{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efb19021",
   "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>id</th>\n",
       "      <th>url</th>\n",
       "      <th>region</th>\n",
       "      <th>region_url</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>VIN</th>\n",
       "      <th>drive</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>image_url</th>\n",
       "      <th>description</th>\n",
       "      <th>county</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>posting_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7222695916</td>\n",
       "      <td>https://prescott.craigslist.org/cto/d/prescott...</td>\n",
       "      <td>prescott</td>\n",
       "      <td>https://prescott.craigslist.org</td>\n",
       "      <td>6000</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>az</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7218891961</td>\n",
       "      <td>https://fayar.craigslist.org/ctd/d/bentonville...</td>\n",
       "      <td>fayetteville</td>\n",
       "      <td>https://fayar.craigslist.org</td>\n",
       "      <td>11900</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7221797935</td>\n",
       "      <td>https://keys.craigslist.org/cto/d/summerland-k...</td>\n",
       "      <td>florida keys</td>\n",
       "      <td>https://keys.craigslist.org</td>\n",
       "      <td>21000</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7222270760</td>\n",
       "      <td>https://worcester.craigslist.org/cto/d/west-br...</td>\n",
       "      <td>worcester / central MA</td>\n",
       "      <td>https://worcester.craigslist.org</td>\n",
       "      <td>1500</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7210384030</td>\n",
       "      <td>https://greensboro.craigslist.org/cto/d/trinit...</td>\n",
       "      <td>greensboro</td>\n",
       "      <td>https://greensboro.craigslist.org</td>\n",
       "      <td>4900</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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426875</th>\n",
       "      <td>7301591192</td>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/atlanta-2...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>23590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>maxima s sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>32226.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>1N4AA6AV6KC367801</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00o0o_iiraFnHg8q...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wy</td>\n",
       "      <td>33.786500</td>\n",
       "      <td>-84.445400</td>\n",
       "      <td>2021-04-04T03:21:31-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426876</th>\n",
       "      <td>7301591187</td>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/atlanta-2...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>30590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>s60 t5 momentum sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>7JR102FKXLG042696</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>red</td>\n",
       "      <td>https://images.craigslist.org/00x0x_15sbgnxCIS...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wy</td>\n",
       "      <td>33.786500</td>\n",
       "      <td>-84.445400</td>\n",
       "      <td>2021-04-04T03:21:29-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426877</th>\n",
       "      <td>7301591147</td>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/atlanta-2...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>34990</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>xt4 sport suv 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>diesel</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>1GYFZFR46LF088296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/00L0L_farM7bxnxR...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wy</td>\n",
       "      <td>33.779214</td>\n",
       "      <td>-84.411811</td>\n",
       "      <td>2021-04-04T03:21:17-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426878</th>\n",
       "      <td>7301591140</td>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/atlanta-2...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>28990</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>lexus</td>\n",
       "      <td>es 350 sedan 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>30112.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>58ABK1GG4JU103853</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>silver</td>\n",
       "      <td>https://images.craigslist.org/00z0z_bKnIVGLkDT...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wy</td>\n",
       "      <td>33.786500</td>\n",
       "      <td>-84.445400</td>\n",
       "      <td>2021-04-04T03:21:11-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426879</th>\n",
       "      <td>7301591129</td>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/atlanta-2...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>30590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>4 series 430i gran coupe</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>22716.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>WBA4J1C58KBM14708</td>\n",
       "      <td>rwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>coupe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00Y0Y_lEUocjyRxa...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wy</td>\n",
       "      <td>33.779214</td>\n",
       "      <td>-84.411811</td>\n",
       "      <td>2021-04-04T03:21:07-0600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>426880 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id                                                url  \\\n",
       "0       7222695916  https://prescott.craigslist.org/cto/d/prescott...   \n",
       "1       7218891961  https://fayar.craigslist.org/ctd/d/bentonville...   \n",
       "2       7221797935  https://keys.craigslist.org/cto/d/summerland-k...   \n",
       "3       7222270760  https://worcester.craigslist.org/cto/d/west-br...   \n",
       "4       7210384030  https://greensboro.craigslist.org/cto/d/trinit...   \n",
       "...            ...                                                ...   \n",
       "426875  7301591192  https://wyoming.craigslist.org/ctd/d/atlanta-2...   \n",
       "426876  7301591187  https://wyoming.craigslist.org/ctd/d/atlanta-2...   \n",
       "426877  7301591147  https://wyoming.craigslist.org/ctd/d/atlanta-2...   \n",
       "426878  7301591140  https://wyoming.craigslist.org/ctd/d/atlanta-2...   \n",
       "426879  7301591129  https://wyoming.craigslist.org/ctd/d/atlanta-2...   \n",
       "\n",
       "                        region                         region_url  price  \\\n",
       "0                     prescott    https://prescott.craigslist.org   6000   \n",
       "1                 fayetteville       https://fayar.craigslist.org  11900   \n",
       "2                 florida keys        https://keys.craigslist.org  21000   \n",
       "3       worcester / central MA   https://worcester.craigslist.org   1500   \n",
       "4                   greensboro  https://greensboro.craigslist.org   4900   \n",
       "...                        ...                                ...    ...   \n",
       "426875                 wyoming     https://wyoming.craigslist.org  23590   \n",
       "426876                 wyoming     https://wyoming.craigslist.org  30590   \n",
       "426877                 wyoming     https://wyoming.craigslist.org  34990   \n",
       "426878                 wyoming     https://wyoming.craigslist.org  28990   \n",
       "426879                 wyoming     https://wyoming.craigslist.org  30590   \n",
       "\n",
       "          year manufacturer                     model condition    cylinders  \\\n",
       "0          NaN          NaN                       NaN       NaN          NaN   \n",
       "1          NaN          NaN                       NaN       NaN          NaN   \n",
       "2          NaN          NaN                       NaN       NaN          NaN   \n",
       "3          NaN          NaN                       NaN       NaN          NaN   \n",
       "4          NaN          NaN                       NaN       NaN          NaN   \n",
       "...        ...          ...                       ...       ...          ...   \n",
       "426875  2019.0       nissan         maxima s sedan 4d      good  6 cylinders   \n",
       "426876  2020.0        volvo  s60 t5 momentum sedan 4d      good          NaN   \n",
       "426877  2020.0     cadillac          xt4 sport suv 4d      good          NaN   \n",
       "426878  2018.0        lexus           es 350 sedan 4d      good  6 cylinders   \n",
       "426879  2019.0          bmw  4 series 430i gran coupe      good          NaN   \n",
       "\n",
       "          fuel  odometer title_status transmission                VIN drive  \\\n",
       "0          NaN       NaN          NaN          NaN                NaN   NaN   \n",
       "1          NaN       NaN          NaN          NaN                NaN   NaN   \n",
       "2          NaN       NaN          NaN          NaN                NaN   NaN   \n",
       "3          NaN       NaN          NaN          NaN                NaN   NaN   \n",
       "4          NaN       NaN          NaN          NaN                NaN   NaN   \n",
       "...        ...       ...          ...          ...                ...   ...   \n",
       "426875     gas   32226.0        clean        other  1N4AA6AV6KC367801   fwd   \n",
       "426876     gas   12029.0        clean        other  7JR102FKXLG042696   fwd   \n",
       "426877  diesel    4174.0        clean        other  1GYFZFR46LF088296   NaN   \n",
       "426878     gas   30112.0        clean        other  58ABK1GG4JU103853   fwd   \n",
       "426879     gas   22716.0        clean        other  WBA4J1C58KBM14708   rwd   \n",
       "\n",
       "       size       type paint_color  \\\n",
       "0       NaN        NaN         NaN   \n",
       "1       NaN        NaN         NaN   \n",
       "2       NaN        NaN         NaN   \n",
       "3       NaN        NaN         NaN   \n",
       "4       NaN        NaN         NaN   \n",
       "...     ...        ...         ...   \n",
       "426875  NaN      sedan         NaN   \n",
       "426876  NaN      sedan         red   \n",
       "426877  NaN  hatchback       white   \n",
       "426878  NaN      sedan      silver   \n",
       "426879  NaN      coupe         NaN   \n",
       "\n",
       "                                                image_url  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "426875  https://images.craigslist.org/00o0o_iiraFnHg8q...   \n",
       "426876  https://images.craigslist.org/00x0x_15sbgnxCIS...   \n",
       "426877  https://images.craigslist.org/00L0L_farM7bxnxR...   \n",
       "426878  https://images.craigslist.org/00z0z_bKnIVGLkDT...   \n",
       "426879  https://images.craigslist.org/00Y0Y_lEUocjyRxa...   \n",
       "\n",
       "                                              description  county state  \\\n",
       "0                                                     NaN     NaN    az   \n",
       "1                                                     NaN     NaN    ar   \n",
       "2                                                     NaN     NaN    fl   \n",
       "3                                                     NaN     NaN    ma   \n",
       "4                                                     NaN     NaN    nc   \n",
       "...                                                   ...     ...   ...   \n",
       "426875  Carvana is the safer way to buy a car During t...     NaN    wy   \n",
       "426876  Carvana is the safer way to buy a car During t...     NaN    wy   \n",
       "426877  Carvana is the safer way to buy a car During t...     NaN    wy   \n",
       "426878  Carvana is the safer way to buy a car During t...     NaN    wy   \n",
       "426879  Carvana is the safer way to buy a car During t...     NaN    wy   \n",
       "\n",
       "              lat       long              posting_date  \n",
       "0             NaN        NaN                       NaN  \n",
       "1             NaN        NaN                       NaN  \n",
       "2             NaN        NaN                       NaN  \n",
       "3             NaN        NaN                       NaN  \n",
       "4             NaN        NaN                       NaN  \n",
       "...           ...        ...                       ...  \n",
       "426875  33.786500 -84.445400  2021-04-04T03:21:31-0600  \n",
       "426876  33.786500 -84.445400  2021-04-04T03:21:29-0600  \n",
       "426877  33.779214 -84.411811  2021-04-04T03:21:17-0600  \n",
       "426878  33.786500 -84.445400  2021-04-04T03:21:11-0600  \n",
       "426879  33.779214 -84.411811  2021-04-04T03:21:07-0600  \n",
       "\n",
       "[426880 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing and configuring CSV.\n",
    "#Dataset from Kaggle: https://www.kaggle.com/datasets/austinreese/craigslist-carstrucks-data?resource=download\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df = pd.read_csv('vehicles.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "62e0eb72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting only relevant features for this study\n",
    "df = df[['price', 'year', 'odometer', 'manufacturer','condition', 'title_status', 'type', 'state']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "77d233bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 426880 entries, 0 to 426879\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   price         426880 non-null  int64  \n",
      " 1   year          425675 non-null  float64\n",
      " 2   odometer      422480 non-null  float64\n",
      " 3   manufacturer  409234 non-null  object \n",
      " 4   condition     252776 non-null  object \n",
      " 5   title_status  418638 non-null  object \n",
      " 6   type          334022 non-null  object \n",
      " 7   state         426880 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 26.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b10dfdd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lots of missing data in categories. We'll encode \"missing\" into the empty values later on in the project and see how the model performs without the data.\n",
    "#Data will now be visualized to see outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8ef6035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Price\n",
    "df['price'].plot(kind='hist', bins=50)\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "746bba65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Looks like we have some junk prices that need to be filtered out, we'll use quantile to filter out the high end and will make the floor $1,000 to remove the free entries.\n",
    "prices = df[(df['price'] > 1000) & (df['price'] <= df['price'].quantile(0.99))]\n",
    "prices['price'].plot(kind='hist', bins=50)\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc8cd3d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Looks better, now the dataset will be updated. \n",
    "\n",
    "df = df[(df['price'] > 1000) & (df['price'] <= df['price'].quantile(0.99))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2e8f787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['year'].plot(kind='hist', bins=50)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Vehicle Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2c8de69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#A few outliers, but this could include classic cars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52bcdcca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mileage\n",
    "df['odometer'].plot(kind='hist', bins=50)\n",
    "plt.xlabel('Miles')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Vehicle Mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb129eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#More outliers.\n",
    "quantile = df['odometer'].quantile(0.99)\n",
    "miles = df[(df['odometer'] >= (0)) & (df['odometer'] <= (quantile))]\n",
    "miles['odometer'].plot(kind='hist', bins=50)\n",
    "plt.xlabel('Miles')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Vehicle Mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "391dfa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['odometer'] >= (0)) & (df['odometer'] <= (quantile))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3cb17ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e96fe9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lots of missing values were spotted initially, let's visualize.\n",
    "missing = df['manufacturer'].fillna('MISSING').value_counts()\n",
    "\n",
    "missing.sort_values().plot(kind='barh')\n",
    "plt.xlabel('Number of listings')\n",
    "plt.ylabel('Manufacturer')\n",
    "plt.title('Listings by manufacturer')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52f904ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#These values will be one-hot encoded later to have the unknown values be ignored when training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6556429",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fixing missing values\n",
    "features = ['manufacturer', 'condition', 'title_status', 'type', 'state']\n",
    "\n",
    "df[features] = df[features].fillna('unknown')\n",
    "\n",
    "#Removing Duplicates\n",
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b136838a",
   "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>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>title_status</th>\n",
       "      <th>type</th>\n",
       "      <th>state</th>\n",
       "      <th>Condition_excellent</th>\n",
       "      <th>Condition_fair</th>\n",
       "      <th>Condition_good</th>\n",
       "      <th>Condition_like new</th>\n",
       "      <th>Condition_new</th>\n",
       "      <th>Condition_salvage</th>\n",
       "      <th>Condition_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33590</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>clean</td>\n",
       "      <td>pickup</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>22590</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>clean</td>\n",
       "      <td>pickup</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>19160.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>clean</td>\n",
       "      <td>pickup</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30990</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>clean</td>\n",
       "      <td>pickup</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15000</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>clean</td>\n",
       "      <td>truck</td>\n",
       "      <td>al</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426875</th>\n",
       "      <td>23590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>32226.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>clean</td>\n",
       "      <td>sedan</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426876</th>\n",
       "      <td>30590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>clean</td>\n",
       "      <td>sedan</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426877</th>\n",
       "      <td>34990</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>clean</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426878</th>\n",
       "      <td>28990</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>30112.0</td>\n",
       "      <td>lexus</td>\n",
       "      <td>clean</td>\n",
       "      <td>sedan</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426879</th>\n",
       "      <td>30590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>22716.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>clean</td>\n",
       "      <td>coupe</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259125 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price    year  odometer manufacturer title_status       type state  \\\n",
       "27      33590  2014.0   57923.0          gmc        clean     pickup    al   \n",
       "28      22590  2010.0   71229.0    chevrolet        clean     pickup    al   \n",
       "29      39590  2020.0   19160.0    chevrolet        clean     pickup    al   \n",
       "30      30990  2017.0   41124.0       toyota        clean     pickup    al   \n",
       "31      15000  2013.0  128000.0         ford        clean      truck    al   \n",
       "...       ...     ...       ...          ...          ...        ...   ...   \n",
       "426875  23590  2019.0   32226.0       nissan        clean      sedan    wy   \n",
       "426876  30590  2020.0   12029.0        volvo        clean      sedan    wy   \n",
       "426877  34990  2020.0    4174.0     cadillac        clean  hatchback    wy   \n",
       "426878  28990  2018.0   30112.0        lexus        clean      sedan    wy   \n",
       "426879  30590  2019.0   22716.0          bmw        clean      coupe    wy   \n",
       "\n",
       "        Condition_excellent  Condition_fair  Condition_good  \\\n",
       "27                        0               0               1   \n",
       "28                        0               0               1   \n",
       "29                        0               0               1   \n",
       "30                        0               0               1   \n",
       "31                        1               0               0   \n",
       "...                     ...             ...             ...   \n",
       "426875                    0               0               1   \n",
       "426876                    0               0               1   \n",
       "426877                    0               0               1   \n",
       "426878                    0               0               1   \n",
       "426879                    0               0               1   \n",
       "\n",
       "        Condition_like new  Condition_new  Condition_salvage  \\\n",
       "27                       0              0                  0   \n",
       "28                       0              0                  0   \n",
       "29                       0              0                  0   \n",
       "30                       0              0                  0   \n",
       "31                       0              0                  0   \n",
       "...                    ...            ...                ...   \n",
       "426875                   0              0                  0   \n",
       "426876                   0              0                  0   \n",
       "426877                   0              0                  0   \n",
       "426878                   0              0                  0   \n",
       "426879                   0              0                  0   \n",
       "\n",
       "        Condition_unknown  \n",
       "27                      0  \n",
       "28                      0  \n",
       "29                      0  \n",
       "30                      0  \n",
       "31                      0  \n",
       "...                   ...  \n",
       "426875                  0  \n",
       "426876                  0  \n",
       "426877                  0  \n",
       "426878                  0  \n",
       "426879                  0  \n",
       "\n",
       "[259125 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One Hot Encoding (Condition)\n",
    "encoded_condition = pd.get_dummies(df['condition'], prefix='Condition', dtype=int)\n",
    "df = df.drop('condition', axis=1)\n",
    "df = pd.concat([df, encoded_condition], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb15cc98",
   "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>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>type</th>\n",
       "      <th>state</th>\n",
       "      <th>Condition_excellent</th>\n",
       "      <th>Condition_fair</th>\n",
       "      <th>Condition_good</th>\n",
       "      <th>Condition_like new</th>\n",
       "      <th>Condition_new</th>\n",
       "      <th>Condition_salvage</th>\n",
       "      <th>Condition_unknown</th>\n",
       "      <th>Title Status_clean</th>\n",
       "      <th>Title Status_lien</th>\n",
       "      <th>Title Status_missing</th>\n",
       "      <th>Title Status_parts only</th>\n",
       "      <th>Title Status_rebuilt</th>\n",
       "      <th>Title Status_salvage</th>\n",
       "      <th>Title Status_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33590</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>pickup</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>22590</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>19160.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30990</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>pickup</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15000</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>truck</td>\n",
       "      <td>al</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <th>426875</th>\n",
       "      <td>23590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>32226.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>sedan</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426876</th>\n",
       "      <td>30590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426877</th>\n",
       "      <td>34990</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426878</th>\n",
       "      <td>28990</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>30112.0</td>\n",
       "      <td>lexus</td>\n",
       "      <td>sedan</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426879</th>\n",
       "      <td>30590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>22716.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>coupe</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259125 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price    year  odometer manufacturer       type state  \\\n",
       "27      33590  2014.0   57923.0          gmc     pickup    al   \n",
       "28      22590  2010.0   71229.0    chevrolet     pickup    al   \n",
       "29      39590  2020.0   19160.0    chevrolet     pickup    al   \n",
       "30      30990  2017.0   41124.0       toyota     pickup    al   \n",
       "31      15000  2013.0  128000.0         ford      truck    al   \n",
       "...       ...     ...       ...          ...        ...   ...   \n",
       "426875  23590  2019.0   32226.0       nissan      sedan    wy   \n",
       "426876  30590  2020.0   12029.0        volvo      sedan    wy   \n",
       "426877  34990  2020.0    4174.0     cadillac  hatchback    wy   \n",
       "426878  28990  2018.0   30112.0        lexus      sedan    wy   \n",
       "426879  30590  2019.0   22716.0          bmw      coupe    wy   \n",
       "\n",
       "        Condition_excellent  Condition_fair  Condition_good  \\\n",
       "27                        0               0               1   \n",
       "28                        0               0               1   \n",
       "29                        0               0               1   \n",
       "30                        0               0               1   \n",
       "31                        1               0               0   \n",
       "...                     ...             ...             ...   \n",
       "426875                    0               0               1   \n",
       "426876                    0               0               1   \n",
       "426877                    0               0               1   \n",
       "426878                    0               0               1   \n",
       "426879                    0               0               1   \n",
       "\n",
       "        Condition_like new  Condition_new  Condition_salvage  \\\n",
       "27                       0              0                  0   \n",
       "28                       0              0                  0   \n",
       "29                       0              0                  0   \n",
       "30                       0              0                  0   \n",
       "31                       0              0                  0   \n",
       "...                    ...            ...                ...   \n",
       "426875                   0              0                  0   \n",
       "426876                   0              0                  0   \n",
       "426877                   0              0                  0   \n",
       "426878                   0              0                  0   \n",
       "426879                   0              0                  0   \n",
       "\n",
       "        Condition_unknown  Title Status_clean  Title Status_lien  \\\n",
       "27                      0                   1                  0   \n",
       "28                      0                   1                  0   \n",
       "29                      0                   1                  0   \n",
       "30                      0                   1                  0   \n",
       "31                      0                   1                  0   \n",
       "...                   ...                 ...                ...   \n",
       "426875                  0                   1                  0   \n",
       "426876                  0                   1                  0   \n",
       "426877                  0                   1                  0   \n",
       "426878                  0                   1                  0   \n",
       "426879                  0                   1                  0   \n",
       "\n",
       "        Title Status_missing  Title Status_parts only  Title Status_rebuilt  \\\n",
       "27                         0                        0                     0   \n",
       "28                         0                        0                     0   \n",
       "29                         0                        0                     0   \n",
       "30                         0                        0                     0   \n",
       "31                         0                        0                     0   \n",
       "...                      ...                      ...                   ...   \n",
       "426875                     0                        0                     0   \n",
       "426876                     0                        0                     0   \n",
       "426877                     0                        0                     0   \n",
       "426878                     0                        0                     0   \n",
       "426879                     0                        0                     0   \n",
       "\n",
       "        Title Status_salvage  Title Status_unknown  \n",
       "27                         0                     0  \n",
       "28                         0                     0  \n",
       "29                         0                     0  \n",
       "30                         0                     0  \n",
       "31                         0                     0  \n",
       "...                      ...                   ...  \n",
       "426875                     0                     0  \n",
       "426876                     0                     0  \n",
       "426877                     0                     0  \n",
       "426878                     0                     0  \n",
       "426879                     0                     0  \n",
       "\n",
       "[259125 rows x 20 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One Hot Encoding (Title)\n",
    "encoded_title_status = pd.get_dummies(df['title_status'], prefix='Title Status', dtype=int)\n",
    "df = df.drop('title_status', axis=1)\n",
    "df = pd.concat([df, encoded_title_status], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "574a0a19",
   "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>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>state</th>\n",
       "      <th>Condition_excellent</th>\n",
       "      <th>Condition_fair</th>\n",
       "      <th>Condition_good</th>\n",
       "      <th>Condition_like new</th>\n",
       "      <th>Condition_new</th>\n",
       "      <th>Condition_salvage</th>\n",
       "      <th>Condition_unknown</th>\n",
       "      <th>Title Status_clean</th>\n",
       "      <th>Title Status_lien</th>\n",
       "      <th>Title Status_missing</th>\n",
       "      <th>Title Status_parts only</th>\n",
       "      <th>Title Status_rebuilt</th>\n",
       "      <th>Title Status_salvage</th>\n",
       "      <th>Title Status_unknown</th>\n",
       "      <th>Vehicle Type_SUV</th>\n",
       "      <th>Vehicle Type_bus</th>\n",
       "      <th>Vehicle Type_convertible</th>\n",
       "      <th>Vehicle Type_coupe</th>\n",
       "      <th>Vehicle Type_hatchback</th>\n",
       "      <th>Vehicle Type_mini-van</th>\n",
       "      <th>Vehicle Type_offroad</th>\n",
       "      <th>Vehicle Type_other</th>\n",
       "      <th>Vehicle Type_pickup</th>\n",
       "      <th>Vehicle Type_sedan</th>\n",
       "      <th>Vehicle Type_truck</th>\n",
       "      <th>Vehicle Type_unknown</th>\n",
       "      <th>Vehicle Type_van</th>\n",
       "      <th>Vehicle Type_wagon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33590</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>22590</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>19160.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30990</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>al</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15000</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>al</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <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",
       "      <th>426875</th>\n",
       "      <td>23590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>32226.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426876</th>\n",
       "      <td>30590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426877</th>\n",
       "      <td>34990</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426878</th>\n",
       "      <td>28990</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>30112.0</td>\n",
       "      <td>lexus</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426879</th>\n",
       "      <td>30590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>22716.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>wy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259125 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price    year  odometer manufacturer state  Condition_excellent  \\\n",
       "27      33590  2014.0   57923.0          gmc    al                    0   \n",
       "28      22590  2010.0   71229.0    chevrolet    al                    0   \n",
       "29      39590  2020.0   19160.0    chevrolet    al                    0   \n",
       "30      30990  2017.0   41124.0       toyota    al                    0   \n",
       "31      15000  2013.0  128000.0         ford    al                    1   \n",
       "...       ...     ...       ...          ...   ...                  ...   \n",
       "426875  23590  2019.0   32226.0       nissan    wy                    0   \n",
       "426876  30590  2020.0   12029.0        volvo    wy                    0   \n",
       "426877  34990  2020.0    4174.0     cadillac    wy                    0   \n",
       "426878  28990  2018.0   30112.0        lexus    wy                    0   \n",
       "426879  30590  2019.0   22716.0          bmw    wy                    0   \n",
       "\n",
       "        Condition_fair  Condition_good  Condition_like new  Condition_new  \\\n",
       "27                   0               1                   0              0   \n",
       "28                   0               1                   0              0   \n",
       "29                   0               1                   0              0   \n",
       "30                   0               1                   0              0   \n",
       "31                   0               0                   0              0   \n",
       "...                ...             ...                 ...            ...   \n",
       "426875               0               1                   0              0   \n",
       "426876               0               1                   0              0   \n",
       "426877               0               1                   0              0   \n",
       "426878               0               1                   0              0   \n",
       "426879               0               1                   0              0   \n",
       "\n",
       "        Condition_salvage  Condition_unknown  Title Status_clean  \\\n",
       "27                      0                  0                   1   \n",
       "28                      0                  0                   1   \n",
       "29                      0                  0                   1   \n",
       "30                      0                  0                   1   \n",
       "31                      0                  0                   1   \n",
       "...                   ...                ...                 ...   \n",
       "426875                  0                  0                   1   \n",
       "426876                  0                  0                   1   \n",
       "426877                  0                  0                   1   \n",
       "426878                  0                  0                   1   \n",
       "426879                  0                  0                   1   \n",
       "\n",
       "        Title Status_lien  Title Status_missing  Title Status_parts only  \\\n",
       "27                      0                     0                        0   \n",
       "28                      0                     0                        0   \n",
       "29                      0                     0                        0   \n",
       "30                      0                     0                        0   \n",
       "31                      0                     0                        0   \n",
       "...                   ...                   ...                      ...   \n",
       "426875                  0                     0                        0   \n",
       "426876                  0                     0                        0   \n",
       "426877                  0                     0                        0   \n",
       "426878                  0                     0                        0   \n",
       "426879                  0                     0                        0   \n",
       "\n",
       "        Title Status_rebuilt  Title Status_salvage  Title Status_unknown  \\\n",
       "27                         0                     0                     0   \n",
       "28                         0                     0                     0   \n",
       "29                         0                     0                     0   \n",
       "30                         0                     0                     0   \n",
       "31                         0                     0                     0   \n",
       "...                      ...                   ...                   ...   \n",
       "426875                     0                     0                     0   \n",
       "426876                     0                     0                     0   \n",
       "426877                     0                     0                     0   \n",
       "426878                     0                     0                     0   \n",
       "426879                     0                     0                     0   \n",
       "\n",
       "        Vehicle Type_SUV  Vehicle Type_bus  Vehicle Type_convertible  \\\n",
       "27                     0                 0                         0   \n",
       "28                     0                 0                         0   \n",
       "29                     0                 0                         0   \n",
       "30                     0                 0                         0   \n",
       "31                     0                 0                         0   \n",
       "...                  ...               ...                       ...   \n",
       "426875                 0                 0                         0   \n",
       "426876                 0                 0                         0   \n",
       "426877                 0                 0                         0   \n",
       "426878                 0                 0                         0   \n",
       "426879                 0                 0                         0   \n",
       "\n",
       "        Vehicle Type_coupe  Vehicle Type_hatchback  Vehicle Type_mini-van  \\\n",
       "27                       0                       0                      0   \n",
       "28                       0                       0                      0   \n",
       "29                       0                       0                      0   \n",
       "30                       0                       0                      0   \n",
       "31                       0                       0                      0   \n",
       "...                    ...                     ...                    ...   \n",
       "426875                   0                       0                      0   \n",
       "426876                   0                       0                      0   \n",
       "426877                   0                       1                      0   \n",
       "426878                   0                       0                      0   \n",
       "426879                   1                       0                      0   \n",
       "\n",
       "        Vehicle Type_offroad  Vehicle Type_other  Vehicle Type_pickup  \\\n",
       "27                         0                   0                    1   \n",
       "28                         0                   0                    1   \n",
       "29                         0                   0                    1   \n",
       "30                         0                   0                    1   \n",
       "31                         0                   0                    0   \n",
       "...                      ...                 ...                  ...   \n",
       "426875                     0                   0                    0   \n",
       "426876                     0                   0                    0   \n",
       "426877                     0                   0                    0   \n",
       "426878                     0                   0                    0   \n",
       "426879                     0                   0                    0   \n",
       "\n",
       "        Vehicle Type_sedan  Vehicle Type_truck  Vehicle Type_unknown  \\\n",
       "27                       0                   0                     0   \n",
       "28                       0                   0                     0   \n",
       "29                       0                   0                     0   \n",
       "30                       0                   0                     0   \n",
       "31                       0                   1                     0   \n",
       "...                    ...                 ...                   ...   \n",
       "426875                   1                   0                     0   \n",
       "426876                   1                   0                     0   \n",
       "426877                   0                   0                     0   \n",
       "426878                   1                   0                     0   \n",
       "426879                   0                   0                     0   \n",
       "\n",
       "        Vehicle Type_van  Vehicle Type_wagon  \n",
       "27                     0                   0  \n",
       "28                     0                   0  \n",
       "29                     0                   0  \n",
       "30                     0                   0  \n",
       "31                     0                   0  \n",
       "...                  ...                 ...  \n",
       "426875                 0                   0  \n",
       "426876                 0                   0  \n",
       "426877                 0                   0  \n",
       "426878                 0                   0  \n",
       "426879                 0                   0  \n",
       "\n",
       "[259125 rows x 33 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One Hot Encoding (Vehicle Type)\n",
    "encoded_type = pd.get_dummies(df['type'], prefix='Vehicle Type', dtype=int)\n",
    "df = df.drop('type', axis=1)\n",
    "df = pd.concat([df, encoded_type], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1247e0fc",
   "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>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>Condition_excellent</th>\n",
       "      <th>Condition_fair</th>\n",
       "      <th>Condition_good</th>\n",
       "      <th>Condition_like new</th>\n",
       "      <th>Condition_new</th>\n",
       "      <th>Condition_salvage</th>\n",
       "      <th>Condition_unknown</th>\n",
       "      <th>Title Status_clean</th>\n",
       "      <th>Title Status_lien</th>\n",
       "      <th>Title Status_missing</th>\n",
       "      <th>Title Status_parts only</th>\n",
       "      <th>Title Status_rebuilt</th>\n",
       "      <th>Title Status_salvage</th>\n",
       "      <th>Title Status_unknown</th>\n",
       "      <th>Vehicle Type_SUV</th>\n",
       "      <th>Vehicle Type_bus</th>\n",
       "      <th>Vehicle Type_convertible</th>\n",
       "      <th>Vehicle Type_coupe</th>\n",
       "      <th>Vehicle Type_hatchback</th>\n",
       "      <th>Vehicle Type_mini-van</th>\n",
       "      <th>Vehicle Type_offroad</th>\n",
       "      <th>Vehicle Type_other</th>\n",
       "      <th>Vehicle Type_pickup</th>\n",
       "      <th>Vehicle Type_sedan</th>\n",
       "      <th>Vehicle Type_truck</th>\n",
       "      <th>Vehicle Type_unknown</th>\n",
       "      <th>Vehicle Type_van</th>\n",
       "      <th>Vehicle Type_wagon</th>\n",
       "      <th>Vehicle State_ak</th>\n",
       "      <th>Vehicle State_al</th>\n",
       "      <th>Vehicle State_ar</th>\n",
       "      <th>Vehicle State_az</th>\n",
       "      <th>Vehicle State_ca</th>\n",
       "      <th>Vehicle State_co</th>\n",
       "      <th>Vehicle State_ct</th>\n",
       "      <th>Vehicle State_dc</th>\n",
       "      <th>Vehicle State_de</th>\n",
       "      <th>Vehicle State_fl</th>\n",
       "      <th>Vehicle State_ga</th>\n",
       "      <th>Vehicle State_hi</th>\n",
       "      <th>Vehicle State_ia</th>\n",
       "      <th>Vehicle State_id</th>\n",
       "      <th>Vehicle State_il</th>\n",
       "      <th>Vehicle State_in</th>\n",
       "      <th>Vehicle State_ks</th>\n",
       "      <th>Vehicle State_ky</th>\n",
       "      <th>Vehicle State_la</th>\n",
       "      <th>Vehicle State_ma</th>\n",
       "      <th>Vehicle State_md</th>\n",
       "      <th>Vehicle State_me</th>\n",
       "      <th>Vehicle State_mi</th>\n",
       "      <th>Vehicle State_mn</th>\n",
       "      <th>Vehicle State_mo</th>\n",
       "      <th>Vehicle State_ms</th>\n",
       "      <th>Vehicle State_mt</th>\n",
       "      <th>Vehicle State_nc</th>\n",
       "      <th>Vehicle State_nd</th>\n",
       "      <th>Vehicle State_ne</th>\n",
       "      <th>Vehicle State_nh</th>\n",
       "      <th>Vehicle State_nj</th>\n",
       "      <th>Vehicle State_nm</th>\n",
       "      <th>Vehicle State_nv</th>\n",
       "      <th>Vehicle State_ny</th>\n",
       "      <th>Vehicle State_oh</th>\n",
       "      <th>Vehicle State_ok</th>\n",
       "      <th>Vehicle State_or</th>\n",
       "      <th>Vehicle State_pa</th>\n",
       "      <th>Vehicle State_ri</th>\n",
       "      <th>Vehicle State_sc</th>\n",
       "      <th>Vehicle State_sd</th>\n",
       "      <th>Vehicle State_tn</th>\n",
       "      <th>Vehicle State_tx</th>\n",
       "      <th>Vehicle State_ut</th>\n",
       "      <th>Vehicle State_va</th>\n",
       "      <th>Vehicle State_vt</th>\n",
       "      <th>Vehicle State_wa</th>\n",
       "      <th>Vehicle State_wi</th>\n",
       "      <th>Vehicle State_wv</th>\n",
       "      <th>Vehicle State_wy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33590</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>22590</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>19160.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30990</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15000</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <th>426875</th>\n",
       "      <td>23590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>32226.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426876</th>\n",
       "      <td>30590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426877</th>\n",
       "      <td>34990</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426878</th>\n",
       "      <td>28990</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>30112.0</td>\n",
       "      <td>lexus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426879</th>\n",
       "      <td>30590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>22716.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259125 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price    year  odometer manufacturer  Condition_excellent  \\\n",
       "27      33590  2014.0   57923.0          gmc                    0   \n",
       "28      22590  2010.0   71229.0    chevrolet                    0   \n",
       "29      39590  2020.0   19160.0    chevrolet                    0   \n",
       "30      30990  2017.0   41124.0       toyota                    0   \n",
       "31      15000  2013.0  128000.0         ford                    1   \n",
       "...       ...     ...       ...          ...                  ...   \n",
       "426875  23590  2019.0   32226.0       nissan                    0   \n",
       "426876  30590  2020.0   12029.0        volvo                    0   \n",
       "426877  34990  2020.0    4174.0     cadillac                    0   \n",
       "426878  28990  2018.0   30112.0        lexus                    0   \n",
       "426879  30590  2019.0   22716.0          bmw                    0   \n",
       "\n",
       "        Condition_fair  Condition_good  Condition_like new  Condition_new  \\\n",
       "27                   0               1                   0              0   \n",
       "28                   0               1                   0              0   \n",
       "29                   0               1                   0              0   \n",
       "30                   0               1                   0              0   \n",
       "31                   0               0                   0              0   \n",
       "...                ...             ...                 ...            ...   \n",
       "426875               0               1                   0              0   \n",
       "426876               0               1                   0              0   \n",
       "426877               0               1                   0              0   \n",
       "426878               0               1                   0              0   \n",
       "426879               0               1                   0              0   \n",
       "\n",
       "        Condition_salvage  Condition_unknown  Title Status_clean  \\\n",
       "27                      0                  0                   1   \n",
       "28                      0                  0                   1   \n",
       "29                      0                  0                   1   \n",
       "30                      0                  0                   1   \n",
       "31                      0                  0                   1   \n",
       "...                   ...                ...                 ...   \n",
       "426875                  0                  0                   1   \n",
       "426876                  0                  0                   1   \n",
       "426877                  0                  0                   1   \n",
       "426878                  0                  0                   1   \n",
       "426879                  0                  0                   1   \n",
       "\n",
       "        Title Status_lien  Title Status_missing  Title Status_parts only  \\\n",
       "27                      0                     0                        0   \n",
       "28                      0                     0                        0   \n",
       "29                      0                     0                        0   \n",
       "30                      0                     0                        0   \n",
       "31                      0                     0                        0   \n",
       "...                   ...                   ...                      ...   \n",
       "426875                  0                     0                        0   \n",
       "426876                  0                     0                        0   \n",
       "426877                  0                     0                        0   \n",
       "426878                  0                     0                        0   \n",
       "426879                  0                     0                        0   \n",
       "\n",
       "        Title Status_rebuilt  Title Status_salvage  Title Status_unknown  \\\n",
       "27                         0                     0                     0   \n",
       "28                         0                     0                     0   \n",
       "29                         0                     0                     0   \n",
       "30                         0                     0                     0   \n",
       "31                         0                     0                     0   \n",
       "...                      ...                   ...                   ...   \n",
       "426875                     0                     0                     0   \n",
       "426876                     0                     0                     0   \n",
       "426877                     0                     0                     0   \n",
       "426878                     0                     0                     0   \n",
       "426879                     0                     0                     0   \n",
       "\n",
       "        Vehicle Type_SUV  Vehicle Type_bus  Vehicle Type_convertible  \\\n",
       "27                     0                 0                         0   \n",
       "28                     0                 0                         0   \n",
       "29                     0                 0                         0   \n",
       "30                     0                 0                         0   \n",
       "31                     0                 0                         0   \n",
       "...                  ...               ...                       ...   \n",
       "426875                 0                 0                         0   \n",
       "426876                 0                 0                         0   \n",
       "426877                 0                 0                         0   \n",
       "426878                 0                 0                         0   \n",
       "426879                 0                 0                         0   \n",
       "\n",
       "        Vehicle Type_coupe  Vehicle Type_hatchback  Vehicle Type_mini-van  \\\n",
       "27                       0                       0                      0   \n",
       "28                       0                       0                      0   \n",
       "29                       0                       0                      0   \n",
       "30                       0                       0                      0   \n",
       "31                       0                       0                      0   \n",
       "...                    ...                     ...                    ...   \n",
       "426875                   0                       0                      0   \n",
       "426876                   0                       0                      0   \n",
       "426877                   0                       1                      0   \n",
       "426878                   0                       0                      0   \n",
       "426879                   1                       0                      0   \n",
       "\n",
       "        Vehicle Type_offroad  Vehicle Type_other  Vehicle Type_pickup  \\\n",
       "27                         0                   0                    1   \n",
       "28                         0                   0                    1   \n",
       "29                         0                   0                    1   \n",
       "30                         0                   0                    1   \n",
       "31                         0                   0                    0   \n",
       "...                      ...                 ...                  ...   \n",
       "426875                     0                   0                    0   \n",
       "426876                     0                   0                    0   \n",
       "426877                     0                   0                    0   \n",
       "426878                     0                   0                    0   \n",
       "426879                     0                   0                    0   \n",
       "\n",
       "        Vehicle Type_sedan  Vehicle Type_truck  Vehicle Type_unknown  \\\n",
       "27                       0                   0                     0   \n",
       "28                       0                   0                     0   \n",
       "29                       0                   0                     0   \n",
       "30                       0                   0                     0   \n",
       "31                       0                   1                     0   \n",
       "...                    ...                 ...                   ...   \n",
       "426875                   1                   0                     0   \n",
       "426876                   1                   0                     0   \n",
       "426877                   0                   0                     0   \n",
       "426878                   1                   0                     0   \n",
       "426879                   0                   0                     0   \n",
       "\n",
       "        Vehicle Type_van  Vehicle Type_wagon  Vehicle State_ak  \\\n",
       "27                     0                   0                 0   \n",
       "28                     0                   0                 0   \n",
       "29                     0                   0                 0   \n",
       "30                     0                   0                 0   \n",
       "31                     0                   0                 0   \n",
       "...                  ...                 ...               ...   \n",
       "426875                 0                   0                 0   \n",
       "426876                 0                   0                 0   \n",
       "426877                 0                   0                 0   \n",
       "426878                 0                   0                 0   \n",
       "426879                 0                   0                 0   \n",
       "\n",
       "        Vehicle State_al  Vehicle State_ar  Vehicle State_az  \\\n",
       "27                     1                 0                 0   \n",
       "28                     1                 0                 0   \n",
       "29                     1                 0                 0   \n",
       "30                     1                 0                 0   \n",
       "31                     1                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ca  Vehicle State_co  Vehicle State_ct  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_dc  Vehicle State_de  Vehicle State_fl  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ga  Vehicle State_hi  Vehicle State_ia  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_id  Vehicle State_il  Vehicle State_in  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ks  Vehicle State_ky  Vehicle State_la  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ma  Vehicle State_md  Vehicle State_me  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_mi  Vehicle State_mn  Vehicle State_mo  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ms  Vehicle State_mt  Vehicle State_nc  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_nd  Vehicle State_ne  Vehicle State_nh  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_nj  Vehicle State_nm  Vehicle State_nv  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ny  Vehicle State_oh  Vehicle State_ok  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_or  Vehicle State_pa  Vehicle State_ri  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_sc  Vehicle State_sd  Vehicle State_tn  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_tx  Vehicle State_ut  Vehicle State_va  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_vt  Vehicle State_wa  Vehicle State_wi  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_wv  Vehicle State_wy  \n",
       "27                     0                 0  \n",
       "28                     0                 0  \n",
       "29                     0                 0  \n",
       "30                     0                 0  \n",
       "31                     0                 0  \n",
       "...                  ...               ...  \n",
       "426875                 0                 1  \n",
       "426876                 0                 1  \n",
       "426877                 0                 1  \n",
       "426878                 0                 1  \n",
       "426879                 0                 1  \n",
       "\n",
       "[259125 rows x 83 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One Hot Encoding (State)\n",
    "encoded_state = pd.get_dummies(df['state'], prefix='Vehicle State', dtype=int)\n",
    "df = df.drop('state', axis=1)\n",
    "df = pd.concat([df, encoded_state], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4fbeb9e4",
   "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>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>Condition_excellent</th>\n",
       "      <th>Condition_fair</th>\n",
       "      <th>Condition_good</th>\n",
       "      <th>Condition_like new</th>\n",
       "      <th>Condition_new</th>\n",
       "      <th>Condition_salvage</th>\n",
       "      <th>Condition_unknown</th>\n",
       "      <th>Title Status_clean</th>\n",
       "      <th>Title Status_lien</th>\n",
       "      <th>Title Status_missing</th>\n",
       "      <th>Title Status_parts only</th>\n",
       "      <th>Title Status_rebuilt</th>\n",
       "      <th>Title Status_salvage</th>\n",
       "      <th>Title Status_unknown</th>\n",
       "      <th>Vehicle Type_SUV</th>\n",
       "      <th>Vehicle Type_bus</th>\n",
       "      <th>Vehicle Type_convertible</th>\n",
       "      <th>Vehicle Type_coupe</th>\n",
       "      <th>Vehicle Type_hatchback</th>\n",
       "      <th>Vehicle Type_mini-van</th>\n",
       "      <th>Vehicle Type_offroad</th>\n",
       "      <th>Vehicle Type_other</th>\n",
       "      <th>Vehicle Type_pickup</th>\n",
       "      <th>Vehicle Type_sedan</th>\n",
       "      <th>Vehicle Type_truck</th>\n",
       "      <th>Vehicle Type_unknown</th>\n",
       "      <th>Vehicle Type_van</th>\n",
       "      <th>Vehicle Type_wagon</th>\n",
       "      <th>Vehicle State_ak</th>\n",
       "      <th>Vehicle State_al</th>\n",
       "      <th>Vehicle State_ar</th>\n",
       "      <th>Vehicle State_az</th>\n",
       "      <th>Vehicle State_ca</th>\n",
       "      <th>Vehicle State_co</th>\n",
       "      <th>Vehicle State_ct</th>\n",
       "      <th>Vehicle State_dc</th>\n",
       "      <th>Vehicle State_de</th>\n",
       "      <th>Vehicle State_fl</th>\n",
       "      <th>Vehicle State_ga</th>\n",
       "      <th>Vehicle State_hi</th>\n",
       "      <th>Vehicle State_ia</th>\n",
       "      <th>Vehicle State_id</th>\n",
       "      <th>Vehicle State_il</th>\n",
       "      <th>Vehicle State_in</th>\n",
       "      <th>Vehicle State_ks</th>\n",
       "      <th>Vehicle State_ky</th>\n",
       "      <th>Vehicle State_la</th>\n",
       "      <th>Vehicle State_ma</th>\n",
       "      <th>Vehicle State_md</th>\n",
       "      <th>Vehicle State_me</th>\n",
       "      <th>Vehicle State_mi</th>\n",
       "      <th>Vehicle State_mn</th>\n",
       "      <th>Vehicle State_mo</th>\n",
       "      <th>Vehicle State_ms</th>\n",
       "      <th>Vehicle State_mt</th>\n",
       "      <th>Vehicle State_nc</th>\n",
       "      <th>Vehicle State_nd</th>\n",
       "      <th>Vehicle State_ne</th>\n",
       "      <th>Vehicle State_nh</th>\n",
       "      <th>Vehicle State_nj</th>\n",
       "      <th>Vehicle State_nm</th>\n",
       "      <th>Vehicle State_nv</th>\n",
       "      <th>Vehicle State_ny</th>\n",
       "      <th>Vehicle State_oh</th>\n",
       "      <th>Vehicle State_ok</th>\n",
       "      <th>Vehicle State_or</th>\n",
       "      <th>Vehicle State_pa</th>\n",
       "      <th>Vehicle State_ri</th>\n",
       "      <th>Vehicle State_sc</th>\n",
       "      <th>Vehicle State_sd</th>\n",
       "      <th>Vehicle State_tn</th>\n",
       "      <th>Vehicle State_tx</th>\n",
       "      <th>Vehicle State_ut</th>\n",
       "      <th>Vehicle State_va</th>\n",
       "      <th>Vehicle State_vt</th>\n",
       "      <th>Vehicle State_wa</th>\n",
       "      <th>Vehicle State_wi</th>\n",
       "      <th>Vehicle State_wv</th>\n",
       "      <th>Vehicle State_wy</th>\n",
       "      <th>Vehicle Manufacturer_acura</th>\n",
       "      <th>Vehicle Manufacturer_alfa-romeo</th>\n",
       "      <th>Vehicle Manufacturer_aston-martin</th>\n",
       "      <th>Vehicle Manufacturer_audi</th>\n",
       "      <th>Vehicle Manufacturer_bmw</th>\n",
       "      <th>Vehicle Manufacturer_buick</th>\n",
       "      <th>Vehicle Manufacturer_cadillac</th>\n",
       "      <th>Vehicle Manufacturer_chevrolet</th>\n",
       "      <th>Vehicle Manufacturer_chrysler</th>\n",
       "      <th>Vehicle Manufacturer_datsun</th>\n",
       "      <th>Vehicle Manufacturer_dodge</th>\n",
       "      <th>Vehicle Manufacturer_ferrari</th>\n",
       "      <th>Vehicle Manufacturer_fiat</th>\n",
       "      <th>Vehicle Manufacturer_ford</th>\n",
       "      <th>Vehicle Manufacturer_gmc</th>\n",
       "      <th>Vehicle Manufacturer_harley-davidson</th>\n",
       "      <th>Vehicle Manufacturer_honda</th>\n",
       "      <th>Vehicle Manufacturer_hyundai</th>\n",
       "      <th>Vehicle Manufacturer_infiniti</th>\n",
       "      <th>Vehicle Manufacturer_jaguar</th>\n",
       "      <th>Vehicle Manufacturer_jeep</th>\n",
       "      <th>Vehicle Manufacturer_kia</th>\n",
       "      <th>Vehicle Manufacturer_land rover</th>\n",
       "      <th>Vehicle Manufacturer_lexus</th>\n",
       "      <th>Vehicle Manufacturer_lincoln</th>\n",
       "      <th>Vehicle Manufacturer_mazda</th>\n",
       "      <th>Vehicle Manufacturer_mercedes-benz</th>\n",
       "      <th>Vehicle Manufacturer_mercury</th>\n",
       "      <th>Vehicle Manufacturer_mini</th>\n",
       "      <th>Vehicle Manufacturer_mitsubishi</th>\n",
       "      <th>Vehicle Manufacturer_morgan</th>\n",
       "      <th>Vehicle Manufacturer_nissan</th>\n",
       "      <th>Vehicle Manufacturer_pontiac</th>\n",
       "      <th>Vehicle Manufacturer_porsche</th>\n",
       "      <th>Vehicle Manufacturer_ram</th>\n",
       "      <th>Vehicle Manufacturer_rover</th>\n",
       "      <th>Vehicle Manufacturer_saturn</th>\n",
       "      <th>Vehicle Manufacturer_subaru</th>\n",
       "      <th>Vehicle Manufacturer_tesla</th>\n",
       "      <th>Vehicle Manufacturer_toyota</th>\n",
       "      <th>Vehicle Manufacturer_unknown</th>\n",
       "      <th>Vehicle Manufacturer_volkswagen</th>\n",
       "      <th>Vehicle Manufacturer_volvo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>33590</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>22590</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>19160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30990</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15000</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426875</th>\n",
       "      <td>23590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>32226.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426876</th>\n",
       "      <td>30590</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426877</th>\n",
       "      <td>34990</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426878</th>\n",
       "      <td>28990</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>30112.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426879</th>\n",
       "      <td>30590</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>22716.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259125 rows × 125 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price    year  odometer  Condition_excellent  Condition_fair  \\\n",
       "27      33590  2014.0   57923.0                    0               0   \n",
       "28      22590  2010.0   71229.0                    0               0   \n",
       "29      39590  2020.0   19160.0                    0               0   \n",
       "30      30990  2017.0   41124.0                    0               0   \n",
       "31      15000  2013.0  128000.0                    1               0   \n",
       "...       ...     ...       ...                  ...             ...   \n",
       "426875  23590  2019.0   32226.0                    0               0   \n",
       "426876  30590  2020.0   12029.0                    0               0   \n",
       "426877  34990  2020.0    4174.0                    0               0   \n",
       "426878  28990  2018.0   30112.0                    0               0   \n",
       "426879  30590  2019.0   22716.0                    0               0   \n",
       "\n",
       "        Condition_good  Condition_like new  Condition_new  Condition_salvage  \\\n",
       "27                   1                   0              0                  0   \n",
       "28                   1                   0              0                  0   \n",
       "29                   1                   0              0                  0   \n",
       "30                   1                   0              0                  0   \n",
       "31                   0                   0              0                  0   \n",
       "...                ...                 ...            ...                ...   \n",
       "426875               1                   0              0                  0   \n",
       "426876               1                   0              0                  0   \n",
       "426877               1                   0              0                  0   \n",
       "426878               1                   0              0                  0   \n",
       "426879               1                   0              0                  0   \n",
       "\n",
       "        Condition_unknown  Title Status_clean  Title Status_lien  \\\n",
       "27                      0                   1                  0   \n",
       "28                      0                   1                  0   \n",
       "29                      0                   1                  0   \n",
       "30                      0                   1                  0   \n",
       "31                      0                   1                  0   \n",
       "...                   ...                 ...                ...   \n",
       "426875                  0                   1                  0   \n",
       "426876                  0                   1                  0   \n",
       "426877                  0                   1                  0   \n",
       "426878                  0                   1                  0   \n",
       "426879                  0                   1                  0   \n",
       "\n",
       "        Title Status_missing  Title Status_parts only  Title Status_rebuilt  \\\n",
       "27                         0                        0                     0   \n",
       "28                         0                        0                     0   \n",
       "29                         0                        0                     0   \n",
       "30                         0                        0                     0   \n",
       "31                         0                        0                     0   \n",
       "...                      ...                      ...                   ...   \n",
       "426875                     0                        0                     0   \n",
       "426876                     0                        0                     0   \n",
       "426877                     0                        0                     0   \n",
       "426878                     0                        0                     0   \n",
       "426879                     0                        0                     0   \n",
       "\n",
       "        Title Status_salvage  Title Status_unknown  Vehicle Type_SUV  \\\n",
       "27                         0                     0                 0   \n",
       "28                         0                     0                 0   \n",
       "29                         0                     0                 0   \n",
       "30                         0                     0                 0   \n",
       "31                         0                     0                 0   \n",
       "...                      ...                   ...               ...   \n",
       "426875                     0                     0                 0   \n",
       "426876                     0                     0                 0   \n",
       "426877                     0                     0                 0   \n",
       "426878                     0                     0                 0   \n",
       "426879                     0                     0                 0   \n",
       "\n",
       "        Vehicle Type_bus  Vehicle Type_convertible  Vehicle Type_coupe  \\\n",
       "27                     0                         0                   0   \n",
       "28                     0                         0                   0   \n",
       "29                     0                         0                   0   \n",
       "30                     0                         0                   0   \n",
       "31                     0                         0                   0   \n",
       "...                  ...                       ...                 ...   \n",
       "426875                 0                         0                   0   \n",
       "426876                 0                         0                   0   \n",
       "426877                 0                         0                   0   \n",
       "426878                 0                         0                   0   \n",
       "426879                 0                         0                   1   \n",
       "\n",
       "        Vehicle Type_hatchback  Vehicle Type_mini-van  Vehicle Type_offroad  \\\n",
       "27                           0                      0                     0   \n",
       "28                           0                      0                     0   \n",
       "29                           0                      0                     0   \n",
       "30                           0                      0                     0   \n",
       "31                           0                      0                     0   \n",
       "...                        ...                    ...                   ...   \n",
       "426875                       0                      0                     0   \n",
       "426876                       0                      0                     0   \n",
       "426877                       1                      0                     0   \n",
       "426878                       0                      0                     0   \n",
       "426879                       0                      0                     0   \n",
       "\n",
       "        Vehicle Type_other  Vehicle Type_pickup  Vehicle Type_sedan  \\\n",
       "27                       0                    1                   0   \n",
       "28                       0                    1                   0   \n",
       "29                       0                    1                   0   \n",
       "30                       0                    1                   0   \n",
       "31                       0                    0                   0   \n",
       "...                    ...                  ...                 ...   \n",
       "426875                   0                    0                   1   \n",
       "426876                   0                    0                   1   \n",
       "426877                   0                    0                   0   \n",
       "426878                   0                    0                   1   \n",
       "426879                   0                    0                   0   \n",
       "\n",
       "        Vehicle Type_truck  Vehicle Type_unknown  Vehicle Type_van  \\\n",
       "27                       0                     0                 0   \n",
       "28                       0                     0                 0   \n",
       "29                       0                     0                 0   \n",
       "30                       0                     0                 0   \n",
       "31                       1                     0                 0   \n",
       "...                    ...                   ...               ...   \n",
       "426875                   0                     0                 0   \n",
       "426876                   0                     0                 0   \n",
       "426877                   0                     0                 0   \n",
       "426878                   0                     0                 0   \n",
       "426879                   0                     0                 0   \n",
       "\n",
       "        Vehicle Type_wagon  Vehicle State_ak  Vehicle State_al  \\\n",
       "27                       0                 0                 1   \n",
       "28                       0                 0                 1   \n",
       "29                       0                 0                 1   \n",
       "30                       0                 0                 1   \n",
       "31                       0                 0                 1   \n",
       "...                    ...               ...               ...   \n",
       "426875                   0                 0                 0   \n",
       "426876                   0                 0                 0   \n",
       "426877                   0                 0                 0   \n",
       "426878                   0                 0                 0   \n",
       "426879                   0                 0                 0   \n",
       "\n",
       "        Vehicle State_ar  Vehicle State_az  Vehicle State_ca  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_co  Vehicle State_ct  Vehicle State_dc  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_de  Vehicle State_fl  Vehicle State_ga  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_hi  Vehicle State_ia  Vehicle State_id  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_il  Vehicle State_in  Vehicle State_ks  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ky  Vehicle State_la  Vehicle State_ma  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_md  Vehicle State_me  Vehicle State_mi  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_mn  Vehicle State_mo  Vehicle State_ms  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_mt  Vehicle State_nc  Vehicle State_nd  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ne  Vehicle State_nh  Vehicle State_nj  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_nm  Vehicle State_nv  Vehicle State_ny  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_oh  Vehicle State_ok  Vehicle State_or  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_pa  Vehicle State_ri  Vehicle State_sc  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_sd  Vehicle State_tn  Vehicle State_tx  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_ut  Vehicle State_va  Vehicle State_vt  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_wa  Vehicle State_wi  Vehicle State_wv  \\\n",
       "27                     0                 0                 0   \n",
       "28                     0                 0                 0   \n",
       "29                     0                 0                 0   \n",
       "30                     0                 0                 0   \n",
       "31                     0                 0                 0   \n",
       "...                  ...               ...               ...   \n",
       "426875                 0                 0                 0   \n",
       "426876                 0                 0                 0   \n",
       "426877                 0                 0                 0   \n",
       "426878                 0                 0                 0   \n",
       "426879                 0                 0                 0   \n",
       "\n",
       "        Vehicle State_wy  Vehicle Manufacturer_acura  \\\n",
       "27                     0                           0   \n",
       "28                     0                           0   \n",
       "29                     0                           0   \n",
       "30                     0                           0   \n",
       "31                     0                           0   \n",
       "...                  ...                         ...   \n",
       "426875                 1                           0   \n",
       "426876                 1                           0   \n",
       "426877                 1                           0   \n",
       "426878                 1                           0   \n",
       "426879                 1                           0   \n",
       "\n",
       "        Vehicle Manufacturer_alfa-romeo  Vehicle Manufacturer_aston-martin  \\\n",
       "27                                    0                                  0   \n",
       "28                                    0                                  0   \n",
       "29                                    0                                  0   \n",
       "30                                    0                                  0   \n",
       "31                                    0                                  0   \n",
       "...                                 ...                                ...   \n",
       "426875                                0                                  0   \n",
       "426876                                0                                  0   \n",
       "426877                                0                                  0   \n",
       "426878                                0                                  0   \n",
       "426879                                0                                  0   \n",
       "\n",
       "        Vehicle Manufacturer_audi  Vehicle Manufacturer_bmw  \\\n",
       "27                              0                         0   \n",
       "28                              0                         0   \n",
       "29                              0                         0   \n",
       "30                              0                         0   \n",
       "31                              0                         0   \n",
       "...                           ...                       ...   \n",
       "426875                          0                         0   \n",
       "426876                          0                         0   \n",
       "426877                          0                         0   \n",
       "426878                          0                         0   \n",
       "426879                          0                         1   \n",
       "\n",
       "        Vehicle Manufacturer_buick  Vehicle Manufacturer_cadillac  \\\n",
       "27                               0                              0   \n",
       "28                               0                              0   \n",
       "29                               0                              0   \n",
       "30                               0                              0   \n",
       "31                               0                              0   \n",
       "...                            ...                            ...   \n",
       "426875                           0                              0   \n",
       "426876                           0                              0   \n",
       "426877                           0                              1   \n",
       "426878                           0                              0   \n",
       "426879                           0                              0   \n",
       "\n",
       "        Vehicle Manufacturer_chevrolet  Vehicle Manufacturer_chrysler  \\\n",
       "27                                   0                              0   \n",
       "28                                   1                              0   \n",
       "29                                   1                              0   \n",
       "30                                   0                              0   \n",
       "31                                   0                              0   \n",
       "...                                ...                            ...   \n",
       "426875                               0                              0   \n",
       "426876                               0                              0   \n",
       "426877                               0                              0   \n",
       "426878                               0                              0   \n",
       "426879                               0                              0   \n",
       "\n",
       "        Vehicle Manufacturer_datsun  Vehicle Manufacturer_dodge  \\\n",
       "27                                0                           0   \n",
       "28                                0                           0   \n",
       "29                                0                           0   \n",
       "30                                0                           0   \n",
       "31                                0                           0   \n",
       "...                             ...                         ...   \n",
       "426875                            0                           0   \n",
       "426876                            0                           0   \n",
       "426877                            0                           0   \n",
       "426878                            0                           0   \n",
       "426879                            0                           0   \n",
       "\n",
       "        Vehicle Manufacturer_ferrari  Vehicle Manufacturer_fiat  \\\n",
       "27                                 0                          0   \n",
       "28                                 0                          0   \n",
       "29                                 0                          0   \n",
       "30                                 0                          0   \n",
       "31                                 0                          0   \n",
       "...                              ...                        ...   \n",
       "426875                             0                          0   \n",
       "426876                             0                          0   \n",
       "426877                             0                          0   \n",
       "426878                             0                          0   \n",
       "426879                             0                          0   \n",
       "\n",
       "        Vehicle Manufacturer_ford  Vehicle Manufacturer_gmc  \\\n",
       "27                              0                         1   \n",
       "28                              0                         0   \n",
       "29                              0                         0   \n",
       "30                              0                         0   \n",
       "31                              1                         0   \n",
       "...                           ...                       ...   \n",
       "426875                          0                         0   \n",
       "426876                          0                         0   \n",
       "426877                          0                         0   \n",
       "426878                          0                         0   \n",
       "426879                          0                         0   \n",
       "\n",
       "        Vehicle Manufacturer_harley-davidson  Vehicle Manufacturer_honda  \\\n",
       "27                                         0                           0   \n",
       "28                                         0                           0   \n",
       "29                                         0                           0   \n",
       "30                                         0                           0   \n",
       "31                                         0                           0   \n",
       "...                                      ...                         ...   \n",
       "426875                                     0                           0   \n",
       "426876                                     0                           0   \n",
       "426877                                     0                           0   \n",
       "426878                                     0                           0   \n",
       "426879                                     0                           0   \n",
       "\n",
       "        Vehicle Manufacturer_hyundai  Vehicle Manufacturer_infiniti  \\\n",
       "27                                 0                              0   \n",
       "28                                 0                              0   \n",
       "29                                 0                              0   \n",
       "30                                 0                              0   \n",
       "31                                 0                              0   \n",
       "...                              ...                            ...   \n",
       "426875                             0                              0   \n",
       "426876                             0                              0   \n",
       "426877                             0                              0   \n",
       "426878                             0                              0   \n",
       "426879                             0                              0   \n",
       "\n",
       "        Vehicle Manufacturer_jaguar  Vehicle Manufacturer_jeep  \\\n",
       "27                                0                          0   \n",
       "28                                0                          0   \n",
       "29                                0                          0   \n",
       "30                                0                          0   \n",
       "31                                0                          0   \n",
       "...                             ...                        ...   \n",
       "426875                            0                          0   \n",
       "426876                            0                          0   \n",
       "426877                            0                          0   \n",
       "426878                            0                          0   \n",
       "426879                            0                          0   \n",
       "\n",
       "        Vehicle Manufacturer_kia  Vehicle Manufacturer_land rover  \\\n",
       "27                             0                                0   \n",
       "28                             0                                0   \n",
       "29                             0                                0   \n",
       "30                             0                                0   \n",
       "31                             0                                0   \n",
       "...                          ...                              ...   \n",
       "426875                         0                                0   \n",
       "426876                         0                                0   \n",
       "426877                         0                                0   \n",
       "426878                         0                                0   \n",
       "426879                         0                                0   \n",
       "\n",
       "        Vehicle Manufacturer_lexus  Vehicle Manufacturer_lincoln  \\\n",
       "27                               0                             0   \n",
       "28                               0                             0   \n",
       "29                               0                             0   \n",
       "30                               0                             0   \n",
       "31                               0                             0   \n",
       "...                            ...                           ...   \n",
       "426875                           0                             0   \n",
       "426876                           0                             0   \n",
       "426877                           0                             0   \n",
       "426878                           1                             0   \n",
       "426879                           0                             0   \n",
       "\n",
       "        Vehicle Manufacturer_mazda  Vehicle Manufacturer_mercedes-benz  \\\n",
       "27                               0                                   0   \n",
       "28                               0                                   0   \n",
       "29                               0                                   0   \n",
       "30                               0                                   0   \n",
       "31                               0                                   0   \n",
       "...                            ...                                 ...   \n",
       "426875                           0                                   0   \n",
       "426876                           0                                   0   \n",
       "426877                           0                                   0   \n",
       "426878                           0                                   0   \n",
       "426879                           0                                   0   \n",
       "\n",
       "        Vehicle Manufacturer_mercury  Vehicle Manufacturer_mini  \\\n",
       "27                                 0                          0   \n",
       "28                                 0                          0   \n",
       "29                                 0                          0   \n",
       "30                                 0                          0   \n",
       "31                                 0                          0   \n",
       "...                              ...                        ...   \n",
       "426875                             0                          0   \n",
       "426876                             0                          0   \n",
       "426877                             0                          0   \n",
       "426878                             0                          0   \n",
       "426879                             0                          0   \n",
       "\n",
       "        Vehicle Manufacturer_mitsubishi  Vehicle Manufacturer_morgan  \\\n",
       "27                                    0                            0   \n",
       "28                                    0                            0   \n",
       "29                                    0                            0   \n",
       "30                                    0                            0   \n",
       "31                                    0                            0   \n",
       "...                                 ...                          ...   \n",
       "426875                                0                            0   \n",
       "426876                                0                            0   \n",
       "426877                                0                            0   \n",
       "426878                                0                            0   \n",
       "426879                                0                            0   \n",
       "\n",
       "        Vehicle Manufacturer_nissan  Vehicle Manufacturer_pontiac  \\\n",
       "27                                0                             0   \n",
       "28                                0                             0   \n",
       "29                                0                             0   \n",
       "30                                0                             0   \n",
       "31                                0                             0   \n",
       "...                             ...                           ...   \n",
       "426875                            1                             0   \n",
       "426876                            0                             0   \n",
       "426877                            0                             0   \n",
       "426878                            0                             0   \n",
       "426879                            0                             0   \n",
       "\n",
       "        Vehicle Manufacturer_porsche  Vehicle Manufacturer_ram  \\\n",
       "27                                 0                         0   \n",
       "28                                 0                         0   \n",
       "29                                 0                         0   \n",
       "30                                 0                         0   \n",
       "31                                 0                         0   \n",
       "...                              ...                       ...   \n",
       "426875                             0                         0   \n",
       "426876                             0                         0   \n",
       "426877                             0                         0   \n",
       "426878                             0                         0   \n",
       "426879                             0                         0   \n",
       "\n",
       "        Vehicle Manufacturer_rover  Vehicle Manufacturer_saturn  \\\n",
       "27                               0                            0   \n",
       "28                               0                            0   \n",
       "29                               0                            0   \n",
       "30                               0                            0   \n",
       "31                               0                            0   \n",
       "...                            ...                          ...   \n",
       "426875                           0                            0   \n",
       "426876                           0                            0   \n",
       "426877                           0                            0   \n",
       "426878                           0                            0   \n",
       "426879                           0                            0   \n",
       "\n",
       "        Vehicle Manufacturer_subaru  Vehicle Manufacturer_tesla  \\\n",
       "27                                0                           0   \n",
       "28                                0                           0   \n",
       "29                                0                           0   \n",
       "30                                0                           0   \n",
       "31                                0                           0   \n",
       "...                             ...                         ...   \n",
       "426875                            0                           0   \n",
       "426876                            0                           0   \n",
       "426877                            0                           0   \n",
       "426878                            0                           0   \n",
       "426879                            0                           0   \n",
       "\n",
       "        Vehicle Manufacturer_toyota  Vehicle Manufacturer_unknown  \\\n",
       "27                                0                             0   \n",
       "28                                0                             0   \n",
       "29                                0                             0   \n",
       "30                                1                             0   \n",
       "31                                0                             0   \n",
       "...                             ...                           ...   \n",
       "426875                            0                             0   \n",
       "426876                            0                             0   \n",
       "426877                            0                             0   \n",
       "426878                            0                             0   \n",
       "426879                            0                             0   \n",
       "\n",
       "        Vehicle Manufacturer_volkswagen  Vehicle Manufacturer_volvo  \n",
       "27                                    0                           0  \n",
       "28                                    0                           0  \n",
       "29                                    0                           0  \n",
       "30                                    0                           0  \n",
       "31                                    0                           0  \n",
       "...                                 ...                         ...  \n",
       "426875                                0                           0  \n",
       "426876                                0                           1  \n",
       "426877                                0                           0  \n",
       "426878                                0                           0  \n",
       "426879                                0                           0  \n",
       "\n",
       "[259125 rows x 125 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One Hot Encoding (Manufacturer)\n",
    "encoded_manufacturer = pd.get_dummies(df['manufacturer'], prefix='Vehicle Manufacturer', dtype=int)\n",
    "df = df.drop('manufacturer', axis=1)\n",
    "df = pd.concat([df, encoded_manufacturer], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c6d1bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Training\n",
    "\n",
    "# Create X and y Dataframes for SKL\n",
    "X= df.drop('price', axis=1)\n",
    "y= df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "59dc5d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) #Test size 20% of data\n",
    "\n",
    "# Random Forrest Classifier\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c749e8fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "# Sample of 5,000 points to maintain readability\n",
    "sample = np.random.RandomState(42).choice(len(y_test), 5000, replace=False)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x=y_test.iloc[sample], y=y_pred[sample], alpha=0.3, s=10)\n",
    "plt.plot([0, y_test.max()], [0, y_test.max()], 'r--', label='Regression Line')\n",
    "plt.xlabel('Actual Price ($)')\n",
    "plt.ylabel('Predicted Price ($)')\n",
    "plt.title('Predicted vs Actual Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1054e087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3656.616633110759\n",
      "R^2: 0.7834092710869567\n",
      "Baseline MAE: 10145.964302942595\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, r2_score\n",
    "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
    "print('R^2:', r2_score(y_test, y_pred))\n",
    "print('Baseline MAE:', mean_absolute_error(y_test, [y_train.median()] * len(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e9f56fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8sAAAIjCAYAAAA0kYn0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8ekN5oAAAACXBIWXMAAA9hAAAPYQGoP6dpAABUVklEQVR4nO3dB5gV1d0/8EORIgiIiopg7yKIvQTRWPCNEmvsKIq91xi72ECjEXuPqNHYYuxRY401VmwIVuyKFQRfAWH/z++8z93/3WF3WXBhWfh8nudG7ty5c8/Mnd3sd37nnGlSUVFRkQAAAIBKTf//PwEAAIAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUCAsAwBMhyeeeCI1adIk/3dONmrUqLyfQ4cObeimADQIYRmAWSr+8I4/wKt7/OlPf5opn/nss8+m0047Lf3www9pdj0eL730UmqsLrvsMoHqV1pyySWr/Cy0adMmrb322umGG25o6KbN1sep/PHzzz+n2c3s/LsHmLbmdVgHAOrd6aefnpZaaqkqy7p16zbT/mAdOHBg6t+/f+rQocNM+Yy5WYTlBRdcMB/fucGGG26Y/vd//ze1aNGiXre72mqrpaOPPjr/+4svvkjXXHNN2nPPPdOECRPSvvvuW6+f1ZiVH6dy9f191Ae/e6BxE5YBaBD/8z//k9Zcc83UmI0fPz5XAOdWP/30U5p33nnT3KZp06apVatW9b7dxRZbLO2+++6VzyNgLb300umCCy4Qlms5TvVlypQpaeLEiTPluwUaJ92wAZgt/etf/0q9evXKYXS++eZLW265ZXrrrbeqrPP6669XBor4A3eRRRZJe++9d/r2228r14kukMcee2z+d1SyS102YzxmbWMyY3m8t3w7sWz48OFp1113TfPPP3/6zW9+U/n63/72t7TGGmuk1q1bp44dO6add945ffLJJzO077FPbdu2TR9//HHaaqut8r8jIFx66aX59TfeeCP99re/zcdmiSWWSDfffHO1Xbv/85//pP333z8tsMACqV27dmmPPfZI33//fbWV4VVWWSW1bNkyde7cOR188MFTdRvdaKONcuX/5ZdfzpXVCMknnHBC7hYb38uTTz5ZeWxj3fDdd9+lY445Jq266qp5H6INcZHktddeq3YM8G233ZbOOuus1KVLl/x9brLJJum9996bqr3//e9/0+9+97v8HcQx6N69e7rwwgurrDNixIi0ww475O8ithUXZu65554q60yaNClX/ZZbbrm8Thyn+E7//e9/T/eY5dLxifNj4403zscnvrNzzz03zaiFFloorbjiiun999+vsvypp55Kf/jDH9Liiy+ev7OuXbumI488Mle7qzuPPvvss7TNNtvkf8c24zuZPHlylXXj+47127dvnyugUdGuqevwY489VvmzGetuvfXW6e23366yTunn5Z133snBNrYbn33yySenioqK/LMR74tzIn5uzz///FSfF7Gi8hzHJY7PCiuskM4777z8ueWifYcccki66aabKs//Bx98ML8Wxyx+lyy88MJ5ebz+17/+darPuvjii/Nr8X3H+RjnWennsbbfPUDjoLIMQIMYM2ZM+uabb6osi6684cYbb8x/rPfp0yedc845uYJ5+eWX5yDz6quv5oAWItR88MEHaa+99sp/cEdou+qqq/J/n3/++fyH6XbbbZf/YP/73/+eK3Slz4g/3L/++uvpbneElAhXZ599duUf3xHwIgTsuOOOaZ999snbjT+iI1RGe2ek+2WEmQiWsY0IXPEHffxhHwHlxBNPTLvttlvetyuuuCKH4PXWW2+qbu2xfnx2/NE+cuTIfAw/+uijyrAX4rUIjJtuumk68MADK9d78cUX0zPPPJPmmWeeyu3FRYhoU1wIiAAUQSJC4qGHHpqDWLQrxPIQ381dd92Vj1m07auvvkpXXnll6t27dw6VEczLDR48OFdtI8zF+RH7HfsZ4bgkvvO4gLDoooumww8/PH/vEdTuu+++/DzE97/BBhvksBrj4OOYRRCPwPiPf/wjbbvttpX7PmjQoPydxfjgsWPH5rHjr7zyStpss82m+zuLCxFbbLFF/l7iXLjjjjvScccdly8WxHGbXr/88kv69NNPcwgrd/vtt+efifi+IuC/8MIL+XyLdeO14nkUP0frrLNODoyPPPJIDqbLLLNMfn+I8ziC69NPP50OOOCAtNJKK6V//vOf+WewKN4f+xIXqOL4RUCPz47jHcet9LNZstNOO+XtxXd7//33pzPPPDNfwIjzIC74xM93nNvxna+11lr5fJ+WuMhR/N0RYTUesS+///3v0+OPP54GDBiQu2w/9NBDObRGAI7fAcXgH+dG/KzE74Zof5yn6667bmWYjt8VcfEuthfnyBFHHJHfe/XVV6fDDjssX5SJcy/GTMcFvDhf44Jabb97gEaiAgBmoeuuuy4SZrWP8OOPP1Z06NChYt99963yvi+//LKiffv2VZb/9NNPU23/73//e97Wf/7zn8plf/7zn/OyDz/8sMq68TyWR5uKYvmpp55a+Tz+Hct22WWXKuuNGjWqolmzZhVnnXVWleVvvPFGRfPmzadaXtPxePHFFyuX7bnnnnnZ2WefXbns+++/r2jdunVFkyZNKm655ZbK5SNGjJiqraVtrrHGGhUTJ06sXH7uuefm5XfffXd+Pnr06IoWLVpUbL755hWTJ0+uXO+SSy7J6/31r3+tXNa7d++87IorrphqH1ZZZZX8etHPP/9cZbulY96yZcuK008/vXLZ448/nre90korVUyYMKFy+YUXXpiXx7EMv/zyS8VSSy1VscQSS+TjUW7KlCmV/95kk00qVl111fz55a+vv/76Fcstt1zlsh49elRsueWWFdOr1N74b/H43HDDDZXLYl8WWWSRiu23336a24x9iu/h66+/zo/Y5379+uVtHnzwwVXWre68HzRoUD43Pvroo6nOo/JjHXr27JnPjZK77rorrxfnR0kc6169ek3187HaaqtVdOrUqeLbb7+tXPbaa69VNG3atGKPPfaY6udlv/32q7LNLl265HYOHjx4qnM72luX41Td747S+V/alzPPPLPK+3bYYYf8ue+9917lslgv2v3WW29VWXfAgAEViy66aMU333xTZfnOO++cfweVjv/WW2+dz/3a1PS7B2gcdMMGoEFEl+KoEpY/Qvw3un/usssuuXpUejRr1ixXx6JiVBJdnkuiqhPrRUUoRJVrZojKW7k777wzj3WMSmJ5e6PiGRXo8vZOr6h4lkSFOLqTRpU0PqsklsVrUcUt2m+//apUhqOS2Lx58/TAAw9UVgljjGZUyqKiWxLjY6N7bFQCy0V31Kji11WsX9puVDijMh0V6Ghzdd9PbLt8kqbo6htK+xZV+g8//DC3t1itL1XKo+t3VAvjGP3444+V30d8dlRY33333VxhLB3TqELHsvoQ+1Y+ljb2JSrW1X031Xn44Ydz1TEeUY2OHhZxTP785z9XWa/8vI8ux7F/66+/fq6qxjGa1jkbx7W8TXE+xHlRqjSH+HmLHgPlYtKxYcOG5e7aUR0uiW7wUYkvnVc1ncOxzeimHO2MKm3x3K7rcYrfA8XfHdG7orQv8TlR8S0X3bLjc6NCXC56Oay88sqVz2Od6H3Qt2/f/O/yn+k4f6LHQ+ncjXZHNT96YQBzJt2wAWgQESKqm+CrFFyii2Z1IsSVRDCKLsS33HJLGj16dJX14o/amaHY1TnaG39URzCuTnlYnR4xhrbYXTPGfcZ43lIwLF9e3VjkYpsizEX35dKYyeiSHSKolIuQF91sS6+XRLfm6ZlxOC4ixFjiGBMdIbd8nGx0Hy6KMbjlSt2PS/tWGrtb26zpMcY5vo/oFh+P6sS5EvsSM7JH9+Pll18+bzO6UPfr1y+HvxlR3XcT+xBdc+saAqObchynN998M/879r14zGMs+ymnnJLHYBe/9+J5X915FG0qf198z3FexPlRrnhe1HS+hOhqHd2di5PeFb/TOFejTaUuyeXLy+caqE28N4YNVCfaGN37Y56DYvvK96Gmn+cYQhEX62I4RzyqU/pdE13s44JT/C5bdtll0+abb567X0eXdGDOICwDMFuJgBWiqhbV2aKogJVE9TBuzRLjEWNsYvyxH++P0FPaTm2KwaakOPlRTVW9UntjO1GxiopWUTGA1FV126pteXHyopmhuO/TEuO6I7DGRElnnHFGrkZGpTkqw9V9P/Wxb6XtxhjYqARWJ4JNiPGxEcDvvvvuXNWNWzXF2NIYB15eEa2rX9v+8hAYbY/JvWJ8dlxwOOqooyrPzajixoWiCGuxToTTqJZHxbd4XGtq06xS3ec35Dlcl5/nED0EqhuzHUoXUyKAxxj/GC8fE4NFRTouDMWFjLiIBzR+wjIAs5WYeCh06tSpxupRiMrYo48+mv8ojT9OS6rrUltTKC5VLouz/harT9Nqb/yRHxWqqFDOTuJYxMzMJePGjctdaWMm6RAzaYf4gz8qySXRNTsqwbUd/7oc35jgKj7/2muvrbI8jnexsjg950ZUXWtqW2k/oqJfl/ZHgI+uzvGI4xMBOiaumpGwXN9iBvjoJhwXHWJW8wjFMRN6TBp1/fXXV3Y9DtOawbs2cR7Ez1Lsf/nFnTgviutVt7w0+3h8pw19K7VoY1R7owt+eXU52ld6vTZRhY/3xUWJupw/sb8xiVk84ucmJvWKCf+OP/74XEGv6WcDaByMWQZgthIVtehqHQEhZr0tKs1gXapOFatRQ4YMmeo9pT/gi6E4Pif+wI9bLJWL6lBdxR/H0ZYI7cW2xPO6di2dGaIbafkxjFmuY4bl0szMEQaii+9FF11Upe0RbqM7b4S1uojjW91thuK4FI9JzNZcGjM8vVZfffV8USK+4+LnlT4nLrLEDN0x23JcGCgqnwG9+N1EUIyq84QJE9LsIqrH0c6Yebmm8z7+Xbx11vSIiydxXsT5URJhMWa5LhddtaMHRwT18uMfFy+iMl+6CNOQog3R9ksuuaTK8ugxEMF1WrOSx/Hdfvvtc5U49mt6zp/4WYrxz/F9lH7uavrdAzQOKssAzFYiwMYf7TF2NMJR3KYoqj0xTjMmnIrxgPGHcKxXuq1S/GEaY1DjD/aoiBbF/Y9D3NoothdVx5jAJ/6QjQpi3NYm/htjqCM4R+VueqqdMbY0KkkxFjhuTxSVqWhH3H4nJtmKLsENISpdca/i6K4e1cC4CBC334pb64Q4rtHuCPrRdT2Wl9aL2/iUT1ZVmzi+8Z3FcYiwGYE1xpxHF+IYFxxV25iAKqqicZug8ir29Igu3PE58d1FaIvtRoCLqmFM1BVjZkuTx8V+xiRZMVlZfF7cDui5557LEzKV7vMcwSaCdbQ/Ksxx26iohsftgmYXEe5iPPVf/vKXfP/r6HYd51ycU3HRIX4OIthVN2a9ruJ4xs9V3GYrzuE4LjFxXXXj/mOysWhT3KosJukq3ToqxhyX35e8ocS+RG+G+FmPfenRo0f+vRBd7aP7f6l3Qm3i90FMzBdjyOP8ieMR3d5jYq+oWse/Q4xRjqEicezidmlxC7P43RQXmUpV7dp+9wCNQENPxw3A3KW6WyVVJ27L06dPn3yrllatWlUss8wyFf3796946aWXKtf59NNPK7bddtt8q6lY7w9/+EPF559/PtWtlMIZZ5xRsdhii+VbxZTfyiVuAxO3ion3zzfffBU77rhjvqVSTbeOitv6VOcf//hHxW9+85uKNm3a5MeKK66Yb/kzcuTI6T4ecQud2EZR3J6oulvVxO10ym+BVNrmk08+mW/dM//881e0bdu2Yrfddqtyy5/yW0VFe+eZZ56KhRdeuOLAAw+c6tZMNX126bZe8flx/OJzS7eRils3HX300fk2PHFroA022KDiueeey6+X32qqdCum22+/vU639nr66acrNttss/x5cZy6d+9ecfHFF1dZ5/3338+3MopbN8V+xXe/1VZbVdxxxx2V68TthdZee+18/kT74hjErb7Kb7c1PbeOqu74xHcZ38+0FL/DckOHDq1yHIYPH16x6aab5u90wQUXzLdTi9s3FY9VTedR6VwuF+dF3KqqXbt2+Wch/v3qq69We/wfeeSR/F3GMYv1+/btm9tU3WcUf16m99yenuNUErefO/LIIys6d+6cv/u4XVjcwqn89mKhuttylXz11Vf5ta5du+ZtxHkUtyS76qqrKte58sorKzbccMOKBRZYIN8OLX5HHXvssRVjxoyp0+8eYPbXJP6noQM7AFB/hg4dmquucUub6mYcBwCmzZhlAAAAKBCWAQAAoEBYBgAAgAJjlgEAAKBAZRkAAAAKhGUAAAAoaF5cAHOiKVOmpM8//zzNN998qUmTJg3dHAAAoIHESOQff/wxde7cOTVtWnP9WFhmrhBBuWvXrg3dDAAAYDbxySefpC5dutT4urDMXCEqyqUfiHbt2jV0cwAAgAYyduzYXEgrZYSaCMvMFUpdryMoC8sAAECTaQzPNMEXAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFDQvLgA5mTdTn0oNW05b0M3AwAA5hqjBm+ZGiOVZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWabQmT56cpkyZ0tDNAAAA5kDCMvXihhtuSAsssECaMGFCleXbbLNN6tevX/733XffnVZfffXUqlWrtPTSS6eBAwemX375pXLdv/zlL2nVVVdNbdq0SV27dk0HHXRQGjduXOXrQ4cOTR06dEj33HNPWnnllVPLli3Txx9/XG17oh1jx46t8gAAAKgrYZl68Yc//CFXeiPIlowePTrdf//9ae+9905PPfVU2mOPPdLhhx+ehg8fnq688socfs8666zK9Zs2bZouuuii9NZbb6Xrr78+PfbYY+mPf/xjlc/56aef0jnnnJOuueaavF6nTp2qbc+gQYNS+/btKx8RvgEAAOqqSUVFRUWd14ZaRCV41KhR6YEHHqisFF966aXpvffeS5tttlnaZJNN0vHHH1+5/t/+9rcchj///PNqt3fHHXekAw44IH3zzTf5eYTrvfbaKw0bNiz16NGj1rZEZbm8yh2V5QjMXY+4LTVtOW897TEAADAtowZvmWYnkQ2ioDZmzJjUrl27GtdrPktbxRxt3333TWuttVb67LPP0mKLLZbDbf/+/VOTJk3Sa6+9lp555pkqleSoRP/888+5WjzvvPOmRx55JFeER4wYkU/g6KJd/npo0aJF6t69+zTbEl204wEAADAjhGXqTc+ePXPFN8Yvb7755rmbdHTDDjH2OMYob7fddlO9L8YwR0V6q622SgceeGAO1B07dkxPP/10GjBgQJo4cWJlWG7dunUO3wAAADOTsEy92meffdKQIUNydXnTTTetHCscE3uNHDkyLbvsstW+7+WXX84zW59//vl57HK47bbbZmnbAQAASoRl6tWuu+6ajjnmmHT11VfnCnPJKaeckivHiy++eNphhx1yII6u2W+++WY688wzc4ieNGlSuvjii1Pfvn1zl+0rrriiQfcFAACYe5kNm3oVA+W333771LZt23zbqJI+ffqk++67Lz388MN5XPO6666bLrjggrTEEkvk16P7dkwIFjNdd+vWLd100015/DIAAEBDMBs29S5mvV5llVXybaBmtxnvzIYNAACz1iizYTO3+/7779MTTzyRH5dddllDNwcAAGCGCcvU62zYEZijK/UKK6zQ0M0BAACYYcIy9SZu/wQAADAnMMEXAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUNC8uADmZG8O7JPatWvX0M0AAABmcyrLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABc2LC2BO1u3Uh1LTlvM2dDOARm7U4C0bugkAwEymsgwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUCAsz0WeeOKJ1KRJk/TDDz80dFMAAABma8IyDWLJJZdMQ4YMaehmAAAAVEtYplGbOHFiQzcBAACYAwnLjdiECRPSYYcdljp16pRatWqVfvOb36QXX3yx8vUHHnggLb/88ql169Zp4403TqNGjZpqG//4xz/SKqusklq2bJmrveeff36V12PZmWeemfbYY4/Utm3btMQSS6R77rknff3112nrrbfOy7p3755eeumlKu97+umnU69evfJnd+3aNbdz/Pjx+bWNNtooffTRR+nII4/M3cLjUZf3ldpzxhln5Pa0a9cu7bfffvV6TAEAAIKw3Ij98Y9/zGH3+uuvT6+88kpadtllU58+fdJ3332XPvnkk7Tddtulvn37pmHDhqV99tkn/elPf6ry/pdffjntuOOOaeedd05vvPFGOu2009LJJ5+chg4dWmW9Cy64IG2wwQbp1VdfTVtuuWXq169fDqu77757/txlllkmP6+oqMjrv//++2mLLbZI22+/fXr99dfTrbfemkPwIYcckl+/8847U5cuXdLpp5+evvjii/yoy/tKzjvvvNSjR4/cnmhvTRcSxo4dW+UBAABQV00qSgmHRiWqrfPPP38OtrvuumteNmnSpFx5PeKII9L333+f7r777vTWW29VvifC8jnnnJNf69ChQ9ptt91yhfjhhx+uEsDvv//+yvfF9qLSe+ONN+bnX375ZVp00UVzSI2wG55//vm03nrr5dC7yCKL5GDerFmzdOWVV1ZuN0Jv7969c7ujCl5qZzxK6vq+nj17pn/+85+1Hp8I/gMHDpxqedcjbktNW847Q8ccoGTU4C0bugkAwAyKQlr79u3TmDFjcm/VmqgsN1JRhY1wHBXfknnmmSetvfba6e23386PddZZp8p7ItCWi3XK3x/i+bvvvpsmT55cuSy6WZcsvPDC+b+rrrrqVMtGjx6d//vaa6/lEB9dtEuPqHhPmTIlffjhhzXuU13ft+aaa07z+Bx//PH55C89otIOAABQV83rvCZzrQjhJaXxxdUti1Abxo0bl/bff/883rho8cUXr/Fz6vq+Nm3aTLPNMQY7HgAAADNCWG6kYpxwixYt0jPPPJMn3QpRaY4JvqJrc4xbjom4ykV36XIrrbRSfn+5eB6TgkV36Bm1+uqrp+HDh+cx1DWJtpdXr+v6PgAAgFlBN+xGKqqrBx54YDr22GPTgw8+mEPmvvvum3766ac0YMCAdMABB+Tu1PH6yJEj08033zzVxF1HH310evTRR/Ps0u+8806eKOySSy5JxxxzzK9q23HHHZeeffbZPDFXTC4W7Yjx0+UTdcXY4//85z/ps88+S998802d3wcAADArCMuN2ODBg/PM0TE7dVRl33vvvfTQQw/lib+i23LMlH3XXXflmaOvuOKKdPbZZ1d5f7zntttuS7fcckvq1q1bOuWUU/KkXf379/9V7Yoxzk8++WQO4DE5WEzIFdvu3Llz5TrxOXErq6iQL7TQQnV+HwAAwKxgNmzmqhnvzIYN1AezYQNA42U2bAAAAJhBwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFDQvLoA52ZsD+6R27do1dDMAAIDZnMoyAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAQfPiApiTdTv1odS05bwN3QxotEYN3rKhmwAAMEuoLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLjdCSSy6ZhgwZUuPro0aNSk2aNEnDhg2r0/b69++fttlmm3psIQAAQOMmLM9Cffv2TVtssUW1rz311FM54L7++uu/+nO6du2avvjii9StW7c0s5WCeW2PoUOHzvR2AAAA1Kfm9bo1ajVgwIC0/fbbp08//TR16dKlymvXXXddWnPNNVP37t1/9ec0a9YsLbLIImlWKAXzkvPOOy89+OCD6ZFHHqlc1r59+1nSFgAAgPqisjwLbbXVVmmhhRaaqtI6bty4dPvtt+cwHZ5++unUq1ev1Lp16xxGDzvssDR+/Pgq7/npp5/S3nvvneabb760+OKLp6uuuqrWbthvvfVW/vx27drl98T233///WrbOWXKlDRo0KC01FJL5Tb06NEj3XHHHbUG89Kjbdu2qXnz5vnfP//8c+rcuXP+7HLRhXyJJZbIn/PEE0/ktt5///35QkGrVq3Suuuum958880q76nLMQEAAKgvwvIsFCFyjz32yGG5oqKicnkE5cmTJ6dddtklB9joqh0V6OiSfeutt+ageMghh1TZ1vnnn58r0a+++mo66KCD0oEHHphGjhxZ7ed+9tlnacMNN0wtW7ZMjz32WHr55Zdz0P7ll1+qXT+C8g033JCuuOKKHHSPPPLItPvuu6cnn3xyusdWb7rpprlqXi6exzjppk3//+l37LHH5n168cUX8wWF6LI+adKk/Fpdj0m5CRMmpLFjx1Z5AAAA1FWTivLUxkw3YsSItNJKK6XHH388bbTRRnlZBNmotN54441pn332ydXaK6+8svI9EQx79+6dK6lReY0QGlXWWD/EVxiV3IEDB6YDDjggV5ajKhxBerXVVksnnHBCuuWWW3KYnmeeeaZqUwTXH374Id111105ZHbs2DF3o15vvfUq14l2RTX75ptvrnX/TjvttLydUlX7tttuy22KrtoR1l955ZUc8j/44IO8H1FZ3njjjXP7dtppp/ye7777LndTj4sKO+64Y52OSXXtiONR1PWI21LTlvPW6bsCpjZq8JYN3QQAgF8lCmkxVHTMmDG5521NVJZnsRVXXDGtv/766a9//Wt+/t577+XJvUpdsF977bUcEqM7c+nRp0+f3GX5ww8/rNxO+djm6MYcYXn06NHVfmYE1wjX1QXlomhPhOLNNtusShui0lxTt+3axCzbEXT/+c9/5uexbxGOIyiXKw/mEdZXWGGF9Pbbb0/XMSl3/PHH55O/9Pjkk0+mu+0AAMDcywRfDSCC8aGHHpouvfTS3CV5mWWWyVXS0vjl/fffP4/JLYqxySXF4BuBOcJjdWKcb13F54cYQ7zYYotVeS0qw9OrRYsWuet57Od2222XK9MXXnjhdG2jrsek2NYZaS8AAEAQlhtAdC0+/PDDc3CMim2MN46wG1ZfffU0fPjwtOyyy9bb50UV+vrrr89jgKdVXV555ZVzyPz4448rA/yvFd2o4zZWl112WR4nHaG56Pnnn68Mvt9//3165513cnf1mXVMAAAAaqMbdgOIbsQxPje6CsdY3hgzXHLcccelZ599Nk9eFd2n33333XT33XfXOpnVtMR7o1/+zjvvnF566aW8zRjvXN2EYDFT9jHHHJMn9YqAHV2vY5zxxRdfnJ/PiAi9McN17FtMYlZdpfv0009Pjz76aJ4FO47HggsumLtwz6xjAgAAUBthuQG7YkcFNcbexu2VyqvAMet0VFZjnHHPnj3TKaecUmWd6bXAAgvkWbCjO3NUi9dYY4109dVX11hlPuOMM9LJJ5+cZ8WOoBszUUe37Jg07Nfs78SJE/Ms3NUZPHhwrrZH27788st077335i7cM+uYAAAA1MZs2MwSEcDjFllx66dypdmw48JBhw4dZvqMd2bDhl/HbNgAQGNnNmxmC1HNjq7Vl1xySZ7UDAAAoDEQlpmpYlxxdK2Oe0rX1AUbAABgdqMbNnMF3bChfuiGDQA0drphAwAAwAwSlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAICC5sUFMCd7c2Cf1K5du4ZuBgAAMJtTWQYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAAChoXlwAc7Jupz6Umract6GbAfVm1OAtG7oJAABzJJVlAAAAKBCWAQAAoL7C8o033pg22GCD1Llz5/TRRx/lZUOGDEl33333jG4SAAAAGm9Yvvzyy9NRRx2Vfve736UffvghTZ48OS/v0KFDDswAAAAw14Xliy++OF199dXpxBNPTM2aNatcvuaaa6Y33nijPtsHAAAAjSMsf/jhh6lnz55TLW/ZsmUaP358fbQLAAAAGldYXmqppdKwYcOmWv7ggw+mlVZaqT7aBQAAAI3rPssxXvnggw9OP//8c6qoqEgvvPBC+vvf/54GDRqUrrnmmvpvJQAAAMzuYXmfffZJrVu3TieddFL66aef0q677ppnxb7wwgvTzjvvXP+tBAAAgNk5LP/yyy/p5ptvTn369Em77bZbDsvjxo1LnTp1mjktBAAAgNl9zHLz5s3TAQcckLtgh3nnnVdQBgAAYI4yQxN8rb322unVV1+t/9YAAABAYx2zfNBBB6Wjjz46ffrpp2mNNdZIbdq0qfJ69+7d66t9AAAA0DjCcmkSr8MOO6xyWZMmTfLM2PHfyZMn118LAQAAoDGE5Q8//LD+WwIAAACNOSwvscQS9d8SAAAAaMxh+YYbbqj19T322GNG2wMAAACNMywffvjhVZ5PmjQp32+5RYsW+VZSwjIAAABz3a2jvv/++yqPcePGpZEjR6bf/OY36e9//3v9txIAAABm97BcneWWWy4NHjx4qqrzzLbkkkumIUOG1Pj6qFGj8gzdw4YNq9P2+vfvn7bZZps0J4rZyvfbb7/UsWPH6TomAAAAc5t6C8uhefPm6fPPP6/Tun379k1bbLFFta899dRTOcy9/vrrv7pNXbt2TV988UXq1q1bmlU22mij3P64eFC05ZZb5tdOO+20NKs9+OCDaejQoem+++6rt2MS+7HaaqvVS/sAAAAa9Zjle+65Z6qKZYSvSy65JG2wwQZ12saAAQPS9ttvnz799NPUpUuXKq9dd911ac0110zdu3dPv1azZs3SIosskma1COkRTP/0pz9VLvvss8/So48+mhZddNHUEN5///382euvv36a3cQ5FPfnjgsus+J9AAAA9V5Zjm7K5Y/tttsuVxgj3P71r3+t0za22mqrtNBCC+VAWS7GP99+++05TIenn3469erVK7Vu3ToH0MMOOyyNHz++ynticrG99947zTfffGnxxRdPV111Va3dsN966638+e3atcvvie1HkKzOlClT0qBBg9JSSy2V29CjR490xx131Gn/vvnmm/TMM89ULrv++uvT5ptvnjp16lRl3RtvvDFfHIi2RLDfdddd0+jRoytff+KJJ/I+RNCO9WIStQi8MU68tu7jRxxxRK5yl14/9NBD08cff5y3Fd3XS9XmGGveoUOHtMACC+R2F49FXNDYZZddcvftNm3a5Db897//zd/dwIED02uvvZa3GY9YVt0x/+GHH/Ky2JfyffrXv/6V1lhjjdSyZcv8XU/reNf0PgAAgAYPyxFoyh9R2fvyyy/TzTffXOeqaVQCY9bsCFdRHSyJoBzbi3AWoS26akcFOrpk33rrrTkYHXLIIVW2df755+cA9+qrr6aDDjooHXjggVWCZLmo7m644YY5ZD322GPp5ZdfzkH7l19+qXb9CG5xq6wrrrgih+wjjzwy7b777unJJ5+sdf9iZvDddtstV8lLYl/js4piNvEzzjgjh8677rorh80It0Unnnhi3teXXnopH7/qtlWTCy+8MJ1++um5ih+9AF588cW8PC48HHXUUXmbEcabNm2att122/y9li5e9O7dOx+36FEQbfzjH/+YX99pp53S0UcfnVZZZZW8zXjEsukRlfforv7222/niy11Pd7F9xVNmDAhjR07tsoDAACgrmao72qErmOOOSZXOMv97//+b/rzn/+cTjnllDptJ8JerB9BqFQBjXAZ4bh9+/Y5iEXgjAppaRKxiy66KIe3yy+/PLVq1Sov/93vfpdDcjjuuOPSBRdckB5//PG0wgorTPWZl156ad72LbfckuaZZ568bPnll6+2fRG4zj777PTII4+k9dZbLy9beumlc2C/8sorczumtX9RtY6gGqF8zJgxuXJbHK9cHnpj+7GPa621Vg6qbdu2rXztrLPOqvzMCIsx/vnnn3+uPA61iX2OynWxW3oc63LRMyAq/sOHD89jmuMCyNdff53DdVSWw7LLLlu5frQvgvuMdnWPc2mzzTab7uNd/r7qROiOqjcAAMAsqyxHCIkgVxTdoacnoKy44oq5O3Gp6/Z7772XJ/cqdcGOKmZUYyOQlR59+vTJVc0PP/ywcjvllcXoohvBrbwbc7noGhwBthSUaxPtiX2KUFbehqh81tRtu1x0IY6AH92IYx/79etX7djaCNIx4Vl0IY9AWwqF0WW6XPl+lir4Ne1nXb377ru5ih+hNLqll7pnlz47jlfPnj0rg3J9ix4BM3K8y99XneOPPz5fnCg9Pvnkk5nSfgAAYM40Q5Xl6DYdobQowu30hqoIxjGWNiq+UVVeZpllKsNiBPL9998/j1MuimBZUgy+0bZSN+KiGAdbV6ULAvfff39abLHFqrwW3bjrIqrGsW9RqX3hhRemej26QccFgHjcdNNNuaobQTWeT5w4scq65ftZOv6l/Yzu0+Xd2Uvdu6clQvoSSyyRrr766tS5c+e8vagolz57eo5XSbQllLenprbEGOgZOd7l76tOrF/X7wgAAOBXheX555+/ciKn6LpcHphjnHGEnQMOOGB6Npl23HHHfG/m6O4bFcQYb1za7uqrr55DZnm3318rqrMx0VaEt2lVl1deeeUcuCK8TqvLdU1isq7osh5V5the0YgRI9K3336bx9/GBGYhxg9PrwjZb775ZpVlURWubR/jc2NsdwTlqLaH4mRZcbyuueaa9N1331V7ISTGZsd3X2xLiDHMUZUutWVa6uN4AwAAzPKwPGTIkFwtjGppdLeOcbDloSm68JbGmtZVdLONSaGi22xMwlQ+sVWMP1533XXzhF777LNPriZGeP73v/+db1M1I2JbF198cdp5553zZ8Y+PP/882nttdeeaoxzdImOoBuTTEXFNWaNji69McN1dFnec88963SBIUJjTaE1KuRx7KJNcaEhAm9M9jW9fvvb3+bx33HBIb6Dv/3tb3lbpbBaU9tiBuyYPTy6dUdILb/VVYgu2jGOOGbajnHAsV5MpBZV6Pic+M6jS3yE4Zg8LI5ZVKPje4sLADGrdXQVP+mkk6a5D/VxvAEAAGZ5WC6FlQhAMda4LuN+69oV+9prr80TdUUIK69qxuRfMQt0VD4jqEc37emdcblchMOYBfvYY4/N1cuY8Gq11Var8f7QEVyjUhpB8YMPPsi3WIqK9wknnFDnz4z31KR0+6zYXkzsFds+77zz0u9///vp2q/otn3yySfnmapj0q+4oBGzjb/xxhu1dpeOic6im3t0vY6LBdGG0mRrIYL8ww8/nCdbi+8nZg2PCnB0LS9NEHbnnXemjTfeON8eKrrSxwWPGKMd32vc3im2e+655+bbZk1LfRxvAACAX6tJRXGg63SKYFYcWxtVQJidRK+F6EXQ9YjbUtOWVWdxh8Zs1OAtG7oJAACNMhtEL9basusMzYYdMxZHd+ZOnTrlrtHRnbf8AQAAAI3ZDIXl6MIcXZnjXscxIVNMABVjmKMLdYyZBQAAgLnu1lH33ntvDsUxtnWvvfbK44ljxuq4BVHc/mi33Xar/5YCAADA7FxZjtsILb300vnf0cc7noeYvfg///lP/bYQAAAAGkNYjqActwsKK664YrrtttsqK861zfwMAAAAc2xYjq7Xr732Wv533Jc3biPUqlWrfH/cGM8MAAAAc92Y5QjFJZtuumkaMWJEevnll/O45bg3MgAAAMx1Ybl4n+WY2CseAAAAMNd2w548eXI644wz0mKLLZbatm2bPvjgg7z85JNPTtdee219txEAAABm/7B81llnpaFDh6Zzzz03tWjRonJ5t27d8j2XAQAAYK4Ly3GP5auuuirfT7lZs2aVy3v06JHHLwMAAMBcF5Y/++yzPJlX0ZQpU9KkSZPqo10AAADQuMLyyiuvnJ566qmplt9xxx2pZ8+e9dEuAAAAaFyzYZ9yyilpzz33zBXmqCbfeeedaeTIkbl79n333Vf/rQQAAIDZtbIcs15XVFSkrbfeOt17773pkUceSW3atMnh+e23387LNttss5nXWgAAAJjdKsvLLbdc+uKLL1KnTp1Sr169UseOHdMbb7yRFl544ZnXQgAAAJidK8tRVS73r3/9K40fP76+2wQAAACNb8xyTeEZZndvDuyT2rVr19DNAAAA5qTKcpMmTfKjuAwAAADm2spyVJL79++fWrZsmZ///PPP6YADDsiTfJWL2bEBAABgrgjLcbuocrvvvnt9twcAAAAaV1i+7rrrZl5LAAAAoDGOWQYAAIC5gbAMAAAABcIyAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFDQvLgA5mTdTn0oNW05b0M3g0Zg1OAtG7oJAAA0IJVlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYXk2t+SSS6YhQ4bU+PqoUaNSkyZN0rBhw+q0vf79+6dtttmmHlsIAAAw5xGWZ5K+ffumLbbYotrXnnrqqRxwX3/99V/9OV27dk1ffPFF6tatW5pVPvzww7Trrrumzp07p1atWqUuXbqkrbfeOo0YMWKGAny5eN9dd901E1oNAABQd8LyTDJgwID073//O3366adTvXbdddelNddcM3Xv3v1Xf06zZs3SIosskpo3b55mhUmTJqXNNtssjRkzJt15551p5MiR6dZbb02rrrpq+uGHH2ZJGwAAAGY2YXkm2WqrrdJCCy2Uhg4dWmX5uHHj0u23357DdHj66adTr169UuvWrXOV+LDDDkvjx4+v8p6ffvop7b333mm++eZLiy++eLrqqqsqX6uuivvWW2/lz2/Xrl1+T2z//fffr7adU6ZMSYMGDUpLLbVUbkOPHj3SHXfcUeN+xbZjW5dddllad9110xJLLJE22GCDdOaZZ+bnIbYVevbsmdu20UYb5ecvvvhiDtoLLrhgat++ferdu3d65ZVXqnQ5D9tuu21+X+l5uPvuu9Pqq6+eK9lLL710GjhwYPrll19qbOeECRPS2LFjqzwAAADqSlieSaLSu8cee+SwXFFRUbk8gvLkyZPTLrvskkNndNXefvvtc5fsqNBGeD7kkEOqbOv888/PlehXX301HXTQQenAAw/MFd3qfPbZZ2nDDTdMLVu2TI899lh6+eWXc9CuKVhGUL7hhhvSFVdckYPwkUcemXbffff05JNPVrt+XABo2rRpDtSxH9V54YUX8n8feeSR3EU8KtDhxx9/THvuuWfex+effz4tt9xy6Xe/+11eXgrTpcp7vK/0PLqtx7E8/PDD0/Dhw9OVV16Zj+tZZ51V4/GP/YpAXnrEhQgAAIC6alJRnuSoVzGGd6WVVkqPP/54ZXU1gmxUY2+88ca0zz775G7UEf5KIkhGxTWqy1FFjepqVIZj/RBfV3S7jsrqAQcckCvLUcmNIL3aaqulE044Id1yyy05TM8zzzzVTvAV3aVjXHBUXzt27JhD7XrrrVe5TrQrqtk333xztft16aWXpj/+8Y+57RHiN95447Tbbrvlim8otqkmUdXu0KFD/pyohIeoKP/zn/+sMgnZpptumjbZZJN0/PHHVy7729/+ltvw+eefV7vt2Ld4lERlOQJz1yNuS01bzltjm6Bk1OAtG7oJAADMBJENoqAWQ0ujN25NVJZnohVXXDGtv/766a9//Wt+/t577+UqaakL9muvvZYrpG3btq189OnTJ4fImESrpHxsc4TJCMujR4+u9jOjO3aE6+qCclG0J0JxdI0ub0NUmmvqth0OPvjg9OWXX6abbroph+yolq+yyip5jHZtvvrqq7TvvvvminKcnHFiRrf0jz/+uNb3xXE6/fTTq7QxthPV52h/daKyHtsvfwAAANTVrJkVai4WwfjQQw/N1djoXrzMMsvkynGIoLj//vvnccpFMTa5pBh8IzBHoK5OjDuuq/j8cP/996fFFltsqrBZmxgLHTN+xyPGK0fIj/9G8K5JdMH+9ttv04UXXpir6/EZEbYnTpw4zXZGJX277bab6rWovgMAANQ3YXkm23HHHfNY2+hqHBXbGG8cYTfEhFUxBnfZZZett8+LKvT111+fZ62eVnV55ZVXzoE1KrulAD8jYn+iiv7ss8/m5y1atMj/LY5pfuaZZ/LEYDFOOXzyySfpm2++qbJOtLn4vjhO0a28Po8TAABAbXTDnsmiy/BOO+2Ux9tGt+EYM1xy3HHH5YAZE3pF9+l33303z/pcnOBresR7ow/+zjvvnF566aW8zRjvXN2EYFEdPuaYY/KkXhGwo+t1zE598cUX5+fViXbGPZVjgq8I+tGV+9prr81dzWN56NSpU65wP/jgg7nrdYwFCNH9Otry9ttvp//+9795nHOxEh5jtB999NHczfv777/Py0455ZR8oSGqyzEJWbw/xmWfdNJJM3ycAAAAaiMsz6Ku2BH8oqty586dq1SBY9bpd955J48zjlstRTAsX2d6LbDAAnkW7Oi6HNXiNdZYI1199dU1VpnPOOOMdPLJJ+fZo2MyspidO7pll27/VNSlS5ccaCO4rrPOOrnqG92q4/mJJ55YORP4RRddlCcui30phegI1XEc4j39+vXL3c8jWBdn/o6xzzEZVxyPEMftvvvuSw8//HBaa6218i2qLrjggtyVGwAAYGYwGzZz1Yx3ZsOmrsyGDQAwZzIbNgAAAMwgYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAgubFBTAne3Ngn9SuXbuGbgYAADCbU1kGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgoHlxAczJup36UGract40Jxo1eMuGbgIAAMwxVJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhmRm20UYbpSOOOKLy+ZJLLpmGDBlS63tOO+20tNpqq82C1gEAAMw4YXkO8OWXX6ZDDz00Lb300qlly5apa9euqW/fvunRRx+dpe148cUX03777Vf5vEmTJumuu+6qss4xxxwzy9sFAAAwvZpP9zuYrYwaNSptsMEGqUOHDunPf/5zWnXVVdOkSZPSQw89lA4++OA0YsSIWdaWhRZaaJrrtG3bNj8AAABmZyrLjdxBBx2UK7gvvPBC2n777dPyyy+fVllllXTUUUel559/Pq/z8ccfp6233jqH1Hbt2qUdd9wxffXVV1N1jb7xxhtzV+r27dunnXfeOf3444+V64wfPz7tscceeRuLLrpoOv/886dqS3k37Ph32HbbbXP7Ss+L3bCnTJmSTj/99NSlS5dcFY/XHnzwwSoXA+L9d955Z9p4443TvPPOm3r06JGee+65mXI8AQAAgrDciH333Xc5WEYFuU2bNlO9HtXmCKMRlGPdJ598Mv373/9OH3zwQdppp52qrPv+++/nLtP33XdffsS6gwcPrnz92GOPzcvuvvvu9PDDD6cnnngivfLKK7V2yQ7XXXdd+uKLLyqfF1144YU5eJ933nnp9ddfT3369Em///3v07vvvltlvRNPPDF34R42bFi+ILDLLrukX375pcbPnzBhQho7dmyVBwAAQF0Jy43Ye++9lyoqKtKKK65Y4zoxPviNN95IN998c1pjjTXSOuusk2644YYcfMsDbITqoUOHpm7duqVevXqlfv36VY4tHjduXLr22mtzoN1kk01yV+/rr7++1rBa6pIdgX2RRRapsYt2bPO4447LlewVVlghnXPOObm6XJwoLILylltumYPywIED00cffZT3vyaDBg3KFfLSI8ZxAwAA1JWw3IhFUJ6Wt99+OwfF8rC48sor5xAbr5VEN+n55puv8nl0tR49enRl1XnixIk5aJd07Ngxh9tfI6q9n3/+eR5zXS6el7ctdO/evUrbQql91Tn++OPTmDFjKh+ffPLJr2orAAAwdzHBVyO23HLL5fG89TGJ1zzzzFPleWw3qs2zi/L2RdtCbe2L8c/xAAAAmBEqy41YVHdjjO+ll16aJ+Aq+uGHH9JKK62Uq6rlldXhw4fn16LCXBfLLLNMDqv//e9/K5d9//336Z133qn1ffGeyZMn1/h6TDbWuXPn9Mwzz1RZHs/r2jYAAICZQWW5kYugHN2W11577TyrdHRXjrHEMZHX5ZdfnoNxjDHebbfd8jjgeC1m0O7du3dac8016/QZMQP2gAED8iRfCyywQOrUqVOecKtp09qvtUTX7hj3HO2LKu/8888/1TqxzVNPPTUH8hirHBOCxSReN9100wwfEwAAgF9LWG7kll566Twr9VlnnZWOPvroPPN0TKYVk3lFWI4uyzGD9aGHHpo23HDDHHC32GKLdPHFF0/X58Q9nGOir759++axzfFZMRa4NjHLddzC6uqrr06LLbZYvg1U0WGHHZa3E9uLMchRUb7nnntyF3MAAICG0qSiLrNEQSMXk4nlWbGPuC01bTlvmhONGrxlQzcBAAAaTTaIol0MDa2JMcsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFAjLAAAAUCAsAwAAQEHz4gKYk705sE9q165dQzcDAACYzaksAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECBsAwAAAAFwjIAAAAUCMsAAABQICwDAABAgbAMAAAABcIyAAAAFDQvLoA5WbdTH0pNW85bL9saNXjLetkOAAAw+1FZBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYbkf79+6dtttmm1nWeeOKJ1KRJk/TDDz/MsnYBAADMaYTl2UQE3Noep512WrrwwgvT0KFDK9+z0UYbpSOOOKLe2zJ58uQ0ePDgtOKKK6bWrVunjh07pnXWWSddc801v/qz6xL4AQAAGlrzhm4A/+eLL76o/Pett96aTjnllDRy5MjKZW3bts2PWWHgwIHpyiuvTJdccklac80109ixY9NLL72Uvv/++1ny+QAAAA1NZXk2scgii1Q+2rdvn6vJ5csiKJdXZePfTz75ZK42l6rPo0aNqnbbTz/9dOrVq1euEnft2jUddthhafz48TW25Z577kkHHXRQ+sMf/pCWWmqp1KNHjzRgwIB0zDHH1PrZUZGO9eI98VkrrLBCXqckquPXX399uvvuuyvfF93Gq+s6PmzYsCr79NFHH6W+ffum+eefP7Vp0yatssoq6YEHHqi34w8AAFBOWG6kIoSut956ad99981V6XhEEC56//330xZbbJG233779Prrr+eqdYTnQw45pMZtRzh/7LHH0tdffz1dnz1lypTUpUuXdPvtt6fhw4fn6vgJJ5yQbrvttvy+CNs77rhjbk/pfeuvv36d9vfggw9OEyZMSP/5z3/SG2+8kc4555xaK+2xblTEyx8AAAB1pRt2IxXV5xYtWqR55503h9uaDBo0KO22226V44uXW265dNFFF6XevXunyy+/PLVq1Wqq9/zlL39JO+ywQ95uVHAj0G699dbpf/7nf2r97GbNmuUu3CVRYX7uuedyWI6QHOE2Ks4RZGtrc3U+/vjjHPhXXXXV/HzppZeudf3Y7/K2AAAATA+V5Tnca6+9licFK415jkefPn1yFfjDDz+s9j0rr7xyevPNN9Pzzz+f9t577zR69OjcBXqfffaZ5uddeumlaY011kgLLbRQ/qyrrroqB91fK7qOn3nmmWmDDTZIp556aq6S1+b4449PY8aMqXx88sknv7oNAADA3ENYnsONGzcu7b///nkMcOkRAfrdd99NyyyzTI3va9q0aVprrbVyRfrOO+/Mgfvaa6+tMWCHW265JXe1jnHLDz/8cP6svfbaK02cOLHWNsZnhYqKisplkyZNqrJOBPUPPvgg9evXL3fDjonHLr744hq32bJly9SuXbsqDwAAgLrSDbsRi67QMalWbVZfffU8fnjZZZf9VZ8V1eZQmhisus9+5plncpftmBysfMz0tNocVegQY5hjAq8QQbsoxkUfcMAB+RGV46uvvjodeuihv2q/AAAAqqOy3IgtueSS6b///W+eMfqbb77JXauLjjvuuPTss8/mCb0igEZFOWajrm2CrxivfMEFF+RtxyzUMVt1TLC1/PLL53sv1/TZMR46bjH10EMPpXfeeSedfPLJ6cUXX5yqzdGFOm6LFe+LCnIE+QjCMVt2tO/+++9P559/fpX3RYU7thuV7VdeeSU9/vjjaaWVVqq3YwkAAFBOWG7EostzTKoVVd+ozlY3Nrh79+75Nk8RXuP2UT179syzVHfu3LnG7caY5nvvvTePU46AvOeee+aQHF2rmzdvXuNnR3fv7bbbLu20005pnXXWSd9++22VKnOIGbTjllLRjTreF9XoeeaZJ/39739PI0aMyO2Nma5jfHK5qEZHYI+AHLNpR7suu+yyejuWAAAA5ZpUlA8UhTlU3DoqZvHuesRtqWnLeetlm6MGb1kv2wEAAGZ9NoiJgGub20hlGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoaF5cAHOyNwf2Se3atWvoZgAAALM5lWUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgAJhGQAAAAqEZQAAACgQlgEAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBgAAgILmxQUwJ6qoqMj/HTt2bEM3BQAAaEClTFDKCDURlpkrfPvtt/m/Xbt2beimAAAAs4Eff/wxtW/fvsbXhWXmCh07dsz//fjjj2v9gYCZfRUzLth88sknqV27dg3dHOZSzkNmF85FZgfOw7lTRUVFDsqdO3eudT1hmblC06b/Nzw/grJfhDS0OAedhzQ05yGzC+ciswPn4dynfR0KaCb4AgAAgAJhGQAAAAqEZeYKLVu2TKeeemr+LzQU5yGzA+chswvnIrMD5yG1aVIxrfmyAQAAYC6jsgwAAAAFwjIAAAAUCMsAAABQICwDAABAgbDMHOHSSy9NSy65ZGrVqlVaZ5110gsvvFDr+rfffntaccUV8/qrrrpqeuCBB2ZZW5mzTc+5+NZbb6Xtt98+r9+kSZM0ZMiQWdpW5lzTcx5effXVqVevXmn++efPj0033XSav0NhZpyLd955Z1pzzTVThw4dUps2bdJqq62WbrzxxlnaXuZM0/t3Ysktt9yS//95m222meltZPYkLNPo3Xrrremoo47K0/6/8sorqUePHqlPnz5p9OjR1a7/7LPPpl122SUNGDAgvfrqq/kXYDzefPPNWd525u5z8aeffkpLL710Gjx4cFpkkUVmeXuZM03vefjEE0/k34mPP/54eu6551LXrl3T5ptvnj777LNZ3nbm7nOxY8eO6cQTT8zn4euvv5722muv/HjooYdmeduZe8/DklGjRqVjjjkmX0xk7uXWUTR6cYVwrbXWSpdcckl+PmXKlPzH3qGHHpr+9Kc/TbX+TjvtlMaPH5/uu+++ymXrrrtuvoJ9xRVXzNK2M3efi+XiivcRRxyRH9BQ52GYPHlyrjDH+/fYY49Z0GLmVL/2XAyrr7562nLLLdMZZ5wxk1vLnGpGzsP4PbjhhhumvffeOz311FPphx9+SHfdddcsbjmzA5VlGrWJEyeml19+OXcbLGnatGl+HlemqxPLy9cPcYWxpvVhZp2LMDueh9HjYdKkSbnKBw11LkYt59FHH00jR47MoQVm5Xl4+umnp06dOuVeiMzdmjd0A+DX+Oabb/LVv4UXXrjK8ng+YsSIat/z5ZdfVrt+LIdZeS7C7HgeHnfccalz585TXVSEWXEujhkzJi222GJpwoQJqVmzZumyyy5Lm2222SxoMXOiGTkPn3766XTttdemYcOGzaJWMjsTlgGALMbPx4Q2MY45JsKBWW2++ebLIWXcuHG5shxjTWNuh4022qihm8Zc4Mcff0z9+vXLEx8uuOCCDd0cZgPCMo1a/CKLK89fffVVleXxvKYJk2L59KwPM+tchNnpPDzvvPNyWH7kkUdS9+7dZ3JLmdPN6LkYXWSXXXbZ/O+YS+Ttt99OgwYNEpaZJefh+++/nyf26tu3b+WyGOMcmjdvnocFLLPMMrOg5cwujFmmUWvRokVaY4018tXn8l9q8Xy99dar9j2xvHz98O9//7vG9WFmnYswu5yH5557bp5A6cEHH8y37oHZ5XdivCe6ZMOsOA/jtqJvvPFG7t1Qevz+979PG2+8cf53TAzG3EVlmUYvumjtueee+Q+8tddeO9+rNma7jttNhJjNNcY/xZXpcPjhh6fevXun888/P8+wGV0OX3rppXTVVVc18J4wt52LMfHI8OHDK/8dt+qJ/zNu27ZtZWUFZvZ5eM4556RTTjkl3XzzzXlW9tL8DXEexgNm1bkY/411o3IXAfmBBx7I91m+/PLLG3hPmFvOwxh+0q1btyrvj/t+h+Jy5g7CMo1e3Arq66+/zn/sxR950W0rqiOlyRw+/vjj3K2rZP31189/FJ500knphBNOSMstt1y+HYBfgszqc/Hzzz9PPXv2rNINNh5xMSfGjMKsOA8jiMTFmh122KHKduKepKeddtosbz9z77kYAeaggw5Kn376aWrdunWu8v3tb3/L24FZdR5COfdZBgAAgAKXUQAAAKBAWAYAAIACYRkAAAAKhGUAAAAoEJYBAACgQFgGAACAAmEZAAAACoRlAAAAKBCWAQAAoEBYBoA5XP/+/VOTJk2merz33nv1sv2hQ4emDh06pIbex2222SbNrkaNGpWP+bBhwxq6KQDUUfO6rggANF5bbLFFuu6666osW2ihhdLsZtKkSWmeeeZJc5KJEyc2dBMAmAEqywAwF2jZsmVaZJFFqjyaNWuWX7v77rvT6quvnlq1apWWXnrpNHDgwPTLL79Uvvcvf/lLWnXVVVObNm1S165d00EHHZTGjRuXX3viiSfSXnvtlcaMGVNZsT7ttNPya/Hvu+66q0o7ogIdlejyauutt96aevfunT//pptuyq9dc801aaWVVsrLVlxxxXTZZZdN1/5utNFG6dBDD01HHHFEmn/++dPCCy+crr766jR+/Pjc3vnmmy8tu+yy6V//+lfle2Jfoj33339/6t69e/7sddddN7355ptVtv2Pf/wjrbLKKvmYLrnkkun888+v8nosO+OMM9Iee+yR2rVrl/bbb7+01FJL5dd69uyZPyPaF1588cW02WabpQUXXDC1b98+H4dXXnmlyvZi/Tge2267bZp33nnTcsstl+65554q67z11ltpq622yp8X+9arV6/0/vvvV77+a48nwNxIWAaAudhTTz2VQ93hhx+ehg8fnq688socZs8666zKdZo2bZouuuiiHMiuv/769Nhjj6U//vGP+bX1118/DRkyJIe0L774Ij+OOeaY6WrDn/70p/z5b7/9durTp08OzKecckpuQyw7++yz08knn5w/e3rE+hFCX3jhhRycDzzwwPSHP/whtzkC6eabb5769euXfvrppyrvO/bYY3MAjiAb1fe+ffvmind4+eWX04477ph23nnn9MYbb+QLA9G20gWAkvPOOy/16NEjvfrqq/n1aEN45JFH8jG688478/Mff/wx7bnnnunpp59Ozz//fA7Cv/vd7/LycnEBIz739ddfz6/vtttu6bvvvsuvffbZZ2nDDTfM4T2+m2jj3nvvXXnBo76OJ8BcpwIAmKPtueeeFc2aNato06ZN5WOHHXbIr22yySYVZ599dpX1b7zxxopFF120xu3dfvvtFQsssEDl8+uuu66iffv2U60Xf2b885//rLIs1ov1w4cffpjXGTJkSJV1lllmmYqbb765yrIzzjijYr311qt1H7feeuvK57179674zW9+U/n8l19+yfvdr1+/ymVffPFF/vznnnsuP3/88cfz81tuuaVynW+//baidevWFbfeemt+vuuuu1ZsttlmVT772GOPrVh55ZUrny+xxBIV22yzTZV1Svv66quvVtRm8uTJFfPNN1/FvffeW7ks3nfSSSdVPh83blxe9q9//Ss/P/744yuWWmqpiokTJ1a7zRk5ngBUVBizDABzgY033jhdfvnllc+jS3V47bXX0jPPPFOlkjx58uT0888/54prdPuNauigQYPSiBEj0tixY3PFsvz1X2vNNdes/Hd0k47uwwMGDEj77rtv5fL4zOimPD2iK3VJdDlfYIEFcnfykuiaHUaPHl3lfeutt17lvzt27JhWWGGFXJEN8d+tt966yvobbLBBrq7HcSt1bS/fp9p89dVX6aSTTspdwKMdsY04rh9//HGN+xLfXVTyS+2OScOi23V1Y73r83gCzG2EZQCYC0TAijG6RTH2OLr4brfddlO9FuNbY1xxjIWNLswRqCM8RpfhCF8xcVVtYTnG2v5fYfT/K3VnLratvD0hxhevs846VdYrBdG6KobHaE/5sngepkyZkupb+T7VJrpgf/vtt+nCCy9MSyyxRO5KHWG9OClYdftSanfr1q1r3H59Hk+AuY2wDABzsZjYa+TIkdUG6RDjXyOUxRjeGLscbrvttirrtGjRIldEi2K8b4zPLXn33XenGh9cFNXezp07pw8++CCPy20IMXZ48cUXz//+/vvv0zvvvJMnxwrx36jEl4vnyy+/fK3hM45RKB6neG9MthXjkMMnn3ySvvnmm+lqb1SdY/xxdTOJzw7HE6CxEpYBYC4WEz9F5TjC4Q477JADcXTNjhmgzzzzzByiI4RdfPHFeaKrCHdXXHHFVLM/RwXz0UcfzZNaRbU5Hr/97W/TJZdckiulERKPO+64Ot0WKirdhx12WO4mHLe8mjBhQnrppZdycD3qqKPSzHb66afnLtsRNE888cQ8SVjpHs5HH310WmuttfJs1zvttFN67rnn8j5Oa3bpTp065Qrwgw8+mLp06ZKr9rF/MaHXjTfemLttRxf3mFystkpxdQ455JD8/cSkY8cff3zebgT+tddeO3chb+jjCdBYmQ0bAOZiMfv0fffdlx5++OEcAuNWSRdccEHuEhwi/Mato84555zUrVu3PLNyjF8uF7NLH3DAATk8RjX53HPPzcujGh23morxtLvuumueJbsuY5z32WeffKujuC90jDGO2ynFbNOl2y/NbIMHD86zc6+xxhrpyy+/TPfee29lZTgq8VFZv+WWW/LxiIsNEa779+9f6zabN2+eZxSP2caj0lsa93zttdfm0BrbjZm5I9RGsJ4eEexjFuy4YBHHKtod3a5LFyYa+ngCNFZNYpavhm4EAEBDi0m2YiK0CK9xP2gA5m4qywAAAFAgLAMAAECBbtgAAABQoLIMAAAABcIyAAAAFAjLAAAAUCAsAwAAQIGwDAAAAAXCMgAAABQIywAAAFAgLAMAAECq6v8Bns4jsjZkkaIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get feature importances from the trained model\n",
    "importances = pd.Series(model.feature_importances_, index=X_train.columns)\n",
    "\n",
    "# Combine one-hot columns back into their original feature since original graph was showing all features making it unreadable.\n",
    "grouped = importances.groupby(lambda col: col.split('_')[0]).sum()\n",
    "grouped = grouped.sort_values()\n",
    "\n",
    "# Plot the feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(grouped.index, grouped.values)\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Feature Importances in Random Forest')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}