used_car_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateCrawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</th>\n",
       "      <th>vehicleType</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>brand</th>\n",
       "      <th>notRepairedDamage</th>\n",
       "      <th>dateCreated</th>\n",
       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>lastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:17</td>\n",
       "      <td>Golf_3_1.6</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>480</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:45</td>\n",
       "      <td>A5_Sportback_2.7_Tdi</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>18300</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manuell</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-04-07 01:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:21</td>\n",
       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>9800</td>\n",
       "      <td>test</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatik</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>diesel</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:04</td>\n",
       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>1500</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2001</td>\n",
       "      <td>manuell</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-17 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:20</td>\n",
       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>3600</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2008</td>\n",
       "      <td>manuell</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>diesel</td>\n",
       "      <td>skoda</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-04-06 10:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dateCrawled                            name  seller offerType  \\\n",
       "0  2016-03-24 11:52:17                      Golf_3_1.6  privat   Angebot   \n",
       "1  2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  privat   Angebot   \n",
       "2  2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   \n",
       "3  2016-03-17 16:54:04              GOLF_4_1_4__3TÜRER  privat   Angebot   \n",
       "4  2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
       "\n",
       "   price abtest vehicleType  yearOfRegistration    gearbox  powerPS  model  \\\n",
       "0    480   test         NaN                1993    manuell        0   golf   \n",
       "1  18300   test       coupe                2011    manuell      190    NaN   \n",
       "2   9800   test         suv                2004  automatik      163  grand   \n",
       "3   1500   test  kleinwagen                2001    manuell       75   golf   \n",
       "4   3600   test  kleinwagen                2008    manuell       69  fabia   \n",
       "\n",
       "   kilometer  monthOfRegistration fuelType       brand notRepairedDamage  \\\n",
       "0     150000                    0   benzin  volkswagen               NaN   \n",
       "1     125000                    5   diesel        audi                ja   \n",
       "2     125000                    8   diesel        jeep               NaN   \n",
       "3     150000                    6   benzin  volkswagen              nein   \n",
       "4      90000                    7   diesel       skoda              nein   \n",
       "\n",
       "           dateCreated  nrOfPictures  postalCode             lastSeen  \n",
       "0  2016-03-24 00:00:00             0       70435  2016-04-07 03:16:57  \n",
       "1  2016-03-24 00:00:00             0       66954  2016-04-07 01:46:50  \n",
       "2  2016-03-14 00:00:00             0       90480  2016-04-05 12:47:46  \n",
       "3  2016-03-17 00:00:00             0       91074  2016-03-17 17:40:17  \n",
       "4  2016-03-31 00:00:00             0       60437  2016-04-06 10:17:21  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('autos.csv', sep=',', header=0, encoding='cp1252')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</th>\n",
       "      <th>vehicleType</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>brand</th>\n",
       "      <th>notRepairedDamage</th>\n",
       "      <th>dateCreated</th>\n",
       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>lastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Golf_3_1.6</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>480</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A5_Sportback_2.7_Tdi</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>18300</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manuell</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-04-07 01:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>9800</td>\n",
       "      <td>test</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatik</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>diesel</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>1500</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2001</td>\n",
       "      <td>manuell</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-17 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>3600</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2008</td>\n",
       "      <td>manuell</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>diesel</td>\n",
       "      <td>skoda</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-04-06 10:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             name  seller offerType  price abtest vehicleType  \\\n",
       "0                      Golf_3_1.6  privat   Angebot    480   test         NaN   \n",
       "1            A5_Sportback_2.7_Tdi  privat   Angebot  18300   test       coupe   \n",
       "2  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   9800   test         suv   \n",
       "3              GOLF_4_1_4__3TÜRER  privat   Angebot   1500   test  kleinwagen   \n",
       "4  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   3600   test  kleinwagen   \n",
       "\n",
       "   yearOfRegistration    gearbox  powerPS  model  kilometer  \\\n",
       "0                1993    manuell        0   golf     150000   \n",
       "1                2011    manuell      190    NaN     125000   \n",
       "2                2004  automatik      163  grand     125000   \n",
       "3                2001    manuell       75   golf     150000   \n",
       "4                2008    manuell       69  fabia      90000   \n",
       "\n",
       "   monthOfRegistration fuelType       brand notRepairedDamage  \\\n",
       "0                    0   benzin  volkswagen               NaN   \n",
       "1                    5   diesel        audi                ja   \n",
       "2                    8   diesel        jeep               NaN   \n",
       "3                    6   benzin  volkswagen              nein   \n",
       "4                    7   diesel       skoda              nein   \n",
       "\n",
       "           dateCreated  nrOfPictures  postalCode             lastSeen  \n",
       "0  2016-03-24 00:00:00             0       70435  2016-04-07 03:16:57  \n",
       "1  2016-03-24 00:00:00             0       66954  2016-04-07 01:46:50  \n",
       "2  2016-03-14 00:00:00             0       90480  2016-04-05 12:47:46  \n",
       "3  2016-03-17 00:00:00             0       91074  2016-03-17 17:40:17  \n",
       "4  2016-03-31 00:00:00             0       60437  2016-04-06 10:17:21  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop('dateCrawled',axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 371528 entries, 0 to 371527\n",
      "Data columns (total 19 columns):\n",
      "name                   371528 non-null object\n",
      "seller                 371528 non-null object\n",
      "offerType              371528 non-null object\n",
      "price                  371528 non-null int64\n",
      "abtest                 371528 non-null object\n",
      "vehicleType            333659 non-null object\n",
      "yearOfRegistration     371528 non-null int64\n",
      "gearbox                351319 non-null object\n",
      "powerPS                371528 non-null int64\n",
      "model                  351044 non-null object\n",
      "kilometer              371528 non-null int64\n",
      "monthOfRegistration    371528 non-null int64\n",
      "fuelType               338142 non-null object\n",
      "brand                  371528 non-null object\n",
      "notRepairedDamage      299468 non-null object\n",
      "dateCreated            371528 non-null object\n",
      "nrOfPictures           371528 non-null int64\n",
      "postalCode             371528 non-null int64\n",
      "lastSeen               371528 non-null object\n",
      "dtypes: int64(7), object(12)\n",
      "memory usage: 53.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 371528 entries, 0 to 371527\n",
      "Data columns (total 13 columns):\n",
      "seller                 371528 non-null object\n",
      "offerType              371528 non-null object\n",
      "price                  371528 non-null int64\n",
      "vehicleType            333659 non-null object\n",
      "yearOfRegistration     371528 non-null int64\n",
      "gearbox                351319 non-null object\n",
      "powerPS                371528 non-null int64\n",
      "model                  351044 non-null object\n",
      "kilometer              371528 non-null int64\n",
      "monthOfRegistration    371528 non-null int64\n",
      "fuelType               338142 non-null object\n",
      "brand                  371528 non-null object\n",
      "notRepairedDamage      299468 non-null object\n",
      "dtypes: int64(5), object(8)\n",
      "memory usage: 36.8+ MB\n"
     ]
    }
   ],
   "source": [
    "data=data.drop(['dateCreated','lastSeen','nrOfPictures','abtest','postalCode','name'],axis=1)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.715280e+05</td>\n",
       "      <td>371528.000000</td>\n",
       "      <td>371528.000000</td>\n",
       "      <td>371528.000000</td>\n",
       "      <td>371528.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.729514e+04</td>\n",
       "      <td>2004.577997</td>\n",
       "      <td>115.549477</td>\n",
       "      <td>125618.688228</td>\n",
       "      <td>5.734445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.587954e+06</td>\n",
       "      <td>92.866598</td>\n",
       "      <td>192.139578</td>\n",
       "      <td>40112.337051</td>\n",
       "      <td>3.712412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.150000e+03</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.950000e+03</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.200000e+03</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.147484e+09</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price  yearOfRegistration        powerPS      kilometer  \\\n",
       "count  3.715280e+05       371528.000000  371528.000000  371528.000000   \n",
       "mean   1.729514e+04         2004.577997     115.549477  125618.688228   \n",
       "std    3.587954e+06           92.866598     192.139578   40112.337051   \n",
       "min    0.000000e+00         1000.000000       0.000000    5000.000000   \n",
       "25%    1.150000e+03         1999.000000      70.000000  125000.000000   \n",
       "50%    2.950000e+03         2003.000000     105.000000  150000.000000   \n",
       "75%    7.200000e+03         2008.000000     150.000000  150000.000000   \n",
       "max    2.147484e+09         9999.000000   20000.000000  150000.000000   \n",
       "\n",
       "       monthOfRegistration  \n",
       "count        371528.000000  \n",
       "mean              5.734445  \n",
       "std               3.712412  \n",
       "min               0.000000  \n",
       "25%               3.000000  \n",
       "50%               6.000000  \n",
       "75%               9.000000  \n",
       "max              12.000000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['privat' 'gewerblich']\n",
      "['Angebot' 'Gesuch']\n"
     ]
    }
   ],
   "source": [
    "print(data.seller.unique())\n",
    "print(data.offerType.unique())\n",
    "y=data.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "data['notRepairedDamage'].fillna(value='not-declared', inplace=True)\n",
    "data['fuelType'].fillna(value='not-declared', inplace=True)\n",
    "data['gearbox'].fillna(value='not-declared', inplace=True)\n",
    "data['vehicleType'].fillna(value='not-declared', inplace=True)\n",
    "data['model'].fillna(value='not-declared', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller                 0\n",
       "offerType              0\n",
       "price                  0\n",
       "vehicleType            0\n",
       "yearOfRegistration     0\n",
       "gearbox                0\n",
       "powerPS                0\n",
       "model                  0\n",
       "kilometer              0\n",
       "monthOfRegistration    0\n",
       "fuelType               0\n",
       "brand                  0\n",
       "notRepairedDamage      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['price', 'yearOfRegistration', 'powerPS', 'kilometer', 'monthOfRegistration']\n"
     ]
    }
   ],
   "source": [
    "num_list=[key for key in dict(data.dtypes) if dict(data.dtypes)[key] in ['float64', 'int64']]\n",
    "print(num_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_num=data[num_list]\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_num_transformed=StandardScaler().fit_transform(data_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(371528, 5)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_num_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.124674</td>\n",
       "      <td>-0.601384</td>\n",
       "      <td>0.607827</td>\n",
       "      <td>-1.544670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.069153</td>\n",
       "      <td>0.387482</td>\n",
       "      <td>-0.015424</td>\n",
       "      <td>-0.197835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.006224</td>\n",
       "      <td>0.246959</td>\n",
       "      <td>-0.015424</td>\n",
       "      <td>0.610266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.038528</td>\n",
       "      <td>-0.211042</td>\n",
       "      <td>0.607827</td>\n",
       "      <td>0.071532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.036849</td>\n",
       "      <td>-0.242269</td>\n",
       "      <td>-0.887975</td>\n",
       "      <td>0.340899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearOfRegistration   powerPS  kilometer  monthOfRegistration\n",
       "0           -0.124674 -0.601384   0.607827            -1.544670\n",
       "1            0.069153  0.387482  -0.015424            -0.197835\n",
       "2           -0.006224  0.246959  -0.015424             0.610266\n",
       "3           -0.038528 -0.211042   0.607827             0.071532\n",
       "4            0.036849 -0.242269  -0.887975             0.340899"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_num_trans_df=pd.DataFrame(data=data_num_transformed[0:,0:],columns=num_list)\n",
    "data_num_trans_df=data_num_trans_df.drop('price',axis=1)\n",
    "data_num_trans_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c499c50>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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88fTr14/Fixez5557custt7TyO6BCrFxZz80TxjP5nnuprq7mnKGnc/Ahh7L1Nts0bjPl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x30295860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(12, 10))\n",
    "plt.title('Pearson Correlation of Used Car Features')\n",
    "# Draw the heatmap using seaborn\n",
    "\n",
    "sns.heatmap(check_df.astype(float).corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gearbox\n",
      "manuell         274214\n",
      "automatik        77105\n",
      "not-declared     20209\n",
      "Name: gearbox, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d19b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\n",
      "golf            30070\n",
      "andere          26400\n",
      "3er             20567\n",
      "not-declared    20484\n",
      "polo            13092\n",
      "Name: model, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x3016d128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brand\n",
      "volkswagen       79640\n",
      "bmw              40274\n",
      "opel             40136\n",
      "mercedes_benz    35309\n",
      "audi             32873\n",
      "Name: brand, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28f094a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vehicleType\n",
      "limousine       95894\n",
      "kleinwagen      80023\n",
      "kombi           67564\n",
      "not-declared    37869\n",
      "bus             30201\n",
      "Name: vehicleType, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x28f0fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fuelType\n",
      "benzin          223857\n",
      "diesel          107746\n",
      "not-declared     33386\n",
      "lpg               5378\n",
      "cng                571\n",
      "Name: fuelType, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x290af0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notRepairedDamage\n",
      "nein            263182\n",
      "not-declared     72060\n",
      "ja               36286\n",
      "Name: notRepairedDamage, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z0p9mjvtdSacfU3GHjvdNSfNbNd5YIelTki7tdB3HQzdeHHpfioiXgJc6XUeXuw54CPhZ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29002630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['gearbox', 'model', 'brand', 'vehicleType', 'fuelType', 'notRepairedDamage']\n",
    "\n",
    "for i, c in enumerate(categories):\n",
    "    v = data[c].unique()\n",
    "    \n",
    "    g = data.groupby(by=c)[c].count().sort_values(ascending=False)\n",
    "    r = range(min(len(v), 5))\n",
    "\n",
    "    print( g.head())\n",
    "    plt.figure(figsize=(5,3))\n",
    "    plt.bar(r, g.head()) \n",
    "    #plt.xticks(r, v)\n",
    "    plt.xticks(r, g.index)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['seller', 'offerType', 'vehicleType', 'gearbox', 'model', 'fuelType', 'brand', 'notRepairedDamage']\n"
     ]
    }
   ],
   "source": [
    "obj_list=[key for key in dict(data.dtypes) if dict(data.dtypes)[key] in ['object']]\n",
    "print(obj_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_obj=data[obj_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_obj_final=pd.get_dummies(data_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seller_gewerblich</th>\n",
       "      <th>seller_privat</th>\n",
       "      <th>offerType_Angebot</th>\n",
       "      <th>offerType_Gesuch</th>\n",
       "      <th>vehicleType_andere</th>\n",
       "      <th>vehicleType_bus</th>\n",
       "      <th>vehicleType_cabrio</th>\n",
       "      <th>vehicleType_coupe</th>\n",
       "      <th>vehicleType_kleinwagen</th>\n",
       "      <th>vehicleType_kombi</th>\n",
       "      <th>...</th>\n",
       "      <th>brand_sonstige_autos</th>\n",
       "      <th>brand_subaru</th>\n",
       "      <th>brand_suzuki</th>\n",
       "      <th>brand_toyota</th>\n",
       "      <th>brand_trabant</th>\n",
       "      <th>brand_volkswagen</th>\n",
       "      <th>brand_volvo</th>\n",
       "      <th>notRepairedDamage_ja</th>\n",
       "      <th>notRepairedDamage_nein</th>\n",
       "      <th>notRepairedDamage_not-declared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 319 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   seller_gewerblich  seller_privat  offerType_Angebot  offerType_Gesuch  \\\n",
       "0                  0              1                  1                 0   \n",
       "1                  0              1                  1                 0   \n",
       "2                  0              1                  1                 0   \n",
       "3                  0              1                  1                 0   \n",
       "4                  0              1                  1                 0   \n",
       "\n",
       "   vehicleType_andere  vehicleType_bus  vehicleType_cabrio  vehicleType_coupe  \\\n",
       "0                   0                0                   0                  0   \n",
       "1                   0                0                   0                  1   \n",
       "2                   0                0                   0                  0   \n",
       "3                   0                0                   0                  0   \n",
       "4                   0                0                   0                  0   \n",
       "\n",
       "   vehicleType_kleinwagen  vehicleType_kombi               ...                \\\n",
       "0                       0                  0               ...                 \n",
       "1                       0                  0               ...                 \n",
       "2                       0                  0               ...                 \n",
       "3                       1                  0               ...                 \n",
       "4                       1                  0               ...                 \n",
       "\n",
       "   brand_sonstige_autos  brand_subaru  brand_suzuki  brand_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",
       "   brand_trabant  brand_volkswagen  brand_volvo  notRepairedDamage_ja  \\\n",
       "0              0                 1            0                     0   \n",
       "1              0                 0            0                     1   \n",
       "2              0                 0            0                     0   \n",
       "3              0                 1            0                     0   \n",
       "4              0                 0            0                     0   \n",
       "\n",
       "   notRepairedDamage_nein  notRepairedDamage_not-declared  \n",
       "0                       0                               1  \n",
       "1                       0                               0  \n",
       "2                       0                               1  \n",
       "3                       1                               0  \n",
       "4                       1                               0  \n",
       "\n",
       "[5 rows x 319 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_obj_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_final=pd.concat([data_num_trans_df,data_obj_final],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(371528, 323)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X_final,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(260069,)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape\n",
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : -12.3561794922\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "clf=RandomForestRegressor(random_state=0)\n",
    "clf.fit(X_train,Y_train)\n",
    "pred=clf.predict(X_test)\n",
    "print('Accuracy :', r2_score(Y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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