8\n",
       "fuelsystem_2bbl         uint8\n",
       "fuelsystem_4bbl         uint8\n",
       "fuelsystem_idi          uint8\n",
       "fuelsystem_mfi          uint8\n",
       "fuelsystem_mpfi         uint8\n",
       "fuelsystem_spdi         uint8\n",
       "fuelsystem_spfi         uint8\n",
       "CarBrand_Nissan         uint8\n",
       "CarBrand_alfa-romero    uint8\n",
       "CarBrand_audi           uint8\n",
       "CarBrand_bmw            uint8\n",
       "CarBrand_buick          uint8\n",
       "CarBrand_chevrolet      uint8\n",
       "CarBrand_dodge          uint8\n",
       "CarBrand_honda          uint8\n",
       "CarBrand_isuzu          uint8\n",
       "CarBrand_jaguar         uint8\n",
       "CarBrand_maxda          uint8\n",
       "CarBrand_mazda          uint8\n",
       "CarBrand_mercury        uint8\n",
       "CarBrand_mitsubishi     uint8\n",
       "CarBrand_nissan         uint8\n",
       "CarBrand_peugeot        uint8\n",
       "CarBrand_plymouth       uint8\n",
       "CarBrand_porcshce       uint8\n",
       "CarBrand_porsche        uint8\n",
       "CarBrand_renault        uint8\n",
       "CarBrand_saab           uint8\n",
       "CarBrand_subaru         uint8\n",
       "CarBrand_toyota         uint8\n",
       "CarBrand_toyouta        uint8\n",
       "CarBrand_vokswagen      uint8\n",
       "CarBrand_volkswagen     uint8\n",
       "CarBrand_volvo          uint8\n",
       "CarBrand_vw             uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonobj_df = cp_df.select_dtypes(include=['int64', 'float64']).copy() # merupakan kolom awal yang sudah memiliki type int64 dan float64\n",
    "df = obj_df.join(nonobj_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### outliers__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.columns:\n",
    "    Q1 = df[i].quantile(0.25)\n",
    "    Q3 = df[i].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    LowerBound = Q1 - 1.5 * IQR\n",
    "    UpperBound = Q3 + 1.5 * IQR\n",
    "    df = df[(df[i] >= LowerBound)|(df[i] <= UpperBound)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature selection__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize = (15,15)) \n",
    "corr = df.corr().iloc[:,-1:] \n",
    "sns.heatmap(corr, annot = True) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['doornumber',\n",
       " 'enginetype',\n",
       " 'carbody_convertible',\n",
       " 'aspiration_std',\n",
       " 'enginelocation_front',\n",
       " 'fuelsystem_1bbl',\n",
       " 'CarBrand_bmw',\n",
       " 'CarBrand_buick',\n",
       " 'CarBrand_chevrolet',\n",
       " 'CarBrand_dodge',\n",
       " 'CarBrand_jaguar',\n",
       " 'CarBrand_mitsubishi',\n",
       " 'CarBrand_nissan',\n",
       " 'CarBrand_peugeot',\n",
       " 'CarBrand_plymouth',\n",
       " 'CarBrand_saab',\n",
       " 'CarBrand_toyota',\n",
       " 'wheelbase',\n",
       " 'carlength',\n",
       " 'carwidth',\n",
       " 'carheight',\n",
       " 'curbweight',\n",
       " 'enginesize',\n",
       " 'boreratio',\n",
       " 'compressionratio',\n",
       " 'peakrpm',\n",
       " 'citympg']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns=['price', 'car_ID'])\n",
    "y = df['price']\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split # import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "import pandas as pd \n",
    "import statsmodels.api as sm \n",
    "\n",
    "def backward_regression(X, y,\n",
    "                           initial_list = [], \n",
    "                           threshold_in = 0.01, \n",
    "                           threshold_out = 0.05, \n",
    "                           verbose=True):\n",
    "    included = list(X.columns)\n",
    "    while True:\n",
    "        changed=False\n",
    "        model = sm.OLS(y, sm.add_constant(pd.DataFrame(X[included]))).fit()\n",
    "        pvalues = model.pvalues.iloc[1:]\n",
    "        worst_pval = pvalues.max() \n",
    "        if worst_pval > threshold_out:\n",
    "            changed = True\n",
    "            worst_feature = pvalues.idxmax()\n",
    "            included.remove(worst_feature)\n",
    "        if not changed:\n",
    "            break\n",
    "    return included\n",
    "\n",
    "backward_regression(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data splitting__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[backward_regression(X_train, y_train)]\n",
    "y = df['price']\n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature scaling__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model__1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model \n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluation__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 2038.95\n",
      "Residual sum of squares (MSE): 11148443.23\n",
      "R2-score: 0.86\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "yhat = regr.predict(X_test)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(yhat - y_test)))\n",
    "\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((yhat - y_test) ** 2)) \n",
    "\n",
    "print(\"R2-score: %.2f\" % r2_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6839.81276694, 18579.59050888, 13488.48449049,  5273.42764108,\n",
       "       10768.07195548, 11209.99937239,  6600.31763338,  5379.37359582,\n",
       "       16523.45224807,  8114.30574944, 21468.57885809, 23716.8328419 ,\n",
       "       13323.14986447, 13222.25039302, 10186.33679635, 10568.70548055,\n",
       "       11184.66059061, 18945.03586246,  8813.2897622 ,  7024.10899449,\n",
       "       10462.74390374, 13266.28731155, 11645.03018537, 11385.35317094,\n",
       "       17531.30292703,  6161.98498429,  7213.34918535, 15056.83417321,\n",
       "        7237.61204655,  6521.17114797,  9283.54699307, 12800.45399216,\n",
       "       14554.32049577, 10378.51785682,  6454.40910646, 29132.88963766,\n",
       "       14755.88751238, 15988.05915225,  5182.78790766, 35646.77095752,\n",
       "        9154.71887204])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model__2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X is a column with a correlation value with a column price of >= abs(0.5) Pearson's results\n",
    "X = df[['cylindernumber', 'curbweight', 'carwidth', 'enginesize','horsepower','highwaympg', \n",
    "        'drivewheel_fwd', 'drivewheel_rwd', 'fuelsystem_mpfi', 'fuelsystem_2bbl', 'CarBrand_buick',\n",
    "        'wheelbase', 'carlength', 'boreratio', 'citympg']] \n",
    "y = df['price']\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 0)\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "from sklearn import linear_model \n",
    "regr2 = linear_model.LinearRegression() \n",
    "regr2.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluasi__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 2742.74\n",
      "Residual sum of squares (MSE): 14697108.76\n",
      "R2-score: 0.81\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "yhat2 = regr2.predict(X_test)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(yhat2 - y_test))) \n",
    "\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((yhat2 - y_test) ** 2)) \n",
    "\n",
    "print(\"R2-score: %.2f\" % r2_score(y_test, yhat2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model__3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X is a column with a correlation value with a column price of >= abs(0.7) Pearson's results\n",
    "X = df[['cylindernumber', 'curbweight', 'carwidth', 'enginesize','horsepower','highwaympg']] \n",
    "y = df['price']\n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 0)\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "X_train = sc.fit_transform(X_train) \n",
    "X_test = sc.transform(X_test) \n",
    "\n",
    "from sklearn import linear_model \n",
    "regr3 = linear_model.LinearRegression() \n",
    "regr3.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### evaluation__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error: 2905.28\n",
      "Residual sum of squares (MSE): 16163076.45\n",
      "R2-score: 0.79\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "yhat3 = regr3.predict(X_test)\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(yhat3 - y_test))) \n",
    "\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((yhat3 - y_test) ** 2))\n",
    "\n",
    "print(\"R2-score: %.2f\" % r2_score(y_test, yhat3))"
   ]
  }
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