mechine learning on used car prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
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       "    <tr>\n",
       "      <td>371523</td>\n",
       "      <td>2016-03-14 17:48:27</td>\n",
       "      <td>Suche_t4___vito_ab_6_sitze</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>2200</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>39576</td>\n",
       "      <td>2016-04-06 00:46:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371524</td>\n",
       "      <td>2016-03-05 19:56:21</td>\n",
       "      <td>Smart_smart_leistungssteigerung_100ps</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>1199</td>\n",
       "      <td>test</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2000</td>\n",
       "      <td>automatik</td>\n",
       "      <td>101</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>125000</td>\n",
       "      <td>3</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-05 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>26135</td>\n",
       "      <td>2016-03-11 18:17:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371525</td>\n",
       "      <td>2016-03-19 18:57:12</td>\n",
       "      <td>Volkswagen_Multivan_T4_TDI_7DC_UY2</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>9200</td>\n",
       "      <td>test</td>\n",
       "      <td>bus</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>102</td>\n",
       "      <td>transporter</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-19 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>87439</td>\n",
       "      <td>2016-04-07 07:15:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371526</td>\n",
       "      <td>2016-03-20 19:41:08</td>\n",
       "      <td>VW_Golf_Kombi_1_9l_TDI</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>3400</td>\n",
       "      <td>test</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2002</td>\n",
       "      <td>manuell</td>\n",
       "      <td>100</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-20 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>40764</td>\n",
       "      <td>2016-03-24 12:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371527</td>\n",
       "      <td>2016-03-07 19:39:19</td>\n",
       "      <td>BMW_M135i_vollausgestattet_NP_52.720____Euro</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>28990</td>\n",
       "      <td>control</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2013</td>\n",
       "      <td>manuell</td>\n",
       "      <td>320</td>\n",
       "      <td>m_reihe</td>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-07 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>73326</td>\n",
       "      <td>2016-03-22 03:17:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>371528 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                dateCrawled                                          name  \\\n",
       "0       2016-03-24 11:52:17                                    Golf_3_1.6   \n",
       "1       2016-03-24 10:58:45                          A5_Sportback_2.7_Tdi   \n",
       "2       2016-03-14 12:52:21                Jeep_Grand_Cherokee_\"Overland\"   \n",
       "3       2016-03-17 16:54:04                            GOLF_4_1_4__3TÜRER   \n",
       "4       2016-03-31 17:25:20                Skoda_Fabia_1.4_TDI_PD_Classic   \n",
       "...                     ...                                           ...   \n",
       "371523  2016-03-14 17:48:27                    Suche_t4___vito_ab_6_sitze   \n",
       "371524  2016-03-05 19:56:21         Smart_smart_leistungssteigerung_100ps   \n",
       "371525  2016-03-19 18:57:12            Volkswagen_Multivan_T4_TDI_7DC_UY2   \n",
       "371526  2016-03-20 19:41:08                        VW_Golf_Kombi_1_9l_TDI   \n",
       "371527  2016-03-07 19:39:19  BMW_M135i_vollausgestattet_NP_52.720____Euro   \n",
       "\n",
       "        seller offerType  price   abtest vehicleType  yearOfRegistration  \\\n",
       "0       privat   Angebot    480     test         NaN                1993   \n",
       "1       privat   Angebot  18300     test       coupe                2011   \n",
       "2       privat   Angebot   9800     test         suv                2004   \n",
       "3       privat   Angebot   1500     test  kleinwagen                2001   \n",
       "4       privat   Angebot   3600     test  kleinwagen                2008   \n",
       "...        ...       ...    ...      ...         ...                 ...   \n",
       "371523  privat   Angebot   2200     test         NaN                2005   \n",
       "371524  privat   Angebot   1199     test      cabrio                2000   \n",
       "371525  privat   Angebot   9200     test         bus                1996   \n",
       "371526  privat   Angebot   3400     test       kombi                2002   \n",
       "371527  privat   Angebot  28990  control   limousine                2013   \n",
       "\n",
       "          gearbox  powerPS        model  kilometer  monthOfRegistration  \\\n",
       "0         manuell        0         golf     150000                    0   \n",
       "1         manuell      190          NaN     125000                    5   \n",
       "2       automatik      163        grand     125000                    8   \n",
       "3         manuell       75         golf     150000                    6   \n",
       "4         manuell       69        fabia      90000                    7   \n",
       "...           ...      ...          ...        ...                  ...   \n",
       "371523        NaN        0          NaN      20000                    1   \n",
       "371524  automatik      101       fortwo     125000                    3   \n",
       "371525    manuell      102  transporter     150000                    3   \n",
       "371526    manuell      100         golf     150000                    6   \n",
       "371527    manuell      320      m_reihe      50000                    8   \n",
       "\n",
       "       fuelType           brand notRepairedDamage          dateCreated  \\\n",
       "0        benzin      volkswagen               NaN  2016-03-24 00:00:00   \n",
       "1        diesel            audi                ja  2016-03-24 00:00:00   \n",
       "2        diesel            jeep               NaN  2016-03-14 00:00:00   \n",
       "3        benzin      volkswagen              nein  2016-03-17 00:00:00   \n",
       "4        diesel           skoda              nein  2016-03-31 00:00:00   \n",
       "...         ...             ...               ...                  ...   \n",
       "371523      NaN  sonstige_autos               NaN  2016-03-14 00:00:00   \n",
       "371524   benzin           smart              nein  2016-03-05 00:00:00   \n",
       "371525   diesel      volkswagen              nein  2016-03-19 00:00:00   \n",
       "371526   diesel      volkswagen               NaN  2016-03-20 00:00:00   \n",
       "371527   benzin             bmw              nein  2016-03-07 00:00:00   \n",
       "\n",
       "        nrOfPictures  postalCode             lastSeen  \n",
       "0                  0       70435  2016-04-07 03:16:57  \n",
       "1                  0       66954  2016-04-07 01:46:50  \n",
       "2                  0       90480  2016-04-05 12:47:46  \n",
       "3                  0       91074  2016-03-17 17:40:17  \n",
       "4                  0       60437  2016-04-06 10:17:21  \n",
       "...              ...         ...                  ...  \n",
       "371523             0       39576  2016-04-06 00:46:52  \n",
       "371524             0       26135  2016-03-11 18:17:12  \n",
       "371525             0       87439  2016-04-07 07:15:26  \n",
       "371526             0       40764  2016-03-24 12:45:21  \n",
       "371527             0       73326  2016-03-22 03:17:10  \n",
       "\n",
       "[371528 rows x 20 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv(r'autos12.csv',encoding = \"ISO-8859-1\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dateCrawled            False\n",
       "name                   False\n",
       "seller                 False\n",
       "offerType              False\n",
       "price                  False\n",
       "abtest                 False\n",
       "vehicleType             True\n",
       "yearOfRegistration     False\n",
       "gearbox                 True\n",
       "powerPS                False\n",
       "model                   True\n",
       "kilometer              False\n",
       "monthOfRegistration    False\n",
       "fuelType                True\n",
       "brand                  False\n",
       "notRepairedDamage       True\n",
       "dateCreated            False\n",
       "nrOfPictures           False\n",
       "postalCode             False\n",
       "lastSeen               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dateCrawled                0\n",
       "name                       0\n",
       "seller                     0\n",
       "offerType                  0\n",
       "price                      0\n",
       "abtest                     0\n",
       "vehicleType            37869\n",
       "yearOfRegistration         0\n",
       "gearbox                20209\n",
       "powerPS                    0\n",
       "model                  20484\n",
       "kilometer                  0\n",
       "monthOfRegistration        0\n",
       "fuelType               33386\n",
       "brand                      0\n",
       "notRepairedDamage      72060\n",
       "dateCreated                0\n",
       "nrOfPictures               0\n",
       "postalCode                 0\n",
       "lastSeen                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\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",
       "      <td>371528.0</td>\n",
       "      <td>371528.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.729514e+04</td>\n",
       "      <td>2004.577997</td>\n",
       "      <td>115.549477</td>\n",
       "      <td>125618.688228</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>50820.66764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\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",
       "      <td>0.0</td>\n",
       "      <td>25799.08247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\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",
       "      <td>0.0</td>\n",
       "      <td>1067.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\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",
       "      <td>0.0</td>\n",
       "      <td>30459.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\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",
       "      <td>0.0</td>\n",
       "      <td>49610.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\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",
       "      <td>0.0</td>\n",
       "      <td>71546.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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",
       "      <td>0.0</td>\n",
       "      <td>99998.00000</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  nrOfPictures    postalCode  \n",
       "count        371528.000000      371528.0  371528.00000  \n",
       "mean              5.734445           0.0   50820.66764  \n",
       "std               3.712412           0.0   25799.08247  \n",
       "min               0.000000           0.0    1067.00000  \n",
       "25%               3.000000           0.0   30459.00000  \n",
       "50%               6.000000           0.0   49610.00000  \n",
       "75%               9.000000           0.0   71546.00000  \n",
       "max              12.000000           0.0   99998.00000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['name','seller','abtest','offerType', 'dateCrawled', 'nrOfPictures', 'lastSeen', 'postalCode', 'dateCreated'], axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                      0\n",
       "vehicleType            37869\n",
       "yearOfRegistration         0\n",
       "gearbox                20209\n",
       "powerPS                    0\n",
       "model                  20484\n",
       "kilometer                  0\n",
       "monthOfRegistration        0\n",
       "fuelType               33386\n",
       "brand                      0\n",
       "notRepairedDamage      72060\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(371528, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"notRepairedDamage\"].fillna(\"nein\",inplace = True)\n",
    "df[\"notRepairedDamage\"].isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"fuelType\"].fillna(\"benzin\",inplace = True)\n",
    "df[\"fuelType\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                      0\n",
       "vehicleType                0\n",
       "yearOfRegistration         0\n",
       "gearbox                    0\n",
       "powerPS                    0\n",
       "model                  20484\n",
       "kilometer                  0\n",
       "monthOfRegistration        0\n",
       "fuelType                   0\n",
       "brand                      0\n",
       "notRepairedDamage          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['vehicleType'].fillna(df['vehicleType'].mode().iloc[0],inplace=True)\n",
    "df['gearbox'].fillna(df['gearbox'].mode().iloc[0],inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"model\"].fillna(\"golf\",inplace =True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>count</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                   int64\n",
       "vehicleType            object\n",
       "yearOfRegistration      int64\n",
       "gearbox                object\n",
       "powerPS                 int64\n",
       "model                  object\n",
       "kilometer               int64\n",
       "monthOfRegistration     int64\n",
       "fuelType               object\n",
       "brand                  object\n",
       "notRepairedDamage      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>price</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>limousine</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>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manuell</td>\n",
       "      <td>190</td>\n",
       "      <td>golf</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9800</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>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1500</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3600</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371523</td>\n",
       "      <td>2200</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2005</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>benzin</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371524</td>\n",
       "      <td>1199</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2000</td>\n",
       "      <td>automatik</td>\n",
       "      <td>101</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>125000</td>\n",
       "      <td>3</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371525</td>\n",
       "      <td>9200</td>\n",
       "      <td>bus</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>102</td>\n",
       "      <td>transporter</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371526</td>\n",
       "      <td>3400</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2002</td>\n",
       "      <td>manuell</td>\n",
       "      <td>100</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371527</td>\n",
       "      <td>28990</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2013</td>\n",
       "      <td>manuell</td>\n",
       "      <td>320</td>\n",
       "      <td>m_reihe</td>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>371528 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price vehicleType  yearOfRegistration    gearbox  powerPS  \\\n",
       "0         480   limousine                1993    manuell        0   \n",
       "1       18300       coupe                2011    manuell      190   \n",
       "2        9800         suv                2004  automatik      163   \n",
       "3        1500  kleinwagen                2001    manuell       75   \n",
       "4        3600  kleinwagen                2008    manuell       69   \n",
       "...       ...         ...                 ...        ...      ...   \n",
       "371523   2200   limousine                2005    manuell        0   \n",
       "371524   1199      cabrio                2000  automatik      101   \n",
       "371525   9200         bus                1996    manuell      102   \n",
       "371526   3400       kombi                2002    manuell      100   \n",
       "371527  28990   limousine                2013    manuell      320   \n",
       "\n",
       "              model  kilometer  monthOfRegistration fuelType           brand  \\\n",
       "0              golf     150000                    0   benzin      volkswagen   \n",
       "1              golf     125000                    5   diesel            audi   \n",
       "2             grand     125000                    8   diesel            jeep   \n",
       "3              golf     150000                    6   benzin      volkswagen   \n",
       "4             fabia      90000                    7   diesel           skoda   \n",
       "...             ...        ...                  ...      ...             ...   \n",
       "371523         golf      20000                    1   benzin  sonstige_autos   \n",
       "371524       fortwo     125000                    3   benzin           smart   \n",
       "371525  transporter     150000                    3   diesel      volkswagen   \n",
       "371526         golf     150000                    6   diesel      volkswagen   \n",
       "371527      m_reihe      50000                    8   benzin             bmw   \n",
       "\n",
       "       notRepairedDamage  \n",
       "0                   nein  \n",
       "1                     ja  \n",
       "2                   nein  \n",
       "3                   nein  \n",
       "4                   nein  \n",
       "...                  ...  \n",
       "371523              nein  \n",
       "371524              nein  \n",
       "371525              nein  \n",
       "371526              nein  \n",
       "371527              nein  \n",
       "\n",
       "[371528 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['kilometer'],df['price'],\"r\")\n",
    "plt.xlabel('kilometer')\n",
    "plt.ylabel('price')\n",
    "plt.title('kilometer vs price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'kilometer vs price')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['kilometer'],df['price'])\n",
    "plt.xlabel('kilometer')\n",
    "plt.ylabel('price')\n",
    "plt.title('kilometer vs price')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'vehicletype vs price')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['vehicleType'],df['price'])\n",
    "plt.xlabel('vehicleType')\n",
    "plt.ylabel('price')\n",
    "plt.title('vehicletype vs price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'yearOfRegistration vs price')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['yearOfRegistration'],df['price'])\n",
    "plt.xlabel('yearOfRegistration')\n",
    "plt.ylabel('price')\n",
    "plt.title('yearOfRegistration vs price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'gearbox vs price')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['gearbox'],df['price'])\n",
    "plt.xlabel('gearbox')\n",
    "plt.ylabel('price')\n",
    "plt.title('gearbox vs price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'powerPS vs price')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['powerPS'],df['price'])\n",
    "plt.xlabel('powerPS')\n",
    "plt.ylabel('price')\n",
    "plt.title('powerPS vs price')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plt.scatter(df['model'],df['price'])\n",
    "plt.xlabel('model')\n",
    "plt.ylabel('price')\n",
    "plt.title('model vs price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'fuelType vs price')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['fuelType'],df['price'])\n",
    "plt.xlabel('fuelType')\n",
    "plt.ylabel('price')\n",
    "plt.title('fuelType vs price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'brand vs price')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['brand'],df['price'])\n",
    "plt.xlabel('brand')\n",
    "plt.ylabel('price')\n",
    "plt.title('brand vs price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'notRepairedDamage vs price')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['notRepairedDamage'],df['price'])\n",
    "plt.xlabel('notRepairedDamage')\n",
    "plt.ylabel('price')\n",
    "plt.title('notRepairedDamage vs price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'monthOfRegistration vs price')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['monthOfRegistration'],df['price'])\n",
    "plt.xlabel('monthOfRegistration')\n",
    "plt.ylabel('price')\n",
    "plt.title('monthOfRegistration vs price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x158cbdccf08>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x158c5654a88>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['yearOfRegistration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x158c0adaa88>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['powerPS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x158c1f427c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['kilometer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x158c1858c88>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['monthOfRegistration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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",
       "      <td>price</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>0.006850</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>-0.002826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>yearOfRegistration</td>\n",
       "      <td>-0.000182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>-0.055319</td>\n",
       "      <td>-0.011607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>powerPS</td>\n",
       "      <td>0.006850</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010738</td>\n",
       "      <td>0.044358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>kilometer</td>\n",
       "      <td>-0.000635</td>\n",
       "      <td>-0.055319</td>\n",
       "      <td>-0.010738</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>monthOfRegistration</td>\n",
       "      <td>-0.002826</td>\n",
       "      <td>-0.011607</td>\n",
       "      <td>0.044358</td>\n",
       "      <td>0.001838</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        price  yearOfRegistration   powerPS  kilometer  \\\n",
       "price                1.000000           -0.000182  0.006850  -0.000635   \n",
       "yearOfRegistration  -0.000182            1.000000  0.000351  -0.055319   \n",
       "powerPS              0.006850            0.000351  1.000000  -0.010738   \n",
       "kilometer           -0.000635           -0.055319 -0.010738   1.000000   \n",
       "monthOfRegistration -0.002826           -0.011607  0.044358   0.001838   \n",
       "\n",
       "                     monthOfRegistration  \n",
       "price                          -0.002826  \n",
       "yearOfRegistration             -0.011607  \n",
       "powerPS                         0.044358  \n",
       "kilometer                       0.001838  \n",
       "monthOfRegistration             1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x158ca512288>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>price</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>limousine</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>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manuell</td>\n",
       "      <td>190</td>\n",
       "      <td>golf</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9800</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>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1500</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3600</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371523</td>\n",
       "      <td>2200</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2005</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>benzin</td>\n",
       "      <td>sonstige_autos</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371524</td>\n",
       "      <td>1199</td>\n",
       "      <td>cabrio</td>\n",
       "      <td>2000</td>\n",
       "      <td>automatik</td>\n",
       "      <td>101</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>125000</td>\n",
       "      <td>3</td>\n",
       "      <td>benzin</td>\n",
       "      <td>smart</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371525</td>\n",
       "      <td>9200</td>\n",
       "      <td>bus</td>\n",
       "      <td>1996</td>\n",
       "      <td>manuell</td>\n",
       "      <td>102</td>\n",
       "      <td>transporter</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371526</td>\n",
       "      <td>3400</td>\n",
       "      <td>kombi</td>\n",
       "      <td>2002</td>\n",
       "      <td>manuell</td>\n",
       "      <td>100</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>diesel</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371527</td>\n",
       "      <td>28990</td>\n",
       "      <td>limousine</td>\n",
       "      <td>2013</td>\n",
       "      <td>manuell</td>\n",
       "      <td>320</td>\n",
       "      <td>m_reihe</td>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>benzin</td>\n",
       "      <td>bmw</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>371528 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price vehicleType  yearOfRegistration    gearbox  powerPS  \\\n",
       "0         480   limousine                1993    manuell        0   \n",
       "1       18300       coupe                2011    manuell      190   \n",
       "2        9800         suv                2004  automatik      163   \n",
       "3        1500  kleinwagen                2001    manuell       75   \n",
       "4        3600  kleinwagen                2008    manuell       69   \n",
       "...       ...         ...                 ...        ...      ...   \n",
       "371523   2200   limousine                2005    manuell        0   \n",
       "371524   1199      cabrio                2000  automatik      101   \n",
       "371525   9200         bus                1996    manuell      102   \n",
       "371526   3400       kombi                2002    manuell      100   \n",
       "371527  28990   limousine                2013    manuell      320   \n",
       "\n",
       "              model  kilometer  monthOfRegistration fuelType           brand  \\\n",
       "0              golf     150000                    0   benzin      volkswagen   \n",
       "1              golf     125000                    5   diesel            audi   \n",
       "2             grand     125000                    8   diesel            jeep   \n",
       "3              golf     150000                    6   benzin      volkswagen   \n",
       "4             fabia      90000                    7   diesel           skoda   \n",
       "...             ...        ...                  ...      ...             ...   \n",
       "371523         golf      20000                    1   benzin  sonstige_autos   \n",
       "371524       fortwo     125000                    3   benzin           smart   \n",
       "371525  transporter     150000                    3   diesel      volkswagen   \n",
       "371526         golf     150000                    6   diesel      volkswagen   \n",
       "371527      m_reihe      50000                    8   benzin             bmw   \n",
       "\n",
       "       notRepairedDamage  \n",
       "0                   nein  \n",
       "1                     ja  \n",
       "2                   nein  \n",
       "3                   nein  \n",
       "4                   nein  \n",
       "...                  ...  \n",
       "371523              nein  \n",
       "371524              nein  \n",
       "371525              nein  \n",
       "371526              nein  \n",
       "371527              nein  \n",
       "\n",
       "[371528 rows x 11 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "data = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"vehicleType\"] =LabelEncoder().fit_transform(data[\"vehicleType\"])\n",
    "data[\"fuelType\"] =LabelEncoder().fit_transform(data[\"fuelType\"])\n",
    "data[\"gearbox\"] =LabelEncoder().fit_transform(data[\"gearbox\"])\n",
    "data[\"notRepairedDamage\"] =LabelEncoder().fit_transform(data[\"notRepairedDamage\"])\n",
    "data[\"brand\"] =LabelEncoder().fit_transform(data[\"brand\"])\n",
    "data[\"model\"] =LabelEncoder().fit_transform(data[\"model\"])\n",
    "#data[\"abtest\"] =LabelEncoder().fit_transform(data[\"abtest\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4134, 11)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.yearOfRegistration > 2017].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(289, 11)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.yearOfRegistration < 1950].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 11)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.price < 100].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(170, 11)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.price > 200000].shape"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#So only 289 registration are there before 1950 and 4000 registration after 2017 , so these are outliers and should be removed for better results\n",
    "#price starts from 0 and goes upto max 2.147484e+09 , but the price 0 is unrealistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[(data.yearOfRegistration < 2017)  & (data.yearOfRegistration > 1950)]\n",
    "data = data[(data.price > 100) & (data.price < 200000) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>price</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>6</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>18300</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>190</td>\n",
       "      <td>118</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9800</td>\n",
       "      <td>7</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>119</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1500</td>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>118</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3600</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>103</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371523</td>\n",
       "      <td>2200</td>\n",
       "      <td>6</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371524</td>\n",
       "      <td>1199</td>\n",
       "      <td>2</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>108</td>\n",
       "      <td>125000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371525</td>\n",
       "      <td>9200</td>\n",
       "      <td>1</td>\n",
       "      <td>1996</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>225</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371526</td>\n",
       "      <td>3400</td>\n",
       "      <td>5</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>118</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371527</td>\n",
       "      <td>28990</td>\n",
       "      <td>6</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>320</td>\n",
       "      <td>148</td>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342990 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  vehicleType  yearOfRegistration  gearbox  powerPS  model  \\\n",
       "0         480            6                1993        1        0    118   \n",
       "1       18300            3                2011        1      190    118   \n",
       "2        9800            7                2004        0      163    119   \n",
       "3        1500            4                2001        1       75    118   \n",
       "4        3600            4                2008        1       69    103   \n",
       "...       ...          ...                 ...      ...      ...    ...   \n",
       "371523   2200            6                2005        1        0    118   \n",
       "371524   1199            2                2000        0      101    108   \n",
       "371525   9200            1                1996        1      102    225   \n",
       "371526   3400            5                2002        1      100    118   \n",
       "371527  28990            6                2013        1      320    148   \n",
       "\n",
       "        kilometer  monthOfRegistration  fuelType  brand  notRepairedDamage  \n",
       "0          150000                    0         1     38                  1  \n",
       "1          125000                    5         3      1                  0  \n",
       "2          125000                    8         3     14                  1  \n",
       "3          150000                    6         1     38                  1  \n",
       "4           90000                    7         3     31                  1  \n",
       "...           ...                  ...       ...    ...                ...  \n",
       "371523      20000                    1         1     33                  1  \n",
       "371524     125000                    3         1     32                  1  \n",
       "371525     150000                    3         3     38                  1  \n",
       "371526     150000                    6         3     38                  1  \n",
       "371527      50000                    8         1      2                  1  \n",
       "\n",
       "[342990 rows x 11 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y  = data[\"price\"]\n",
    "x =  data.drop(\"price\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y,test_size=0.2,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# classifier\n",
    "rfr = RandomForestRegressor(n_estimators=10,criterion=\"mse\",random_state=0)\n",
    "rfr.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rfr.save']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "dump(rfr,'rfr.save')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9424.29166667,  805.        , 8564.08333333, ...,  967.33333333,\n",
       "       1512.9       , 4060.        ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=rfr.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8100578203428344"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score#to find accuracy of the model by pred with input \n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1993</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>190</td>\n",
       "      <td>118</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>119</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>118</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>103</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371523</td>\n",
       "      <td>6</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371524</td>\n",
       "      <td>2</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>108</td>\n",
       "      <td>125000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371525</td>\n",
       "      <td>1</td>\n",
       "      <td>1996</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>225</td>\n",
       "      <td>150000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371526</td>\n",
       "      <td>5</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>118</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>371527</td>\n",
       "      <td>6</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>320</td>\n",
       "      <td>148</td>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342990 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        vehicleType  yearOfRegistration  gearbox  powerPS  model  kilometer  \\\n",
       "0                 6                1993        1        0    118     150000   \n",
       "1                 3                2011        1      190    118     125000   \n",
       "2                 7                2004        0      163    119     125000   \n",
       "3                 4                2001        1       75    118     150000   \n",
       "4                 4                2008        1       69    103      90000   \n",
       "...             ...                 ...      ...      ...    ...        ...   \n",
       "371523            6                2005        1        0    118      20000   \n",
       "371524            2                2000        0      101    108     125000   \n",
       "371525            1                1996        1      102    225     150000   \n",
       "371526            5                2002        1      100    118     150000   \n",
       "371527            6                2013        1      320    148      50000   \n",
       "\n",
       "        monthOfRegistration  fuelType  brand  notRepairedDamage  \n",
       "0                         0         1     38                  1  \n",
       "1                         5         3      1                  0  \n",
       "2                         8         3     14                  1  \n",
       "3                         6         1     38                  1  \n",
       "4                         7         3     31                  1  \n",
       "...                     ...       ...    ...                ...  \n",
       "371523                    1         1     33                  1  \n",
       "371524                    3         1     32                  1  \n",
       "371525                    3         3     38                  1  \n",
       "371526                    6         3     38                  1  \n",
       "371527                    8         1      2                  1  \n",
       "\n",
       "[342990 rows x 10 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['limousine', 'coupe', 'suv', 'kleinwagen', 'cabrio', 'bus', 'kombi', 'andere']\n",
      "['benzin', 'diesel', 'lpg', 'andere', 'hybrid', 'cng', 'elektro']\n"
     ]
    }
   ],
   "source": [
    "vehicleType = df[\"vehicleType\"].unique()\n",
    "fuelType = df[\"fuelType\"].unique()\n",
    "\n",
    "print(list(vehicleType))\n",
    "print(list(fuelType))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 3, 7, 4, 2, 1, 5, 0]\n",
      "[1, 3, 6, 0, 5, 2, 4]\n"
     ]
    }
   ],
   "source": [
    "vehicleType = data[\"vehicleType\"].unique()\n",
    "fuelType = data[\"fuelType\"].unique()\n",
    "\n",
    "print(list(vehicleType))\n",
    "print(list(fuelType))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['manuell', 'automatik']\n"
     ]
    }
   ],
   "source": [
    "gear= df[\"gearbox\"].unique()\n",
    "print(list(gear))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 0]\n"
     ]
    }
   ],
   "source": [
    "gear= data[\"gearbox\"].unique()\n",
    "print(list(gear))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['golf', 'grand', 'fabia', '3er', '2_reihe', 'andere', 'c_max', '3_reihe', 'passat', 'navara', 'ka', 'polo', 'twingo', 'a_klasse', 'scirocco', '5er', 'meriva', 'arosa', 'c4', 'civic', 'transporter', 'punto', 'e_klasse', 'clio', 'kadett', 'kangoo', 'corsa', 'one', 'fortwo', '1er', 'b_klasse', 'signum', 'astra', 'a8', 'jetta', 'fiesta', 'c_klasse', 'micra', 'vito', 'sprinter', '156', 'escort', 'forester', 'xc_reihe', 'scenic', 'a4', 'a1', 'insignia', 'combo', 'focus', 'tt', 'a6', 'jazz', 'omega', 'slk', '7er', '80', '147', 'glk', '100', 'z_reihe', 'sportage', 'sorento', 'v40', 'ibiza', 'mustang', 'eos', 'touran', 'getz', 'a3', 'almera', 'megane', 'lupo', 'r19', 'zafira', 'caddy', 'mondeo', 'cordoba', 'colt', 'impreza', 'vectra', 'berlingo', 'm_klasse', 'tiguan', 'i_reihe', 'espace', 'sharan', '6_reihe', 'panda', 'up', 'seicento', 'ceed', '5_reihe', 'yeti', 'octavia', 'mii', 'rx_reihe', '6er', 'modus', 'fox', 'matiz', 'beetle', 'c1', 'rio', 'touareg', 'logan', 'spider', 'cuore', 's_max', 'a2', 'x_reihe', 'a5', 'galaxy', 'c3', 'viano', 's_klasse', '1_reihe', 'avensis', 'sl', 'roomster', 'q5', 'kaefer', 'santa', 'cooper', 'leon', '4_reihe', '500', 'laguna', 'ptcruiser', 'clk', 'primera', 'exeo', '159', 'transit', 'juke', 'qashqai', 'carisma', 'accord', 'corolla', 'lanos', 'phaeton', 'boxster', 'verso', 'swift', 'rav', 'kuga', 'picanto', 'kalos', 'superb', 'stilo', 'alhambra', '911', 'mx_reihe', 'm_reihe', 'roadster', 'ypsilon', 'cayenne', 'galant', 'justy', '90', 'sirion', 'crossfire', 'agila', 'duster', 'cr_reihe', 'v50', 'discovery', 'c_reihe', 'v_klasse', 'yaris', 'c5', 'aygo', 'cc', 'carnival', 'fusion', 'bora', 'forfour', 'cl', 'tigra', '300c', 'q3', 'spark', 'v70', 'x_type', 'ducato', 's_type', 'x_trail', 'toledo', 'altea', 'voyager', 'calibra', 'bravo', 'range_rover', 'antara', 'tucson', 'q7', 'citigo', 'jimny', 'cx_reihe', 'wrangler', 'lybra', 'range_rover_sport', 'lancer', 'freelander', 'captiva', 'c2', 'range_rover_evoque', 'sandero', 'note', '900', 'defender', 'cherokee', 'clubman', 'samara', '601', 'legacy', 'pajero', 'auris', 'niva', 's60', 'nubira', 'vivaro', 'g_klasse', 'lodgy', '850', 'serie_2', 'charade', 'croma', 'outlander', 'gl', 'doblo', 'musa', 'amarok', 'move', '9000', 'v60', '145', 'aveo', '200', 'b_max', 'delta', 'terios', 'rangerover', 'materia', 'kalina', 'elefantino', 'i3', 'kappa', 'serie_3', 'serie_1', 'discovery_sport']\n"
     ]
    }
   ],
   "source": [
    "model= df[\"model\"].unique()\n",
    "print(list(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[118, 119, 103, 11, 8, 61, 10, 171, 161, 174, 228, 33, 200, 15, 42, 40, 74, 225, 177, 97, 76, 131, 135, 167, 108, 6, 48, 206, 43, 32, 126, 104, 60, 153, 238, 214, 3, 100, 106, 245, 199, 29, 130, 26, 124, 80, 105, 226, 84, 31, 125, 166, 209, 19, 20, 2, 117, 0, 249, 210, 230, 122, 159, 99, 223, 115, 28, 37, 151, 145, 182, 63, 156, 82, 79, 123, 235, 51, 147, 220, 205, 17, 58, 170, 229, 121, 70, 14, 247, 165, 250, 154, 191, 18, 155, 109, 150, 50, 55, 188, 222, 144, 212, 88, 194, 27, 242, 30, 114, 57, 237, 193, 5, 45, 208, 190, 179, 132, 198, 81, 142, 12, 213, 138, 176, 77, 175, 101, 102, 4, 224, 128, 181, 66, 34, 83, 140, 172, 53, 236, 217, 187, 137, 173, 134, 216, 215, 36, 25, 148, 189, 248, 68, 113, 129, 22, 207, 87, 35, 96, 231, 160, 13, 152, 92, 62, 234, 246, 59, 47, 201, 69, 67, 111, 52, 107, 75, 219, 9, 178, 85, 211, 233, 244, 95, 195, 243, 221, 38, 240, 64, 54, 183, 41, 227, 180, 73, 127, 89, 241, 146, 185, 139, 110, 65, 184, 197, 163, 23, 90, 72, 78, 196, 141, 169, 44, 56, 162, 192, 164, 239, 112, 143, 21, 16, 203, 71, 86, 168, 116, 94, 158, 39, 157, 24, 232, 7, 1, 49, 91, 46, 218, 186, 149, 133, 98, 120, 136, 204, 202, 93]\n"
     ]
    }
   ],
   "source": [
    "model= data[\"model\"].unique()\n",
    "print(list(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['volkswagen', 'audi', 'jeep', 'skoda', 'bmw', 'peugeot', 'ford', 'mazda', 'nissan', 'renault', 'mercedes_benz', 'opel', 'seat', 'citroen', 'honda', 'fiat', 'mini', 'smart', 'hyundai', 'sonstige_autos', 'alfa_romeo', 'subaru', 'volvo', 'mitsubishi', 'kia', 'suzuki', 'lancia', 'porsche', 'toyota', 'chevrolet', 'dacia', 'daihatsu', 'trabant', 'saab', 'chrysler', 'jaguar', 'daewoo', 'rover', 'land_rover', 'lada']\n"
     ]
    }
   ],
   "source": [
    "model= df[\"brand\"].unique()\n",
    "print(list(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38, 1, 14, 31, 2, 25, 10, 19, 23, 27, 20, 30, 11, 9, 24, 21, 32, 12, 0, 34, 39, 22, 15, 35, 17, 26, 5, 33, 36, 3, 6, 8, 4, 13, 7, 28, 29, 18, 37, 16]\n"
     ]
    }
   ],
   "source": [
    "model= data[\"brand\"].unique()\n",
    "print(list(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['nein', 'ja']\n"
     ]
    }
   ],
   "source": [
    "model= df[\"notRepairedDamage\"].unique()\n",
    "print(list(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 0]\n"
     ]
    }
   ],
   "source": [
    "model= data[\"notRepairedDamage\"].unique()\n",
    "print(list(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([747.])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=rfr.predict([[6, 1993, 1, 0, 71, 150000, 0, 1, 38, 0]])\n",
    "test"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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