model1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt \n",
    "from matplotlib import style\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('autos.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateCrawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</th>\n",
       "      <th>vehicleType</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>brand</th>\n",
       "      <th>notRepairedDamage</th>\n",
       "      <th>dateCreated</th>\n",
       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>lastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:17</td>\n",
       "      <td>Golf_3_1.6</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>480</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:45</td>\n",
       "      <td>A5_Sportback_2.7_Tdi</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>18300</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manuell</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-24 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-04-07 01:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:21</td>\n",
       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>9800</td>\n",
       "      <td>test</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatik</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>diesel</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:04</td>\n",
       "      <td>GOLF_4_1_4__3T�RER</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>1500</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2001</td>\n",
       "      <td>manuell</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-17 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:20</td>\n",
       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>3600</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2008</td>\n",
       "      <td>manuell</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>diesel</td>\n",
       "      <td>skoda</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-04-06 10:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dateCrawled                            name  seller offerType  \\\n",
       "0  2016-03-24 11:52:17                      Golf_3_1.6  privat   Angebot   \n",
       "1  2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  privat   Angebot   \n",
       "2  2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   \n",
       "3  2016-03-17 16:54:04              GOLF_4_1_4__3T�RER  privat   Angebot   \n",
       "4  2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
       "\n",
       "   price abtest vehicleType  yearOfRegistration    gearbox  powerPS  model  \\\n",
       "0    480   test         NaN                1993    manuell        0   golf   \n",
       "1  18300   test       coupe                2011    manuell      190    NaN   \n",
       "2   9800   test         suv                2004  automatik      163  grand   \n",
       "3   1500   test  kleinwagen                2001    manuell       75   golf   \n",
       "4   3600   test  kleinwagen                2008    manuell       69  fabia   \n",
       "\n",
       "   kilometer  monthOfRegistration fuelType       brand notRepairedDamage  \\\n",
       "0     150000                    0   benzin  volkswagen               NaN   \n",
       "1     125000                    5   diesel        audi                ja   \n",
       "2     125000                    8   diesel        jeep               NaN   \n",
       "3     150000                    6   benzin  volkswagen              nein   \n",
       "4      90000                    7   diesel       skoda              nein   \n",
       "\n",
       "           dateCreated  nrOfPictures  postalCode             lastSeen  \n",
       "0  2016-03-24 00:00:00             0       70435  2016-04-07 03:16:57  \n",
       "1  2016-03-24 00:00:00             0       66954  2016-04-07 01:46:50  \n",
       "2  2016-03-14 00:00:00             0       90480  2016-04-05 12:47:46  \n",
       "3  2016-03-17 00:00:00             0       91074  2016-03-17 17:40:17  \n",
       "4  2016-03-31 00:00:00             0       60437  2016-04-06 10:17:21  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\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",
       "      <td>NaN</td>\n",
       "      <td>-0.003164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearOfRegistration</th>\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",
       "      <td>NaN</td>\n",
       "      <td>-0.001652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>powerPS</th>\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",
       "      <td>NaN</td>\n",
       "      <td>0.027113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kilometer</th>\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",
       "      <td>NaN</td>\n",
       "      <td>-0.026694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthOfRegistration</th>\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",
       "      <td>NaN</td>\n",
       "      <td>0.014963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nrOfPictures</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postalCode</th>\n",
       "      <td>-0.003164</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.027113</td>\n",
       "      <td>-0.026694</td>\n",
       "      <td>0.014963</td>\n",
       "      <td>NaN</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",
       "nrOfPictures              NaN                 NaN       NaN        NaN   \n",
       "postalCode          -0.003164           -0.001652  0.027113  -0.026694   \n",
       "\n",
       "                     monthOfRegistration  nrOfPictures  postalCode  \n",
       "price                          -0.002826           NaN   -0.003164  \n",
       "yearOfRegistration             -0.011607           NaN   -0.001652  \n",
       "powerPS                         0.044358           NaN    0.027113  \n",
       "kilometer                       0.001838           NaN   -0.026694  \n",
       "monthOfRegistration             1.000000           NaN    0.014963  \n",
       "nrOfPictures                         NaN           NaN         NaN  \n",
       "postalCode                      0.014963           NaN    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Co-efficient weightage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yearOfRegistration</td>\n",
       "      <td>-8.361697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>powerPS</td>\n",
       "      <td>127.808197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kilometer</td>\n",
       "      <td>-0.051331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Attribute   Co-efficient weightage\n",
       "0  yearOfRegistration               -8.361697\n",
       "1             powerPS              127.808197\n",
       "2           kilometer               -0.051331"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop('price' , axis= 1)\n",
    "cols = ['yearOfRegistration','powerPS','kilometer']\n",
    "lm= LinearRegression()\n",
    "lm.fit(X[cols], data.price)\n",
    "ans= pd.DataFrame(zip(X[cols], lm.coef_), columns= ['Attribute ', 'Co-efficient weightage'])\n",
    "ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc07ad7ed90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7FPBd4NvAC8BC59y/xWKuNrN98XPGjAQeAU52zr0VO9QioBO4BxgGLAcuTJxuLnADfnTT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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc07ad25c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = data[:500]\n",
    "plt.scatter(data2.yearOfRegistration,data2.price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc07e08bcd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ISG3VNXhx94fM7M+BzwOfBh4Hlrv7/4mVudLMDiDMyTIe+Alwuru/HDvUCqAfuB0YC6wD\nzk+cbilwA2GU0e6o7PLYeXab2dsJScP3EOaT+TrwmVrdr4iIiNSfuXuzr2HYimYC3rRp0yZ1G4lI\n3eRyObZu3crUqVOZNm1asy9HpCZi3Uaz3H1zLY+tqfFFRJpkx44dLFlyBjNmzKCzs5Pp06ezZMkZ\n7Ny5s/qHRUYxBS8iIk2ydOkyNmy4D1hNmHdzNRs23EdX11lNvjKR4a3eCbsiIpIil8vR07OWELic\nGW09k/5+p6dnGb29vepCEilDLS8iIk2wdevW6Lf5iT0LAOjr62vo9YhkiYIXEZEmOProo6Pf7krs\n2QjA1KlTG3o9Ilmi4EVEpAmmT59OR0cnra0XEbqOfgusprV1OR0dneoyEqlAwYuISJN0d69m0aI5\nwDLCpOHLWLRoDt3dq5t8ZSLDmxJ2RUSaZMKECaxbdwe9vb309fVpnheRAVLwIiLSZNOmTVPQIjII\n6jYSERGRTFHwIiIiIpmi4EVEREQyRcGLiIiIZIqCFxEREckUBS8iIiKSKQpeREREJFMUvIiIiEim\nKHgRERGRTNEMu1JVLpdj69atmrpcRESGBbW8SFk7duxgyZIzmDFjBp2dnUyfPp0lS85g586dzb40\nEREZxRS8SFlLly5jw4b7gNXAk8BqNmy4j66us5p8ZSIiMpqp20hS5XI5enrWEgKXM6OtZ9Lf7/T0\nLKO3t1ddSCIi0hRqeZFUW7dujX6bn9izAIC+vr6GXo/IUOVyOe688056e3ubfSkiUiMKXiTV0Ucf\nHf12V2LPRgCmTp3a0OsRGSzlbImMXApeJNX06dPp6OiktfUiQtfRb4HVtLYup6OjU11GMuwpZ0tk\n5FLwImV1d69m0aI5wDLgCGAZixbNobt7dZOvTKSyfM5Wf//1hJytKYScrevo6VmrLiSRjFPCrpQ1\nYcIE1q27g97eXvr6+jTPi2TGQHK29HdZJLsUvEhV06ZN03/0kinFOVtnxvYoZ0tkJFC3kYiMOMrZ\nEhnZFLyIyIiknC2RkUvdRiIyIilnS2TkUvAiIiOacrZERh4FLyKSGVrhXERAOS8ikgGaLVdE4hS8\niMiwp9lyRSRO3UYiMqxphXMRSVLLi4gMa1rhXESSFLyIyLCmFc5FJEnBi4gMWS6X484776zrQoea\nLVdEkhS8iMigNXr0j2bLFZE4JeyKyKAVj/6ZD9zFhg0X0dV1FuvW3VHz82m2XBGJa1jLi5l9wsx2\nm9kXEtsvN7OnzOxFM/uBmU1N7B9rZjea2TNm9ryZ3W5mhyfKTDCzb5rZLjPbaWZfMbMDE2WmmNkd\nZvaCmW0zsyvNTC1PIoOUH/3T3389YfTPFMLon+vo6Vlb1y6kadOmcfrppytwERnlGvLwNrPZwLnA\nLxLbPw5cEO07CXgB6DGzMbFi1wJnAO8mfMV7LfCdxCm+BcwETo3Kzgduip2nBVhLaGmaA5wNfBC4\nvBb3JzKaaPSPiDRb3YMXMzuI0Lb8YeD3id3LgSvc/fvu/kvgA4Tg5F3RZ8cB5wAr3H2ju/8M+Avg\nLWZ2UlRmJtABfMjdH3L3e4ALgfeb2eToPB3AMcCZ7v6Iu/cAnwbONzN1nYkMgkb/lNeIBGYRaUzL\ny43Av7r7j+IbzewoYDLww/w2d38OuB84Jdp0IqG1JF5mC2GKzXyZOcDOKLDJ2wA4cHKszCPu/kys\nTA/QBrxhb25OZLTR6J9SWr5ApLHqGryY2fuB44FPpuyeTAgwtie2b4/2AUwCXo6CmnJlJgNPx3e6\nez+wI1Em7TzEyojIAGn0TzEtXyDSWHXrMjGzPyHkqyxy91fqdZ5GWLFiBW1tbUXburq66OrqatIV\niTSXRv8UaPkCEeju7qa7u7to265du+p2vnrme8wCDgM2m5lF21qB+WZ2ASEHxQitK/FWkUlAvgto\nGzDGzMYlWl8mRfvyZZKjj1qBQxJlZieub1JsX0XXXHMN7e3t1YqJjDrTpk0b9Q/mgSQwj/Y6kpEv\n7Qv95s2bmTVrVl3OV89uow3AmwjdRsdFr4cIX0+Oc/fHCIHDqfkPRAm6JwP3RJs2Aa8myswgtFPf\nG226FxhvZifEzn0qITC6P1bmTWZ2aKzMacAu4Fd7e6MiMnopgVmk8erW8uLuL5AIDMzsBeBZd380\n2nQt8Ckz6wOeAK4Afgd8LzrGc2Z2C/AFM9sJPA9cD9zt7g9EZX5tZj3AKjM7DxgDfBHodvd8q8r6\n6Fpui4ZnvyY61w1Z79ISkebKJzBv2HAR/f1OaHHZSGvrchYtGp0JzCL11uhJ2rzojfuVhEDjJkIr\nyf7A6e7+cqzYCuD7wO3Aj4GnCHO+xC0Ffk1o7fk+4SvQR2Ln2Q28HegntOp8A/g68Jma3JWIjGpK\nYBZpLHP36qVGKTNrBzZt2rRJOS8iUpUSmEUKYjkvs9x9cy2PrQnaRERqRAnMIo2htX1EREQkUxS8\niIiISKYoeBEREZFMUfAiIiIimaLgRURERDJFwYuIiIhkioIXERERyRQFLyIiIpIpCl5EREQkUxS8\niIiISKYoeBEREZFMUfAiIiIimaLgRURERDJFwYuIiIhkioIXERERyZR9mn0BIjI4uVyOrVu3MnXq\nVKZNm9bsyxERaTi1vIhkxI4dO1iy5AxmzJhBZ2cn06dPZ8mSM9i5c2ezL01EpKEUvIhkxNKly9iw\n4T5gNfAksJoNG+6jq+usJl+ZiEhjqdtIJANyuRw9PWsJgcuZ0dYz6e93enqW0dvbqy4kERk11PIi\nkgFbt26Nfpuf2LMAgL6+voZej4hIMyl4EcmAo48+OvrtrsSejQBMnTq1odcjItJMCl5EMmD69Ol0\ndHTS2noRoevot8BqWluX09HRqS4jERlVFLyIZER392oWLZoDLAOOAJaxaNEcurtXN/nKBi+Xy3Hn\nnXfS29vb7EsRkQxSwq7IMJQ2l8uECRNYt+4Oent76evry+Q8Lzt27GDp0mVR8nHQ0dFJd/dqJkyY\n0MQrE5EsUcuLyDAykLlcpk2bxumnn565wAU03FtEakPBi8gwMpIf7vnh3v391xOGe08hDPe+jp6e\ntepCEpEBU/AiMkyM9Id7s4Z7K79GZORR8CIyTIz0uVwaPdxbyymIjFwKXkSGiZE+l0ujh3vXugtu\nIC04auURaRB316vMC2gHfNOmTS7SCB0dnd7aeojDbQ5POtzmra2HeEdHZ7MvrSZ27NjhHR2dDux5\ndXR0+o4dO2p6ni1btkTHX+3gsddtDngulxvwsZ599tmq1zyQMiKjzaZNm/L/Htq9xs9ntbyIDCMj\naS6XNPnh3rlcjrVr15LL5Vi37o6aD5OuZRfcQFpwRnKitchwpHleRIaRkTCXy0BMmzatrvdV3AV3\nZmzP4LrgBrIgprtr0UyRBlPLi8gwlJ/Lxd2VQzEEtcqvGUgLzkhPtBYZjhS8iAxDGimz92rRBTeQ\nJOqRnmgtMhyp20hkGCrOoZgP3MWGDRfR1XUW69bd0eSry4ZadMHlW3A2bLiI/n4ntKZspLV1OYsW\ndTJx4kSWLl1G+B54PiE3sbiMuoxEak/Bi8gwUy3PYv369fT394/YfJha29v8mu7u1XR1nUVPz7I9\n2xYt6tyzPQSZXwbWEFp5isuISO0peBEZZsrnUBwHtNDR0bFnS3xRw7TFHGXvlWvBKQ0yzwV6gZuB\nq/niF6/VYpMidaLgRWSYKT9S5gPAwcCNxLuS3v3u9zJmzBit1FxnyRac9CBzGnARcDV9fX0KIqWI\nvmDUjhJ2RYaZ9JEyVwE/JwQuxese/du//UhzjDSBEnVloJSAX3sKXkSGodKRMpdGe5JdSVOA3SN2\nMcfhrNHLHUh2aRLD2qtr8GJmnzSzB8zsOTPbbmbfNbPpKeUuN7OnzOxFM/uBmU1N7B9rZjea2TNm\n9ryZ3W5mhyfKTDCzb5rZLjPbaWZfMbMDE2WmmNkdZvaCmW0zsyvNTAGcDDvJmWh7enqiPclv+d+P\nfjZnjpF6r+Uz3NcKGukzIsveG+mrxTdLvR/c84AvAicDi4B9gfVmtn++gJl9HLiAkO12EvAC0GNm\nY2LHuRY4A3g34X/p1wLfSZzrW8BM4NSo7Hzgpth5WoC1hDyfOcDZwAeBy2typyJ1kJ+s7rTTTkv9\nlt/Ssioq2diui3o3g2elmb1Ryx1IdmkSwzqp9WJJlV7AocBuYG5s21PAitj7ccBLwHtj7/8I/Hms\nzIzoOCdF72dG70+IlekAXgUmR+9PB14BDo2V+QiwE9inzPVqYUYZNsotarhw4eKGL+ZYWEBydXTO\n1TU9Z72PXy9btmzxtWvXDmrhRxnZarlIaNbUc2HGRgcvU4F+4Njo/VFR0PHmRLkfA9dEvy+MPjMu\nUeYJYHn0+18Azyb2t0bByp9F71cCmxNljozOf1yZ61XwIsNOLpcrekA2aqXmvHr/Z5zF/+y1qrRU\nMtJXiy+nnsFLw4ZKm5kRun9+6u6/ijZPjm5se6L49mgfwCTgZXd/rkKZycDT8Z3u3m9mOxJl0s6T\n3/eLQd2QSAX1HBKZHLLb6MUcB9IM7u5Dvv+BHH+4JcNqRmSppNJEhzI0jZzn5UvAscBbGnjOmlix\nYgVtbW1F27q6uujq6mrSFclwtWPHDpYuXdaUOVfqvVJzXrUVmz/3ub/nJz/ZuGfrYO+/VitCN4pm\nRJZqRsNq8d3d3XR3dxdt27VrV/1OWOumnLQXcAPwG+CIxPaBdBu9DXUbSUZkNVcjzbp163zlypW+\nfv36kn3lmsEnTpyUev9z5y4YVC5IlprZ165dGzWNP5no5nrYoUVdSTJqZTrnJQpcfgu8vsz+cgm7\n74m9r5awe0wU4MQTdk+jOGF3CaUJu+cSEnb3LXNtCl5kwLKYq5Gmr6/PJ06cVPTQnThxkj/22GN7\nyqTl2cydO7/i/Q/mAd7oPJ69Uf7P/XiHthERyIoMRWaDF0JX0U7CkOlJsdd+sTKXAs8C7wDeBPwz\nYYGQMYnjPA68FZgF3A38JHGutcBDwGxC19QW4LbY/hZCXsudwJsJo5G2A1dUuH4FLzJg5b+BP+mA\nr1y5MhMBTAhcih+60OYTJ04qKRtPHq52/3DroB/gyeTk4aq0pejKERHIiuyNLAcvu6MWkeTrA4ly\nl0UtMC8CPcDUxP6xhPlingGeB/4JODxRZjyh03lXFDCtAg5IlJlCmNXrv6LA5e+BlgrXr+BFBiz9\nG/iz0Tfw4d+C4B66iio9dNO6kPKqtTxBbsQ+wNNaiioFcmvXrm32JYvUXT2Dl7pOUufuLe7emvL6\nRqLcZe7+Wnc/wN073L0vsf+P7n6hux/q7ge7+3vcPTm66Pfufpa7t7n7BHf/S3d/MVHmt+7+dnc/\nyN0nufvH3X13/WpARov86KJ58xYkJpI7ldBomI1pwe+///7ot/SRPvfee2/Zz5abLh+WR8frIzSq\nhmM1anKuRszSO/AZkYdn0rFI5tQ6GhpJL9TyIlWkze+RzBepdddBPSdC25uWF/dyLRDjE++Pb0jL\nS7PnXslS0rFIPWS22yjrLwUvUk250UVz5y7wlStX1rTrYG8exoMJeAo5L4WHbrmcl3LyuSrjxx/q\nA82fqbVmj/zKUtKxSD0oeFHwIsNQtRyPnp6emra8DOVhPJSA57HHHqs62mggajH6aqitTMNp5FdW\nko5Fai2zOS8iI1m1mWD7+/tTc0BaW5fT0dFZcZKqZJ7GUFemLcz8ehVwK3B11Zybo446imee2cb6\n9etZuXIl69ev55lntnHUUUdVq5Iie7Mg3d4uzDicFsPLL6450iYlE2mmRs6wKzJsDWU6/4HMBJs2\nLfhxx83ms59dmXoNP//5z7nhhi+VzFB7zjlnR+8GPiV/YebX44FL9nyiv//4PQFPpXtdvHgxixcv\nrlwJKfJ12draGm0Z/Ey5ezvdftZm6RWRQap1U85IeqFuoxFvb5M6B5KUuWXLFr/22mv92GPfmHqe\n4mswh7GJBNexPmfOKRW7QebNW1By7DVr1niY4bW4qym8b6n5cN20utx33/0HnT9Tqy6fWuTuiMjQ\nKedFwYvUyd4mdVZKykx7mIeRNg8XnadwDVdFwcaERLAx3qHF581bMKgp+WfNOqliEFBt5FAt6hLG\n+WBHG91aFdhbAAAgAElEQVR8881RuaEnOhcCoOMHdW4RqR0FLwpepA5q9Q1/y5YtvmrVKl+1alXR\nZ9If5oc4dBadp3ANN1e8nmOOOdYXLlxc9DAe2JT86UHAZz/72YbVJax3WOthorr0IKQ02Bv6n0vx\nbL+5qucWkdqrZ/CinBcZtQaS1FkpJ+SBBx7gvPPOZ/Pmh/Zsy6+g/J//+Z+pKw2Hf8fLiE/WVriG\ndRWvJ5d7kte97khyudyelWn7+vro7Lyr7GeC9LyPp59+eki5Pmmq1WVYZuz06PfVQGneSXGey1eB\nCwn1tQDYSGvrchYtqpzonFea85L/TPq5RSRbNNpIRq3iB1xc5aTO/EiYk08+ORa4LARu2jOSp/rD\nvG/PeQrXcETF69m9+6NRQMSe0SvV7iH8Ez+f4hlvLwBauOee+4Y8miep+nX8O5VGW/X09CRGU90O\nnEII9I4AlrFo0Ry6u1dXvI78KC0zG/JILxHJgFo35YykF+o2GvGGMgtq5e6ggc3xAlel5Lx8zAsJ\ntvEk00Oi7beWdHmE7pp8nkz8MxOi7eXyPqzmE7iVq8vknDHxhOjSrqJkF9dGB3zVqlUVz52WX7Rw\n4eKSbjZNEifSOMp5UfAidZKWcDt37nxfs2ZNam5Ftenz8w/bm2++2dvbZ5c8zMPol5ai8zz44IOJ\na0hPMg0JvcU5H4XcjoWJz8TfJ/M+flzxHoaazFopebmnp8dXrlxZkiRcnKw89GuqlHitSeJEmkPB\ni4IXqbNcLudr1qxJHXKcPnIoPQkWzo+1eJD4PQxpvuWWW1LPc+2113qh5eUqDy0tV0Xvj/eWlgkl\nLSPFibLxACWZDLzOYaWHxNmPxe5hi9c6mTUeLFQail6a5NvpyVangbQGDafZdEWkQMGLgpemqudC\ngMNJpW/vA20hgH3dbHzRMVpa2ry9/cQ99Vd8nh87XOItLW2xkUPJocUtewKftC6PSl1fc+cucNgn\ncbz8+5mJ7bUfRlypTotHBG1xWOOQHjymyf+9XLVqVcWAUiOLRJpDwYuCl6Zo9qq8jVR9qG/lFoJ4\nd1ClFoDCef4xOk5xkNLWNjE61tUeWl4+7nCAjx8/sey1V+quOeSQwz1tYcQQwNR3wcSBr/2U7CYL\nQVW5eWjS589Ry4vIcKPgRcFLUzR7Vd5GKm4FiD8An4w9HPP7dpQEHuPGTfC/+7u/q9oCUJyjkkz6\nPSjlIbzF8908+YdwuZawZF5J9fycq+v6sK9Wp2vXro3NgjvwICo9YXps1OI1uC4nEakfBS8KXhpu\nJOURDKTbq/h+43kgaS0vz5YEL2EK/z+tWmeF86SVi+eilJ7j4IPH+7x5by3a1tHR6Vu3bi1piWhv\nn+3nnntulYDs1rJBRa3qfWAtLwP/O1b+mP/oyfyikdpKKJIVCl4UvDTcQL41D3f333+/t7fPLnmo\nP/jgg6nl3/a2RZ6+rtCfevFw5IVeOoX/OAfzWbNKRxglWwDa20+M1W1aoHS1F7qm0rp7CtvMxvsh\nhxwey8e5Nfp8W4UgqTEtL+6V83GG8nes2mdWrVo1KvKzRLJAwYuCl4ar1uVQ63VxaqmQE9Hi6fke\nLSXfyrds2eLHHddeUt5sfLQ9Ofy4fCtMpXlN3N0feOABL831KG41CO9vKhN0fDUl4ElPvi3ktiTz\nc8Z7Wt7OvHkLavpnUSkfZyiteyOpRVBkpFPwouCl4cLieOUnP6s2aVgzdXR0ektLW8WHXEtLm3d0\ndA54PZ3Cvs8mvvmXtpC0th7i8+YtqNgCUJzrkdaS0xZtj19LPP8m/8oHVAckPp+f2G6Kl442anU4\n2JOJshMnTqpbN0u5uVb2bpJA5beIDGcKXhS8NFxhZd/0yc+Ga/BS+GZ+iRfyOnIpAUDILyl08+TL\nJ7sjfhy797Ee5kqJ58YMvhWgNL+mUtCUS9l2qRe6h/Iz6X64QtCVHM3zRk+29Mydmz4MO+3a165d\n6z09PX7zzTeXLEY5WJVaZmr5mXoZLdMIiAyFghcFLw1XbfKz4fqfdSEnYk7igd3p8IAXkmI3Jh7u\nD3hxEJGelAvmhe6oeIJtPHConBdUnLdRbZTTx7y0uyete+gSTw/S8pPelbbKXHzxxX7WWWf51772\ntT3XVu5hXNpCVRz8LFy4eK+Ch6HMgtvMmXNH0zQCIkOl4EXBS1OMH3+op+VLjB9/aLMvrazitX7i\nD+zxiQfuMdHPj3thXaL4/C1pXTnl1guqZ8tLMg9mTJmAKh6k7fDS7q7Kxz7kkMNSRzLlH8Zz5y7w\nlpZ8fZTWjdn4ve62yVIrRrlpBObOrdxdKDKaKHhR8NJw4QFrnjb6BmzY/udcfbK5f/Xyawc96MWt\nLZUe+vn1gmb7YBNfSxdTXOjpuUWHJa6ztUJA9U0vtKoc7yHozAc15Vp1Lokd52AvzY0Z63PnLojN\n/Dv0rrJK0lox2ttPLDsqrNkGNqGhWmJE6hm8tCCSYuPGjYS/c7cAOWBt9PMrgEf7h4dcLsedd95J\nb28vW7dujbbOT5RaEP28HHgSWB37+ThgwI+ALmC/2DFywJ1Ab+wYAHcB04AeYA6wDDgi+vk8F1zw\n0dTrA6Jr3A2cEJX/EbArcYwxwCux67wK6Ae+CJwJTIl+Xh8d68Xo/XXAz5kyZQKf/vSnYtcal/+z\nOzZ2nKOBAxP1cgA//elPueeezbH6qFy/fX19DNbSpcvYsOE+4B+BhQBs3vwQs2fPZsmSM9i5c+eg\nj1kLyT+3vOp/x24FVrNhw310dZ1V56sUGaVqHQ2NpBejuOWlkLBbfj6NZkv7xl7cSlDuW3GlvJYW\nnzPnlESLTLKFxjx9+PGJHl/5udz1hQUY0/KJPhTbnsxjqZYbsyq6n2SCdT7nJX6t+ZFI8VygSnV2\nad1aXopbMdJHbjV6FFG1fJbqLS+5vaoTkZFC3UYKXhouC/NplMs7mDhxUslQ2tC9Ep8czlMfltDm\nCxcu9n322d/T54jZz8OcKskuloUO/1j0sC2+voe9NBga62Fm2HgAhMPRiXIDCTDWOyxOXHN+Ecly\nQdjV0f0n6yUZGN3q6flAhfodas5LIXn5x3X7+zbYXJriRTjDqK5kEJU2XLuQO5W/9nBPwyHQF2kG\nBS8KXppi4cLFJevFmI33hQsXN/vSqgZX8+YtSDyw9/XBDHOuvM+ih/dFDkcVnSe/8nPp9ZUGSqFu\n40nEnQ5pCynmH4r5XJZki0+rF4Z6J685/5n8g/iqxEN2IPd7tZeu51Sb0UalQ9trN6PzUEYEFa4n\nPeDLB0Bbt24tmYwwJIU/5mktesp/kdFIwYuCl6YYTvNpJFWbJv6aa67xN7zhzYmHS4sXunzOiX0+\nPkV/2kKMxcdOf7iF1pL8t+zi68vPVpw+Hf+xx74xmlTvyipBxIFefqh0uWt+uCTQKIxIit9Tq5cG\nRvGlB/Lbr/KWloP8xBNn+6pVqwY1z0u5FpCBTCo4lJaXoSwsGv7cyg8vzwdRhWPnV//OL8lwvKeN\nxtIkejIaKXhR8NJUzZxPo5zyLS9fjh4+aUsDTIiCl/0rPPzLDYEurO5caHkpHYqdD14KyyvMqBo4\nrFmzJhEklguaLApG4nkyYd/JJ59cpT7yx04PoEJgVDrj7mOPPbbXAWy1FpBCkJz/M9v7mXOH2u05\nkGUxBjbaaPh2t4o0ikYbSVM99thjPPjggzzxxBPNvhQgjALZunUr48dPBM4njMT5BnA1cDFwAGEE\nzo2EkTQvAYcAf0sYVTSGMJrmMMJIo+TIo0lAJ3ARYQTMqcCM6PhEx/g8xaN+vgjs5uKL/5r589/G\nkiVLgBZgW+L49wH5EShh1M+ECRNYt+4Obrnllmh7udFBDjxMGOV0evQz7Hv7298ene+i6Dy/jX5e\nDBwc/b4Q+N+J/cujz90E/IzCyLKrePbZ7bz66qusW3cHuVyOtWvXksvlWLfuDiZMmFD2zyepMJqo\nUA/xkTj5+3/wwftpb59OfNTVokVz6O5ePeBz5VUbEVRuVNTu3bsrfu7VV18dwGijwZ9XRAap1tHQ\nSHoxylte+vr6Svr189/GmyHtG3zpPDQtDhdGvz/spaOJ8q0P1b49X+uQX006LXG33LpDB3jIr/lQ\nleNf5fk5WvJdEYX1pNJHBx1wwMElLRPQ5vPnv61Crkb8GpJ5Kzi8Kfq5MfV+quWarFu3zleuXFl2\noc6htIDUoqVvqC0vA/mcWl5EBkbdRgpemuKQQw5LeXAf5Acd1NaU/4RLcxiOT7m+Ni8sDXB89OD/\nRy8dQpw2wiZtqHGlh1TaukNXJT5bKW8m5MlcdNFFvn79+tjw9PSurCOPfL2nzbCbT6CeM+ctXjoK\nqsVDEBe/hvzSCMmcoHiXVvHDOhlMDDSwrZablA+O6jG77lAXcBzI5yqV0cKRIoGCFwUvDVfa99+Y\nERTlHmKlU+rfnLi+ZBAxO7Y/fUh06ec7vTiXpfIImOJ1h/IjeOLBSbVv561FZQ8+OD9b7gQvHh00\noaRsCL4eLAoy2tomenowd7wXJyXf5uVzghbuedi+7W2LyuaqFK+KXTjXxImTKvy5ldbD7bffXrek\n8KEmnA/kc5XKDOdEd5FGUvCi4KXhLrww3/WSH41zYsnDqpbfJqsldRa+wSdbRsoFFu+Ifv64wsMz\nPkV/WrnBrDu0wGGNF1pePuyhSyvZzTPBQ+tJfnRPYS6RwpT+ydaTVodxsbq/yuGg6Jzhfo8+elqZ\na00m7Ma72srf18SJk/yUU+Z62vIQxx13QsXPJ7uQ0udEaYtd11iHm/b8vWppafP29hNr1goz1G6o\ngXyuUpnhmOgu0kgKXhS8NNwVV1zh6V0Y6V0Le6vasNbyay1VCyzyuSflgpzkgz1ZbmFKANLmcGji\ncy2J36/1MK9M8vjx92ndQ/mh3PkhuPHZbe/3QotS/jUzEZCUu/5ka0xLhTr5H9HQ5XxwlzZiq3yd\nrly5sujPNq0lItzrw7FjzvcQ/C0oKqcWC5Hs0mgjabhnn30WaKV0NE58tMwCYO9HUORyOXp61tLf\nfz3xETz9/dfR07M2traMEUYS5a/neMJoo9WEUTeXABcAY2NHrzaC5zbCKKIDypR7L/A8xesOvQy8\nSnG9tAHzomvaTRjl85nYcVYA46J9eY8SRvnkj/Gb6B5vBP4G+AAwNSp7A3AKYTRQ/LxPEUYLLUu5\n/hxh3aT8qKv8yKgbouv4dpk66WD37ndHZdLWUvIydRU+f8oppxRtdXdKvRb4E8KoqddFx3pfdIzj\nCaOqtD6QiKTbp9kXIMPTunXrCAsB3ggcCnyN8PC8jvCg7AXuB2Dq1KnpBxmggQxr3bRpE8UPUwgP\n5vnA2RQHBWMIQcHphIfiOYQg5ynCMOingc8Ci4Gl0Wd2A58ALiQ8nBcQHqQfT1zT64HHgFWx6zgz\n+swyQhBzFXA48J/AFYQg8Fbgjyl3fztwbuIYU4Ad0e9ro3L3EYKUK4HZwC+BkwiByDLgpxSGSuev\nf1X02XJDei+L6iN/rxdFx3hddP5Kn20h1Gm8ri5g4sRJLF68uOgTxUOl5xP+TC6iEAQ/mbLvE8Ad\n9Pc7PT3L6O3tZdq0aaXVV0f5IflTp05t+LlFpIpaN+WMpBejuNvouOOOi5r7Dks09+fff6xmOS/V\nkjrnzo13JSS7KuakdItM8OI1ZmZ4aR7JPg5v9dJE5GQ3z76JLpoDKnaZwOtTukfyx03rgmnxwqil\nfN7Nh718knHyzyOfAzQ2cb6BdK0dV+ZYa736bL+Xe7ILL2200d4MK45PwjeU5QGGaijLCohIKeW8\nKHhpuHe9611eSCpNPkBba/ofenjAxZNn87kl42KBRv5Bkp8h9lkPeRLVHn7xY6cFDgtSgoSDvHjN\noiOjn/MG8MBNW5eo2meu9fT5aAaSKDzWQw5KPOCJj1Qa62H237RVpfOrVsdHIeFhCYB8/ZQuGRAW\ngCxcT36od5pqQ6Ur71vrtcyrGqihLCsgIqUUvNQ2IDmfkMjxEqEtfnaFsqM2eHnzm/NzgKQ/QI85\n5pianavwgIu3KlRLdJ0XBRnVHn5XVLyP4n2lw8FD4HSMFycLl3uot1Q5T7nrnOGDG6J9iZcGYd/0\n9EnozAsT0RW3BoWk3PQRQMcem/9McnRX6fIGlVpF9q7l5aqGBw1ZWE1dJCuUsFsjZvY+4B8ImZQn\nAL8Aeszs0KZe2DD09NNPR7+l5zz8/ve/r9m5jj76aEIOxauE5NnpFKa0jyfELox+7wN+QiEhdg1w\nJyEPBwqJpwcC/7fifRTvW0aIZ+PnPZCQ+BpPFv45cBTFSbzPEfJmKp2nXNLwFooTYz9cpfxfUpxA\nuxv4N2ACcEd0zoMIdfk24HeEPJxbo5+Pc8opc5kwYb/EPfw3zPZn7twFfPe7t0fnOgfoiX6/Ojp+\nfmmAcD2Vcp6mT59OR0cnra3Fyxa0ti6nvX028+YtKNkXkq5bgEuGvDzAUA11WQERabBaR0PD+UV4\nMl0Xe2+E/9kvLVN+1La8HHDAAbFvoOs8DPtdv+cb6IEHHlizc5VOQFetKyi/QOLDDsWzvIZWhXFe\n2nJT7Zv/UFoI8nO6THG4pcrn01pr4nOdDHSIdrllCfbz9PlqSltjOjo6/YEHHojeX+2FbqPiFoaF\nCxe7Wb7LaaGHVp4rPd8lNdBWkcFO6DZv3gJfs2ZNU1o51PIiUjvqNqpN4LIv8ArwzsT2rwPfLfOZ\nURu87L///p4+Ydo+Dub7779/zc5VnBexvMzDPN4VlH9A56f/T+bkHOIh2MoHYJUCh5Oi8h+rct5K\n+8xhlVdalyh9jpqxDqeUeVhe5uldZzelPlQL872Uu9awJEB+1euBTNtfGlgUz/I72HWusjKhW3HQ\nFv4czcbvWYZBRAZG3Ua1cShhzOr2xPbtwOTGX87w9tJLLxGa7g+ktBulJdpfG6HbCEI3ySOx3+Py\nXSZTCd0LRui+KZ4bJgwd3kHoUpoe+/wfKO4i+QNhpel10bb8itHlzltpnxNWj94dO3785+6ozMzE\n599C6IbJr2Ad7zq5OvrcRsJw6RywBPhkotyFhCHfvyJ0CZW71icBWLBgAZCs89J7mjp16p7VnnO5\nHO3ts2ltbSP+d+H3v3+F8867gIGaNm0ap59+euqw40r7msH9JeJ/juG9iAwbtY6GhusLeA3haXBy\nYvvfA/eW+cyobXlhAN0ttVQY4XFamRaMwro7hXWJKrWGrEq0moxPtEyMd/hp7Hiv8fQRT/mE2LTW\nm0O8kND6IS8sBxAf7dPmobUqf+yrHP4+UbflEm2T9b/DK894HO63vX32gBYGHOgCgqOpK6X4XktH\nYo2kexWpt3q2vIymSeqeIcy6NimxfRLha3NZK1asoK2trWhbV1cXXV1dNb3A4alSAmrtdHevpqvr\nLHp68pOy5Vsu8loIk9L9KPo97y4Kk8VBoTVkQez3luh1KfBfhATUrcDc2P53AN8CXkycNz9br1OY\naTevE3hndE23RMdxwky/8es+ltBy8mJsX3KSty5aWu7mqKMO58tfvhF3p6PjdEonzcvPwuuE1pm/\nKbn3m276Ep/61Gfo6Slc66JFnSWJr4U6r1xuIEmsw6XFZG8V3+sUIH9f+wEj615Faqm7u5vu7u6i\nbbt27arfCWsdDQ3nF+kJu78FLilTftS2vCxatKjit+3TTjutLufN5XJ+zDFvSGnBOChqjdg3+mke\nFjhMaymJt9Dk80aSLRYhR+Q73/mOL1y42OHgWItMsoUmnu9zkIf8mI2xcxzmIWE27Tz513QvnieG\naGXm0iTWvHBdpXkyCxcuHlCryUDzSKqVG70tLyP7XkXqTQm7tQte3kv4+vsB4BjCHPLPAoeVKT9q\ngxd398IkdclE19a6njd9Ib98EBJPGjVPT2oNv5944km+Zs0aP+GE/IrYxd05p5wyN3G+fNfQpQ5/\n7XDpnkTNjo5Ob2kZ73BE4nytsUBkrBe6hvKLKh7gBx88oegz7e0n+oMPPujulQOHwY7SqecssAPt\nYhoJRtO9itSTgpfaBjAfBZ4gTFJ3L3BihbKjOni57rrrPDnCBFr9uuuua8j5c7mcr1mzJrE8AP6G\nN7zJ3/Oe9/jXvvY1z+VyfvHFF/s73/nOPe+TwcBAH/QPPvigt7fPHnCw8MY3vnnPkN4dO3ZELSXF\nwdTChYt9x44dezWaZjiM0ml0sNRMo+leReqpnsGLeXhISwozawc2bdq0ifb29mZfTtN0dHRw//33\nc/LJJ9PT01P9A3XQ29tLX1/fXi2SN9BjVCpX7Ri9vb1s3BhyTxYsWDDi8iNq8eeQFaPpXkXqYfPm\nzcyaNQtglrtvruWxFbxUoOBFRERkaOoZvIymeV5ERERkBFDwIiIiIpmi4EVEREQyRcGLiIiIZIqC\nFxEREckUBS8iIiKSKQpeREREJFMUvIiIiEimKHgRERGRTFHwIiIiIpmi4EVEREQyRcGLiIiIZIqC\nFxEREckUBS8iIiKSKQpeREREJFMUvIiIiEimKHgRERGRTFHwIiIiIpmi4EVEREQyRcGLiIiIZIqC\nFxEREckUBS8iIiKSKQpeREREJFMUvIiIiEimKHgRERGRTFHwIiIiIpmi4EVEREQyRcGLiIiIZIqC\nFxEREckUBS8iIiKSKQpeREREJFMUvIiIiEimKHgRERGRTFHwIiIiIpmi4EVEREQyRcGLiIiIZIqC\nFxEREckUBS8iIiKSKQpeREREJFMUvMiAdHd3N/sShgXVQ4HqIlA9FKguAtVD/dUleDGz15nZV8zs\nMTN70cx6zewyM9s3UW6Kmd1hZi+Y2TYzu9LMWhJl3mxmd5nZS2b2GzO7JOV8bzWzTWb2BzPLmdnZ\nKWXeY2aPRsf5hZmdXvs7H7n0jzFQPRSoLgLVQ4HqIlA91F+9Wl6OAQz4S+BYYAXwV8D/zheIgpS1\nwD7AHOBs4IPA5bEyBwM9wONAO3AJcJmZfThW5kjg+8APgeOA64CvmNniWJk/Bb4FrAKOB74H/LOZ\nHVvLmxYREZH6q0vw4u497v4hd/+huz/h7t8Hrgb+e6xYByHIOdPdH3H3HuDTwPlmtk9U5ixgX+BD\n7v6ou38buB7469hxzgMec/dL3X2Lu98I3E4ImPIuAu509y9EZf4XsBm4oPZ3LyIiIvXUyJyX8cCO\n2Ps5wCPu/kxsWw/QBrwhVuYud381UWaGmbXFymxInKsHOCX2/pQBlBEREZEM2Kd6kb1nZlMJrRzx\nFpPJwPZE0e2xfb+Ifj5WocyuCscZZ2Zj3f2PFcpMrnLp+wE8+uijVYqNfLt27WLz5s3NvoymUz0U\nqC4C1UOB6iJQPQSxZ+d+tT72oIIXM/sc8PEKRRyY6e652Gf+G3AnsMbdvzqkq0y5lBodp5ojAc46\n66wGnW54mzVrVrMvYVhQPRSoLgLVQ4HqIlA9FDkSuKeWBxxsy8vVwNeqlNnTUmJmrwV+BPzU3T+S\nKLcNmJ3YNim2L/9zUkoZH0CZ56JWl0pltlFZD3Am8ATwhyplRUREpGA/QuDSU+sDDyp4cfdngWcH\nUjZqcfkR8CBwTkqRe4G/NbNDY3kvpxG6gn4VK/NZM2t19/5YmS3uvitWJjns+bRoe/xcpxKSffMW\nJ8qUiO73W5XKiIiISFk1bXHJM3ev/UFDi8tGwhDnDwL5wAN33x6VaQF+BjxF6Ip6DfAN4GZ3/3RU\nZhzwa+AHwN8DbwJuAZa7+y1RmSOBR4AvAV8lBCnXAp3uviEqcwrwY+CTwB1AF/AJoN3d84GSiIiI\nZEC9gpezCYFE0WbA3b01Vm4K8GXgrcALwNeBT7r77liZNwI3ErqYngGud/erE+ebD1xDmFPmd8Dl\n7n5bosy7CfPMvA7oBS6JhmeLiIhIhtQleBERERGpF61tJCIiIpmi4EVEREQyRcELYGZ/a2Z3RwtE\n7ihTpiaLSGaNmZ1vZo9H93SfmSWHt2eemc0zs38xs/9nZrvN7J0pZS43s6eihUZ/EE28GN8/1sxu\nNLNnzOx5M7vdzA5v3F3sPTP7pJk9YGbPmdl2M/uumU1PKTei68LM/ipavHVX9LrHzJYkyozoOkhj\nZp+I/n18IbF9xNeFmX0muvf461eJMiO+HiAMyDGz26L7eDH6t9KeKFP3ulDwEuwLfJuQPFzCarSI\nZNaY2fuAfwA+A5xAmPW4x8wObeqF1d6BwM+BjxLmECpiZh8nzBB9LnASIbm8x8zGxIpdC5wBvBuY\nD7wW+E59L7vm5gFfBE4GFhH+Xaw3s/3zBUZJXfyWMAKyHZhFmPLhe2Y2E0ZNHRSJvrScS/g/IL59\nNNXFLwnzg02OXnPzO0ZLPZjZeOBu4I+E9QlnAn8D7IyVaUxduLte0YsQlOxI2X468ApwaGzbR6I/\nsH2i9+cRRkPtEyvzOeBXzb6vvaiP+4DrYu+NMJrr0mZfWx3veTfwzsS2p4AVsffjgJeA98be/xH4\n81iZGdGxTmr2Pe1FXRwa3cNc1QXPAn8xGusAOAjYAiwE/g34wmj7+0D4Are5wv7RUg+fBzZWKdOQ\nulDLy8DUahHJzDCzfQnfOn+Y3+bhb9kGRtGClmZ2FOFbVrwengPup1APJxJa5eJltgBPku26Gk9o\nidoBo7MuzKzFzN4PHADcMxrrgDBVxb+6+4/iG0dhXUyLupa3mtlqC1N9jLZ6eAfwkJl9O+pa3hzv\nXWhkXSh4GZhqi0gOtEyWHAq0MrQFLUeSyYQHeKV6mAS8HP0jLVcmU8zMCE27P/XCRI6jpi7M7I1m\n9jzhG+KXCN8StzCK6gAgCtyOJ0zwmTSa6uI+QqpAB/BXwFHAXWZ2IKOrHl5P6GXYQpjJ/svA9Wa2\nLNrfsLpoyKrSzWBDWERSRPb4EmHSx7c0+0Ka5NfAcYTW1f8BfMPCZJijhpn9CSGAXeTurzT7eprJ\niyc0/aWZPQD8Bngv4e/KaNECPODRLPjALyxMJPtXwG3lP1afCxmprgaOqfCaSWwRySrKLeyY3zfQ\nMoQc4OgAAALTSURBVFnyDGFZh6EsaDmSbCPk+lSqh23AGAvLWZQrkxlmdgPQCbzV3f8jtmvU1IW7\nv+ruj7n7z9z9fxISVZcziuqA0G18GLDZzF4xs1eABcByM3uZ8E15tNRFEQ9r6+WAqYyuvxP/ATya\n2PYocET0e8PqYsQGL+7+rLvnqrxerX4kICzg+KbEKJu0RSTnm1lrokx8EcnMiL5pbSKsFQXs6Uo4\nlTottDUcufvjhH9Q8XoYRxiRk6+HTcCriTIzCP+gKy7+OdxEgcufAW9z9yfj+0ZbXSS0AGNHWR1s\nIKwndzyhFeo44CFgNXCcuz/G6KmLImZ2ECFweWqU/Z24m5BcGzeD0ArV2P8jmp29PBxewBTCP8z/\nRQhI8v9QD4z2txC+ed0JvJnQ77kduCJ2jHGELOtbCc3t7wP+C/hQs+9vL+rlvcCLwAcIrVU3EUZd\nHNbsa6vxfR4Y/XkfT8h4vzh6PyXaf2l03+8g/Gf+z4T1scbEjvElwjD5txK+sd4N/KTZ9zbIevgS\nYQTdPMK3oPxrv1iZEV8XwN9FdfA64I2EUYOvAgtHSx1UqJvkaKNRURfAVYQhva8D/pSwWPB2YOIo\nq4cTCXlgnwSOBpYCzwPvb/TfiaZXxnB4AV8jdJEkX/NjZaYA3ycEJNsJq1y3JI7zRsJq2i8SMqc/\n1ux7q0HdfBR4gjDU7V7gxGZfUx3ucQEhaEn++X81VuYyQnD6ImEU2dTEMcYS5kh5JvrH/E/A4c2+\nt0HWQ1od9AMfSJQb0XUBfIXQpfwS4VvkeqLAZbTUQYW6+RGx4GW01AXQTZgm4qXo//ZvAUeNtnqI\n7qMTeDi6z38HzkkpU/e60MKMIiIikikjNudFRERERiYFLyIiIpIpCl5EREQkUxS8iIiISKYoeBER\nEZFMUfAiIiIimaLgRURERDJFwYuIiIhkioIXERERyRQFLyIiIpIpCl5EREQkU/4/CoPodE/K4pMA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc07f7e5b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data2.powerPS,data2.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc07dfd8390>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mvWY2NpbmRGA78ARS9bSmyeDV4+ygNGZV5XI5tm7dDNxE4fiHG9m6dbNeazVI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\nkUz4/1+JgsLHZyNeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc07e0c1610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data2.kilometer,data2.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import KFold   #For K-fold cross validation\n",
    "\n",
    "def linearRegModel(model, data, predictors, outcome):\n",
    "    #Fit the model:\n",
    "    model.fit(data[predictors],data[outcome])\n",
    "  \n",
    "     \n",
    "    predictions = model.predict(data[predictors])\n",
    "  \n",
    "    #Print accuracy\n",
    "    mse = metrics.mean_squared_error(predictions, data[outcome]) \n",
    "    #accuracy = metrics.accuracy_score(predictions,data[outcome])\n",
    "    print \"mse  \" \n",
    "    print (mse)\n",
    "    \n",
    "    \n",
    "    kf = KFold(data.shape[0], n_folds=5)\n",
    "    mapeError = []\n",
    "    error = []\n",
    "    errorPercent = []\n",
    "    for train, test in kf:\n",
    "        \n",
    "        \n",
    "        # Filter training data\n",
    "        train_predictors = (data[predictors].iloc[train,:])\n",
    "    \n",
    "        # The target we're using to train the algorithm.\n",
    "        train_target = data[outcome].iloc[train]\n",
    "    \n",
    "        # Training the algorithm using the predictors and target.\n",
    "        model.fit(train_predictors,train_target)\n",
    "  \n",
    "        #Make predictions on training set:\n",
    "        predictions = model.predict(data[predictors])\n",
    "        \n",
    "        #errorPercent.append(abs(predictions[test] - data[outcome][test])/(data[outcome][test]))\n",
    "        \n",
    "        error.append(metrics.mean_squared_error(data[outcome][test],predictions[test]) )\n",
    "        mapeError.append(metrics.mean_absolute_error(predictions, data[outcome]))\n",
    "\n",
    "    print(\"Cross-Validation RMSE Score : \")\n",
    "    print(np.mean(error))\n",
    "    \n",
    "    print(\"Cross-Validation MAPE Score : \")\n",
    "    print(np.mean(mapeError))\n",
    "   \n",
    "    \n",
    "    model.fit(data[predictors],data[outcome]) \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse  \n",
      "1.28727686388e+13\n",
      "Cross-Validation RMSE Score : \n",
      "1.28741347864e+13\n",
      "Cross-Validation MAPE Score : \n",
      "24292.0159822\n",
      "coefficients are\n",
      "[ -8.36169688e+00   1.27808197e+02  -5.13308721e-02]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "outcome_var = 'price'\n",
    "model = LinearRegression()\n",
    "predictor_var = ['yearOfRegistration','powerPS','kilometer']\n",
    "#predictor_var = ['NOX']\n",
    "linearRegModel(model, data, predictor_var,outcome_var)\n",
    "m = model.coef_\n",
    "print(\"coefficients are\")\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "privat        371525\n",
      "gewerblich         3\n",
      "Name: seller, dtype: int64\n",
      "Angebot    371516\n",
      "Gesuch         12\n",
      "Name: offerType, dtype: int64\n",
      "limousine     95894\n",
      "kleinwagen    80023\n",
      "kombi         67564\n",
      "bus           30201\n",
      "cabrio        22898\n",
      "coupe         19015\n",
      "suv           14707\n",
      "andere         3357\n",
      "Name: vehicleType, dtype: int64\n",
      "test       192585\n",
      "control    178943\n",
      "Name: abtest, dtype: int64\n",
      "manuell      274214\n",
      "automatik     77105\n",
      "Name: gearbox, dtype: int64\n",
      "golf                  30070\n",
      "andere                26400\n",
      "3er                   20567\n",
      "polo                  13092\n",
      "corsa                 12573\n",
      "astra                 10830\n",
      "passat                10306\n",
      "a4                    10257\n",
      "c_klasse               8775\n",
      "5er                    8546\n",
      "e_klasse               7561\n",
      "a3                     6604\n",
      "a6                     6023\n",
      "focus                  5950\n",
      "fiesta                 5775\n",
      "transporter            5528\n",
      "twingo                 4953\n",
      "2_reihe                4816\n",
      "fortwo                 4338\n",
      "a_klasse               4316\n",
      "vectra                 4255\n",
      "1er                    3837\n",
      "mondeo                 3626\n",
      "clio                   3553\n",
      "touran                 3540\n",
      "3_reihe                3503\n",
      "punto                  3349\n",
      "zafira                 3049\n",
      "megane                 2927\n",
      "ibiza                  2702\n",
      "                      ...  \n",
      "mii                      81\n",
      "crossfire                66\n",
      "range_rover_evoque       65\n",
      "gl                       64\n",
      "nubira                   62\n",
      "move                     60\n",
      "lybra                    54\n",
      "145                      50\n",
      "v60                      48\n",
      "croma                    45\n",
      "amarok                   42\n",
      "delta                    40\n",
      "terios                   35\n",
      "lodgy                    35\n",
      "9000                     31\n",
      "charade                  27\n",
      "b_max                    26\n",
      "musa                     25\n",
      "materia                  18\n",
      "200                      18\n",
      "kappa                    15\n",
      "samara                   13\n",
      "elefantino               11\n",
      "i3                       10\n",
      "kalina                    9\n",
      "serie_2                   8\n",
      "rangerover                6\n",
      "serie_3                   4\n",
      "serie_1                   2\n",
      "discovery_sport           1\n",
      "Name: model, dtype: int64\n",
      "benzin     223857\n",
      "diesel     107746\n",
      "lpg          5378\n",
      "cng           571\n",
      "hybrid        278\n",
      "andere        208\n",
      "elektro       104\n",
      "Name: fuelType, dtype: int64\n",
      "nein    263182\n",
      "ja       36286\n",
      "Name: notRepairedDamage, dtype: int64\n",
      "volkswagen        79640\n",
      "bmw               40274\n",
      "opel              40136\n",
      "mercedes_benz     35309\n",
      "audi              32873\n",
      "ford              25573\n",
      "renault           17969\n",
      "peugeot           11027\n",
      "fiat               9676\n",
      "seat               7022\n",
      "mazda              5695\n",
      "skoda              5641\n",
      "smart              5249\n",
      "citroen            5182\n",
      "nissan             5037\n",
      "toyota             4694\n",
      "sonstige_autos     3982\n",
      "hyundai            3646\n",
      "mini               3394\n",
      "volvo              3327\n",
      "mitsubishi         3061\n",
      "honda              2836\n",
      "kia                2555\n",
      "alfa_romeo         2345\n",
      "suzuki             2328\n",
      "porsche            2215\n",
      "chevrolet          1845\n",
      "chrysler           1452\n",
      "dacia               900\n",
      "jeep                807\n",
      "daihatsu            806\n",
      "subaru              779\n",
      "land_rover          770\n",
      "jaguar              621\n",
      "trabant             591\n",
      "daewoo              542\n",
      "saab                530\n",
      "rover               490\n",
      "lancia              484\n",
      "lada                225\n",
      "Name: brand, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print data['seller'].value_counts()   #drop seller\n",
    "print data['offerType'].value_counts()   #drop offertype\n",
    "print data['vehicleType'].value_counts()   #keep\n",
    "print data['abtest'].value_counts()   #keep\n",
    "print data['gearbox'].value_counts()   #keep\n",
    "print data['model'].value_counts()   #keep\n",
    "print data['fuelType'].value_counts()   #keep \n",
    "print data['notRepairedDamage'].value_counts()   #keep fill NaNs with YES\n",
    "print data['brand'].value_counts()   #keep     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.drop('seller',axis = 1)\n",
    "data = data.drop('offerType',axis = 1)\n",
    "data = data.drop('nrOfPictures',axis = 1)\n",
    "data = data.drop('postalCode',axis = 1)\n",
    "data = data.drop('lastSeen',axis = 1)\n",
    "data = data.drop('dateCreated',axis = 1)\n",
    "data  = data.drop('monthOfRegistration',axis  = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateCrawled</th>\n",
       "      <th>name</th>\n",
       "      <th>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>fuelType</th>\n",
       "      <th>brand</th>\n",
       "      <th>notRepairedDamage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:17</td>\n",
       "      <td>Golf_3_1.6</td>\n",
       "      <td>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>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:45</td>\n",
       "      <td>A5_Sportback_2.7_Tdi</td>\n",
       "      <td>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>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:21</td>\n",
       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
       "      <td>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>diesel</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:04</td>\n",
       "      <td>GOLF_4_1_4__3T�RER</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>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:20</td>\n",
       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
       "      <td>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>diesel</td>\n",
       "      <td>skoda</td>\n",
       "      <td>nein</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dateCrawled                            name  price abtest  \\\n",
       "0  2016-03-24 11:52:17                      Golf_3_1.6    480   test   \n",
       "1  2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  18300   test   \n",
       "2  2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"   9800   test   \n",
       "3  2016-03-17 16:54:04              GOLF_4_1_4__3T�RER   1500   test   \n",
       "4  2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic   3600   test   \n",
       "\n",
       "  vehicleType  yearOfRegistration    gearbox  powerPS  model  kilometer  \\\n",
       "0         NaN                1993    manuell        0   golf     150000   \n",
       "1       coupe                2011    manuell      190    NaN     125000   \n",
       "2         suv                2004  automatik      163  grand     125000   \n",
       "3  kleinwagen                2001    manuell       75   golf     150000   \n",
       "4  kleinwagen                2008    manuell       69  fabia      90000   \n",
       "\n",
       "  fuelType       brand notRepairedDamage  \n",
       "0   benzin  volkswagen               NaN  \n",
       "1   diesel        audi                ja  \n",
       "2   diesel        jeep               NaN  \n",
       "3   benzin  volkswagen              nein  \n",
       "4   diesel       skoda              nein  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOW trying with RandomForestRegression: \n",
      "mse  \n",
      "9.04815347366e+12\n",
      "Cross-Validation RMSE Score : \n",
      "1.4296694277e+13\n",
      "Cross-Validation MAPE Score : \n",
      "17389.2344205\n"
     ]
    }
   ],
   "source": [
    "print 'NOW trying with RandomForestRegression: '\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "outcome_var = 'price'\n",
    "predictor_var = ['yearOfRegistration','powerPS','kilometer']\n",
    "regressor = RandomForestRegressor(n_estimators= 6 , min_samples_split= 20)\n",
    "linearRegModel(regressor, data, predictor_var,outcome_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input predictor_var yearOfRegistration powerPS kilometer\n",
      "year: 2014\n",
      "power: 120\n",
      "kms travelled: 12000\n",
      "initial cost:1200\n",
      "linear reg ans  [ 23617.31763974]\n",
      "randome forest regressor  reg ans  [ 17418.40371031]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fsftn/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n",
      "/home/fsftn/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "print 'input predictor_var yearOfRegistration','powerPS','kilometer' \n",
    "yearOfRegistration = float(raw_input(\"year: \"))\n",
    "powerPS = float(raw_input(\"power: \"))\n",
    "kilometer = float(raw_input(\"kms travelled: \"))\n",
    "init=float(raw_input(\"initial cost:\"))\n",
    "ans =  model.predict([yearOfRegistration,powerPS,kilometer])\n",
    "print 'linear reg ans ',ans\n",
    "ans =  regressor.predict([yearOfRegistration,powerPS,kilometer])\n",
    "print 'randome forest regressor  reg ans ',ans"
   ]
  }
 ],
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