Features, Exploratio and Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading\n",
    "Let's load the data and take a look into what is in there and how we can use it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "#Install packages needed\n",
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Imports\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets load the data from the csv file generated by the scraper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alfa Romeo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Audi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brilliance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Changan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Changhe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chevrolet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chrysler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Citroën</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DFM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>DS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Daihatsu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Dodge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ferrari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Fiat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Foton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GWM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Geely</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Hafei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Haval</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Hyundai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>JAC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Jeep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Jmc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Kia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Land Rover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Lexus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Lincoln</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Maserati</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Mazda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Mercedes Benz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Mini</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Mitsubishi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Nissan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Peugeot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Porsche</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Renault</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Seat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Skoda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Ssangyong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Subaru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Suzuki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Toyota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Volkswagen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Volvo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Zotye</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "0      Alfa Romeo\n",
       "1            Audi\n",
       "2             BMW\n",
       "3      Brilliance\n",
       "4           Chana\n",
       "5         Changan\n",
       "6         Changhe\n",
       "7           Chery\n",
       "8       Chevrolet\n",
       "9        Chrysler\n",
       "10        Citroën\n",
       "11            DFM\n",
       "12             DS\n",
       "13       Daihatsu\n",
       "14          Dodge\n",
       "15        Ferrari\n",
       "16           Fiat\n",
       "17           Ford\n",
       "18          Foton\n",
       "19            GWM\n",
       "20          Geely\n",
       "21          Hafei\n",
       "22          Haval\n",
       "23          Honda\n",
       "24        Hyundai\n",
       "25            JAC\n",
       "26           Jeep\n",
       "27            Jmc\n",
       "28            Kia\n",
       "29     Land Rover\n",
       "30          Lexus\n",
       "31        Lincoln\n",
       "32             MG\n",
       "33       Maserati\n",
       "34          Mazda\n",
       "35  Mercedes Benz\n",
       "36           Mini\n",
       "37     Mitsubishi\n",
       "38         Nissan\n",
       "39        Peugeot\n",
       "40        Porsche\n",
       "41        Renault\n",
       "42           Seat\n",
       "43          Skoda\n",
       "44      Ssangyong\n",
       "45         Subaru\n",
       "46         Suzuki\n",
       "47         Toyota\n",
       "48     Volkswagen\n",
       "49          Volvo\n",
       "50          Zotye"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nº of different engines 302\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2016 entries, 0 to 2015\n",
      "Data columns (total 16 columns):\n",
      "brand           2016 non-null object\n",
      "color           2016 non-null object\n",
      "engine          1715 non-null object\n",
      "fuel            1744 non-null object\n",
      "id              2016 non-null object\n",
      "image_urls      2016 non-null object\n",
      "images          2005 non-null object\n",
      "location        2016 non-null object\n",
      "mileage         2016 non-null int64\n",
      "model           2016 non-null object\n",
      "onlyOwner       1199 non-null object\n",
      "price           2016 non-null object\n",
      "steering        2016 non-null object\n",
      "traction        1405 non-null object\n",
      "transmission    2016 non-null object\n",
      "year            2016 non-null int64\n",
      "dtypes: int64(2), object(14)\n",
      "memory usage: 252.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./get_data/crawl_data.csv')\n",
    "brands = pd.DataFrame(df.brand.unique()).sort_values(0).reset_index(drop=True)\n",
    "display(brands)\n",
    "print('Nº of different engines %s' % len(df.engine.unique()))\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot of different type of engines, they are all in different formats (some ar in liters others in C.C),  and some don't even have that, they just put the name.\n",
    "\n",
    "Because of this there is not an accesible way to make this information reliable so I choose to delete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>brand</th>\n",
       "      <th>color</th>\n",
       "      <th>fuel</th>\n",
       "      <th>id</th>\n",
       "      <th>location</th>\n",
       "      <th>mileage</th>\n",
       "      <th>model</th>\n",
       "      <th>onlyOwner</th>\n",
       "      <th>price</th>\n",
       "      <th>steering</th>\n",
       "      <th>traction</th>\n",
       "      <th>transmission</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Negro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>MCO477026829</td>\n",
       "      <td>El vehículo está en Cedritos - Usaquén - Bogot...</td>\n",
       "      <td>0</td>\n",
       "      <td>Gol</td>\n",
       "      <td>Sí</td>\n",
       "      <td>34.990.000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>MCO481357221</td>\n",
       "      <td>El vehículo está en Perpetuo Socorro - Medellí...</td>\n",
       "      <td>0</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>Sí</td>\n",
       "      <td>90.990.000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Gris</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>MCO483493511</td>\n",
       "      <td>El vehículo está en Turin - Pereira - Risaralda</td>\n",
       "      <td>102973</td>\n",
       "      <td>Aveo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.500.000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Plateado</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>MCO485896179</td>\n",
       "      <td>El vehículo está en Normandía - Engativa - Bog...</td>\n",
       "      <td>63000</td>\n",
       "      <td>Captiva</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.900.000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>MCO477152685</td>\n",
       "      <td>El vehículo está en Cedritos - Usaquén - Bogot...</td>\n",
       "      <td>0</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Sí</td>\n",
       "      <td>109.990.000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        brand     color      fuel            id  \\\n",
       "0  Volkswagen     Negro  Gasolina  MCO477026829   \n",
       "1     Hyundai      Otro  Gasolina  MCO481357221   \n",
       "2   Chevrolet      Gris  Gasolina  MCO483493511   \n",
       "3   Chevrolet  Plateado  Gasolina  MCO485896179   \n",
       "4  Volkswagen    Blanco  Gasolina  MCO477152685   \n",
       "\n",
       "                                            location  mileage    model  \\\n",
       "0  El vehículo está en Cedritos - Usaquén - Bogot...        0      Gol   \n",
       "1  El vehículo está en Perpetuo Socorro - Medellí...        0   Tucson   \n",
       "2    El vehículo está en Turin - Pereira - Risaralda   102973     Aveo   \n",
       "3  El vehículo está en Normandía - Engativa - Bog...    63000  Captiva   \n",
       "4  El vehículo está en Cedritos - Usaquén - Bogot...        0     Golf   \n",
       "\n",
       "  onlyOwner        price    steering traction transmission  year  \n",
       "0        Sí   34.990.000    Asistida      NaN     Mecánica  2019  \n",
       "1        Sí   90.990.000    Asistida      4x2   Automática  2018  \n",
       "2       NaN   16.500.000  Hidráulica      NaN     Mecánica  2008  \n",
       "3       NaN   28.900.000  Hidráulica      NaN   Automática  2011  \n",
       "4        Sí  109.990.000    Asistida      4x2   Automática  2019  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.drop(columns=['engine', 'image_urls', 'images'])\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "next variable to be deal with is the location, this starts with *El vehículo está en* wich needs to be removed, and its also separated by hyphens that also need to be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_location(locations, starts_with='El vehículo está en ',):\n",
    "    location_only_places = locations.str.replace(starts_with, '')\n",
    "    locations_split = location_only_places.str.split('-')\n",
    "    location_reverse = locations_split.apply(reversed).apply(list)\n",
    "    return location_reverse\n",
    "\n",
    "def get_state_city(location):\n",
    "    return pd.DataFrame(location.values.tolist())[[0,1]]\n",
    "    \n",
    "loc_temp = clean_location(df.location)\n",
    "df_locations = get_state_city(loc_temp)\n",
    "df_locations.columns = ['State', 'City']\n",
    "df = pd.concat([df,df_locations], axis=1)\n",
    "df = df.drop(columns=['location'])\n",
    "df = df.set_index('id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets do some data exploration.\n",
    "\n",
    "The first thing its see the distribution of our data. One that interest me its how many samples of each car and models do we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9sr36KaPJ58TTPqBrZgPEoV+kpl0HWNG0hytOH99n2xw2qIIzJo7guQ272XfIJ2uZWf9z6BfpZytfA+CK9xzfp9t977QxtLR18PDSpj7drplZdxz6RVr4whZOm1DTZ0M7nSaNHsKkUYP5/lPr6fDXN82snzn0i7Blz0Ge27C7z/fyO50//TjW7zjA4y9t7Zftm5l1cugXYVHn0E4fjucXOm3CCCaOHMx3f7muX7ZvZtbJoV+EhStf4+Rxwzmxdli/bD+fE5983xSeeWUnKzf5B9TNrP849Huwdd8hlqzfyRWn98/QTqfrZk9iaFWe7/3qlX59HDPLtqJ+OUvSXOAfgDzw3Yj4apflg4AfAucAO4DrImK9pCkkP6a+Ju36dETc3DelD4xFq14nAq54z5EN7dz3zIai+tVUV/Kxcyfxo9+8yhfnnsLxIw7/m7tmZkeqxz19SXngLuAKYCZwvaSZXbp9CtgVEdOBvwf+pmDZ2og4M70dU4EP8G8rNjOtdignjeufoZ1Cf/i+qXRE8P2n1vf7Y5lZNhUzvDMbaIyIdRHRAtwPzOvSZx7wg3T6IeCDOtqL05SB1/ce4plXdnLVrAlHfa2dYkweM4Qrz5jAD3+znu1vNPf745lZ9hQT+hOBjQXzTWlbt30iog3YA4xJl02VtEzSf0j6QHcPIOkmSQ2SGrZt29arP6A//duKLUTAlWdMGLDH/NwlM2hu6+Dbi9cO2GOaWXb094HcLcDkiDgL+Dxwn6Sarp0i4p6IqI+I+tra2n4uqXg/WbGZmeNrmD62/4d2Ok2rHcbvnT2Rf3rmVbbsOThgj2tm2VBM6G8CJhXM16Vt3faRVAGMAHZERHNE7ACIiKXAWuCkoy16IGzceYBlG3bzu7MGbi+/02c+OIOI4JuPNw74Y5vZu1sxob8EmCFpqqQqYD6woEufBcCN6fQ1wBMREZJq0wPBSJoGzACOiTOQfrpiCwBXntE/J2S9k7pRQ7hh9mQeaNjI+u37B/zxzezdq8fQT8fobwUWkXz98oGIWCXpTklXpd2+B4yR1EgyjHNb2n4BsELScpIDvDdHxM6+/iP6w0+e38xZk0cyafSQkjz+LRdNpzIvvr5oTc+dzcyKVNT39CNiIbCwS9sdBdOHgGu7We9h4OGjrHHANW59g9Vb9nLHlV2/mTpwxtZUc8uF0/nbx37LVate4/LT+vfkMDPLBp+R242fPL8ZCT5cgqGdQjdfeCIzx9fwpUdWsvtAS0lrMbN3B4d+F+0dwYMNGzn/xOMYV1Pas2Ir8zn+x7Wz2H2ghTt/srqktZjZu4NDv4snXtrK5j2H+MR7Tyh1KQDMnFDDpy+azr8u28TjL75e6nLM7Bjn0O/iR0+/yvE11Vxy6thSl/KmWy+azinHD+cLDz7P2m1vlLocMzuGOfQLrN++nyd/u40b5kymIl8+T01VRY67f/8cKnLixnufZeveQ6UuycyOUeWTbGXgn55+lYqcmH/upJ47D7ATxgzl3k+ey879LXzyH5f4h9TN7Ig49FMHW9p5cGkTl7/neMaW+ADu4ZxRN5Jvf/xs1ry+j0/9oIGd+/2NHjPrnaK+p58FP1mxmT0HW/n9MjmAezgXnjyWa86p4+GlTXzwb3/Bx+ecwISRgwG4Yc7kEldnZuXOe/pAW3sH9zy5jpPGDWPO1NGlLqdHs+pGctMF0+gIuPvJtTz36i4iotRlmdkxwKEP/HjJRhq3vsEXLjt5QK6b3xfqRg3h0xeeyMSRg3nouSbueXIdz2/cXeqyzKzMZT709x5q5RuP/ZbZU0dz2cxxpS6nV4ZXV/LHH5jG1WdOZPv+Fubd9Wv+9MfLeG6D9/zNrHuZH9P/9uK17Njfwvc/PPOY2csvlJOYPXU0Z9SNYNsbzfzwqfX85PnNnDq+hutnT+Ly044v+ZnFZlY+Mh36G3ce4N5fvcJHz57I6XUjSl3OUamuzPPFuadwy0XTeXT5Jv756Q3c8egq7nh0FWfUjeDiU8Zy3rQxzJo0kurKfKnLNbMSyWzoRwR3/nQ1uRz82eUnl7oc7ntmQ7ftvflGTuc2hPj4nMm8vq+Zl7bs5cUte/mHn7/MN3iZipw4+4RRnDV5JLPqRjJr0kgmjKg+Jj/lmFnvZTb0//7nL/PY6tf50odOZfyIwaUu57AO92bQE0kcX1PN8TXVXHjyWA60tPHqjgO8sn0/+w618o+/Wk9LewcAo4dWcdqEGk6bMIKZE2qYOX44U48bRj6nPnkzMrPykcnQf2RZE998/GU+Vl/HH39gaqnLGRBDqio4dXwNp46v4YY5k2lua+elLft4vmk3qzbtZeXmPXzvV+tobU8OAFdX5jhp3HAq8zmOr6lmXE0142oGMWxQhT8VmB3DMhf6z76yky8+9ALnTRvDX199eiYDrHDvvSKXY9akZJjnmnPqaNz6Bi9u2cvqLXtZ89o+lm3YxdJXd73Zf0hVnrHDq3lh0x6mjx3GtNqhTB0zlLpRg8vqekVm1r3MhH5EcN+zG/jrn75I3ejBfOcT51BV4ZAqVFWRS4Z3JtTwe2nbfc9sYN+hVl7f28zrew/x+t5DbNvXzL+v3MLuA29d/6ciJ+pGDWbS6CFMGj2EulGDmThyMKs372XkkCqGV1eQK3iDPdrhoe6GnTzkZNazokJf0lzgH4A88N2I+GqX5YOAHwLnADuA6yJifbrsduBTQDvwmYhY1GfVF2nbvma++PAKnnhpK++ffhx/+7FZjBhSOdBllL3Djd8Pr65keHUl08cOe7MtItjf0s72fc3s2N/M9jda2Lm/hVe272fpq7s40NL+tm3klGynprqC4dWVvPTaXmqHDWL0sCrGDK1i1JAqagZXMry6guGDKhlUmaMqnyOXe/snsYigtT1obmuntT1obe+gta2D1vbgmXU7ONjazqHWdg61dnCotZ2W9g6eXreT9o6gI4IICIJZdSPJ50Q+JyrzOaorc1RX5Fn66i4GpdPVlXmGVOX5o/dPJZ/L3idCe3dSTyfxSMoDvwUuBZqAJcD1EbG6oM+ngTMi4mZJ84GPRMR1kmYCPwZmAxOAnwMnRUR718fpVF9fHw0NDUf5Z0FHR7Bk/U4eWtrEwhe20NoR3H7FKdx43pT/L0j6wpEecH23am5rZ/eB1uR2sIU9B1vZe7CVPQdb2Xeojea2DvYc7PlKoRU5vfkJIYg3jzkMJAlGDK5kzNAqxgwblN5XMXpoMj1ySCWjhiT3w6srGTaoguHVFQyqyGVy+NBKQ9LSiKjvqV8xe/qzgcaIWJdu+H5gHlD4+33zgL9Ipx8CvqXkX/s84P6IaAZekdSYbu83xf4hxTrQ0sbil7bx0mvJVxRf2LSH1/c2M2xQBVeeMYE/uWAq08cO7+uHtcMYVJFnXE3+sCeGdR5M3rm/hR1vtLDrQAv7DrWx71DyptDS3kFLWwfNbR1vW68yn2NQRY6Vm/ZQkc9Rme6pV1XkmPue46muzDO4Mp/suVfmGVSR49Hlm8lJ5HLJ11klkj3+CDoi+YnM1vYO2jqSTxDNrR0caks+MRxoSW77m9vY39zGtn3NvLJ9P/ub2zjY2s477TNJpJ8Ykvoqcsl9PifySj5lVOSTN7WK9FPHjjdayOUgn0v6VaR9Txk/nKqK5NNP4X1lPkdlRfI8VORzVOZFRbpuZf6tTzKd26rI59L7dP7Nvrk3P/m8+bjpG25yT9m/gRW+nh0RtHcE7RG0t6f3HW+/tXUEEcmyjo5kp6Lw9ZSSfy+5XHISZE7p65L+W6ooeI1ync9duqzz9S3H56yY0J8IbCyYbwLmHK5PRLRJ2gOMSduf7rLuxCOu9h0cau3glvueI58T044bypypY7jw5Frmvud4hlRl5tDFMeOdPhkNqapgSMF8d2P13a3/6o4D3W6v25PR9Ob/ATCY3p+w1t4RHGhp42BLOwfTN4izTxjJG4fa2HuojebWpH1F0563wiYNpo6OYMLI6jfDp/O+pb2D9tagI9rebG/vCNbv2J+8CaZvhqWSUxKAnYGY/o/CbFPB8/r29ndW+P5ZGL5RsCQZnks6d4Z0RyQ9yvXKI4d7zuCtN5ZOZ9SN4F/+03n9Wk9ZpKGkm4Cb0tk3JK0pWHwcsL0321tHMo70P/umvMPpdV0D6F1V28f7qZAu3lXP2QAp17rgGK3tJeCBm494u0VdF76Y0N8EFP6UVF3a1l2fJkkVwAiSA7rFrEtE3APc092DS2ooZpxqoJVrXeDajkS51gXlW1u51gWu7Z0U853FJcAMSVMlVQHzgQVd+iwAbkynrwGeiOQI8QJgvqRBkqYCM4Bn+6Z0MzPrrR739NMx+luBRSRf2bw3IlZJuhNoiIgFwPeAH6UHaneSvDGQ9nuA5KBvG3DLO31zx8zM+ldRY/oRsRBY2KXtjoLpQ8C1h1n3K8BXjqLGbod9ykC51gWu7UiUa11QvrWVa13g2g6rx+/pm5nZu4evQ2BmliFlGfqSrpW0SlKHpPouy26X1ChpjaTLS1Tf3PTxGyXdVooaCmq5V9JWSSsL2kZLekzSy+n9qBLUNUnSYkmr09fys2VUW7WkZyU9n9b2l2n7VEnPpK/rv6RfXBhwkvKSlkn6aZnVtV7SC5KWS2pI20r+eqZ1jJT0kKSXJL0o6bxS1ybp5PS56rztlfS5UtdVlqEPrAQ+CjxZ2Jhe1mE+cBowF/h2epmIAZM+3l3AFcBM4Pq0rlL5PslzUeg24PGImAE8ns4PtDbgCxExE3gvcEv6PJVDbc3AxRExCzgTmCvpvcDfAH8fEdOBXSTXjCqFzwIvFsyXS10AF0XEmQVfOSyH1xOSa4P9LCJOAWaRPH8lrS0i1qTP1Zkk1yU7ADxS6rqIiLK9Ab8A6gvmbwduL5hfBJw3wDWdByw6XE0lep6mACsL5tcA49Pp8cCaMngtHyW5flNZ1QYMAZ4jOct8O1DR3es8gPXUkQTBxcBPSU7eLHld6WOvB47r0lby15PkvKBXSI9RllNtBbVcBvy6HOoq1z39w+nukhD9clmHMq+hJ+MiYks6/RowrpTFSJoCnAU8Q5nUlg6hLAe2Ao8Ba4HdEdGWdinV6/oN4L8CnddaGFMmdUFyBYT/K2lpehY9lMfrORXYBvxjOiz2XUlDy6S2TvNJLj4JJa6rZKEv6eeSVnZzm1eqmt6NItmdKNlXtCQNAx4GPhcRewuXlbK2iGiP5GN3HclFAE8pRR2FJF0JbI2IpaWu5TDeHxFnkwxt3iLpgsKFJXw9K4Czgf8VEWcB++kyZFLKf2vpMZirgAe7LitFXSW79k5EXHIEqxV1WYd+Vg419OR1SeMjYouk8SR7swNOUiVJ4P9zRPxrOdXWKSJ2S1pMMmwyUlJFuldditf1fOAqSR8CqoEakrHqUtcFQERsSu+3SnqE5M2yHF7PJqApIp5J5x8iCf1yqA2SN8nnIuL1dL6kdR1rwzvlcFmHYi5LUWqFl8W4kWQ8fUBJEsmZ2i9GxN+VWW21kkam04NJjjW8CCwmuYxISWqLiNsjoi4ippD8u3oiIj5e6roAJA2VNLxzmmSMeiVl8HpGxGvARkknp00fJLkKQMlrS13PW0M7UOq6SnVgo4eDHh8hefduBl7n7QdOv0Qy/roGuKJE9X2I5Idl1gJfKvFz9WNgC9CaPmefIhkHfhx4meSCo6NLUNf7ST62rgCWp7cPlUltZwDL0tpWAnek7dNIdiIaST6KDyrh63oh8NNyqSut4fn0tqrz3305vJ5pHWcCDelr+n+AUeVQGzCU5OKTIwraSlqXz8g1M8uQY214x8zMjoJD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M36QXo1xSGlrsOsK39l06wfSFpPcrHA7aWuxayQ9/QtsyT9gaQV6XX1fyRpiqQn0rbHJU1O+31f0jUF672R3l8o6RcF13H/ZyU+A0wAFqeXeDArGyW79o5ZKUk6Dfhz4H0RsV3SaOAHwA8i4geS/gj4JnB1D5s6i+T3HTYDvwbOj4hvSvo8ybXnvadvZcV7+pZVFwMPdoZyROwkuejafenyH5FcSqInz0ZEU0R0kFxqYko/1GrWZxz6Zj1rI/1vRVIOKPy5wuaC6Xb86dnKnEPfsuoJ4FpJYyD5rVfQGD00AAAAkUlEQVTgKZKrWwJ8HPhlOr2e5OfuILkuemUR298HDO+rYs36ivdKLJMiYpWkrwD/Iamd5Kqbf0ry60t/RvJLTH+Ydv/fwKOSngd+RvIjHT25B/iZpM0RcVHf/wVmR8Zf2TQzyxAP75iZZYhD38wsQxz6ZmYZ4tA3M8sQh76ZWYY49M3MMsShb2aWIQ59M7MM+X8Muhf8vrBPAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.groupby(['brand','model']).size().reset_index().rename(columns={0: 'count'})['count']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected this distribution its highly skewed, which means that we have an over representation of some car and models and under representation of other. This makes sense since some cars an brands are more popular in the market than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean   56487816.79365079\n",
      "STD    48814677.612826034\n",
      "Median 43900000.0\n",
      "IQR    43500000.0\n"
     ]
    }
   ],
   "source": [
    "df['price'] = df['price'].map(lambda x: x.replace('.', '')).astype(int)\n",
    "print('Mean   {}'.format(df['price'].mean()))\n",
    "print('STD    {}'.format(df['price'].std()))\n",
    "print('Median {}'.format(df['price'].median()))\n",
    "print('IQR    {}'.format(scipy.stats.iqr(df['price'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The standard deviation its quite large compare to the mean, plus the fact that the mean its well above the median leads me to believe that there multiple outliers, to confirm this let's lock a boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(df['price'], density=True, alpha=0.7)\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "def millions(x, pos):\n",
    "    return '$%1.1fM' % (x*1e-6)\n",
    "\n",
    "formatter = FuncFormatter(millions)\n",
    "plt.figure(figsize=(10,6));\n",
    "\n",
    "ax = sns.boxplot(y='price', data=df);\n",
    "ax.yaxis.set_major_formatter(formatter);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That its interesting, there seams to be a large number of *\"outliers\"*, lets see at least the ones over the 0.99 quantile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>brand</th>\n",
       "      <th>color</th>\n",
       "      <th>fuel</th>\n",
       "      <th>mileage</th>\n",
       "      <th>model</th>\n",
       "      <th>onlyOwner</th>\n",
       "      <th>price</th>\n",
       "      <th>steering</th>\n",
       "      <th>traction</th>\n",
       "      <th>transmission</th>\n",
       "      <th>year</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MCO482030566</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>0</td>\n",
       "      <td>Prado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Magdalena</td>\n",
       "      <td>Santa Marta (distrito Turistico Cultural E His...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487849596</th>\n",
       "      <td>Lexus</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>30600</td>\n",
       "      <td>GX</td>\n",
       "      <td>Sí</td>\n",
       "      <td>233000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2016</td>\n",
       "      <td>Casanare</td>\n",
       "      <td>Yopal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487147406</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>0</td>\n",
       "      <td>LC200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>430000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2019</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO486658262</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1500</td>\n",
       "      <td>Serie 6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2014</td>\n",
       "      <td>Cundinamarca</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO484464269</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>LC200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>390000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2019</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO485693920</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12450</td>\n",
       "      <td>X5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250000000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2016</td>\n",
       "      <td>Norte De Santander</td>\n",
       "      <td>Cúcuta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487847418</th>\n",
       "      <td>Lexus</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>30600</td>\n",
       "      <td>GX</td>\n",
       "      <td>Sí</td>\n",
       "      <td>233000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2016</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482103954</th>\n",
       "      <td>Mercedes Benz</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>0</td>\n",
       "      <td>Clase GL</td>\n",
       "      <td>Sí</td>\n",
       "      <td>660000000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482030739</th>\n",
       "      <td>Lexus</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>0</td>\n",
       "      <td>LX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>640000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO486323614</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Azul</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>1000</td>\n",
       "      <td>X6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370000000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO489324879</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>45000</td>\n",
       "      <td>X5</td>\n",
       "      <td>Sí</td>\n",
       "      <td>235000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2016</td>\n",
       "      <td>Valle Del Cauca</td>\n",
       "      <td>Cartago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482893625</th>\n",
       "      <td>Lexus</td>\n",
       "      <td>Negro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>64471</td>\n",
       "      <td>LX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>325000000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2014</td>\n",
       "      <td>Antioquia</td>\n",
       "      <td>Medellín</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487791614</th>\n",
       "      <td>Porsche</td>\n",
       "      <td>Gris</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>35000</td>\n",
       "      <td>Cayenne</td>\n",
       "      <td>Sí</td>\n",
       "      <td>380000000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2016</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO490074758</th>\n",
       "      <td>Mercedes Benz</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>13885</td>\n",
       "      <td>Clase GLE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>295000000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Antioquia</td>\n",
       "      <td>Medellín</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482424511</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Plateado</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>31125</td>\n",
       "      <td>GT-R</td>\n",
       "      <td>No</td>\n",
       "      <td>270000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2009</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482030619</th>\n",
       "      <td>Ferrari</td>\n",
       "      <td>Rojo</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>13845</td>\n",
       "      <td>Otros Modelos</td>\n",
       "      <td>No</td>\n",
       "      <td>750000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2010</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO484548593</th>\n",
       "      <td>Porsche</td>\n",
       "      <td>Rojo</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>17000</td>\n",
       "      <td>Cayman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2014</td>\n",
       "      <td>Santander</td>\n",
       "      <td>Bucaramanga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO479496878</th>\n",
       "      <td>Maserati</td>\n",
       "      <td>Negro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>0</td>\n",
       "      <td>Levante</td>\n",
       "      <td>Sí</td>\n",
       "      <td>379172000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO484871465</th>\n",
       "      <td>Porsche</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>39001</td>\n",
       "      <td>Macan</td>\n",
       "      <td>Sí</td>\n",
       "      <td>310000000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2015</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Usaquén</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO491109462</th>\n",
       "      <td>Porsche</td>\n",
       "      <td>Negro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>6666</td>\n",
       "      <td>Boxster</td>\n",
       "      <td>No</td>\n",
       "      <td>255000000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2014</td>\n",
       "      <td>Antioquia</td>\n",
       "      <td>Medellín</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO491684529</th>\n",
       "      <td>Kia</td>\n",
       "      <td>Plateado</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>300000</td>\n",
       "      <td>Rio</td>\n",
       "      <td>No</td>\n",
       "      <td>300000000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>2009</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Kennedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      brand     color      fuel  mileage          model  \\\n",
       "id                                                                        \n",
       "MCO482030566         Toyota    Blanco    Diesel        0          Prado   \n",
       "MCO487849596          Lexus    Blanco  Gasolina    30600             GX   \n",
       "MCO487147406         Toyota    Blanco  Gasolina        0          LC200   \n",
       "MCO486658262            BMW    Blanco       NaN     1500        Serie 6   \n",
       "MCO484464269         Toyota    Blanco       NaN        0          LC200   \n",
       "MCO485693920            BMW    Blanco       NaN    12450             X5   \n",
       "MCO487847418          Lexus    Blanco  Gasolina    30600             GX   \n",
       "MCO482103954  Mercedes Benz    Blanco  Gasolina        0       Clase GL   \n",
       "MCO482030739          Lexus    Blanco  Gasolina        0             LX   \n",
       "MCO486323614            BMW      Azul  Gasolina     1000             X6   \n",
       "MCO489324879            BMW    Blanco    Diesel    45000             X5   \n",
       "MCO482893625          Lexus     Negro  Gasolina    64471             LX   \n",
       "MCO487791614        Porsche      Gris  Gasolina    35000        Cayenne   \n",
       "MCO490074758  Mercedes Benz    Blanco    Diesel    13885      Clase GLE   \n",
       "MCO482424511         Nissan  Plateado  Gasolina    31125           GT-R   \n",
       "MCO482030619        Ferrari      Rojo  Gasolina    13845  Otros Modelos   \n",
       "MCO484548593        Porsche      Rojo  Gasolina    17000         Cayman   \n",
       "MCO479496878       Maserati     Negro  Gasolina        0        Levante   \n",
       "MCO484871465        Porsche    Blanco  Gasolina    39001          Macan   \n",
       "MCO491109462        Porsche     Negro  Gasolina     6666        Boxster   \n",
       "MCO491684529            Kia  Plateado  Gasolina   300000            Rio   \n",
       "\n",
       "             onlyOwner      price    steering traction transmission  year  \\\n",
       "id                                                                          \n",
       "MCO482030566       NaN  310000000    Asistida      4x4   Automática  2018   \n",
       "MCO487849596        Sí  233000000    Asistida      4x4   Automática  2016   \n",
       "MCO487147406       NaN  430000000    Asistida      4x4   Automática  2019   \n",
       "MCO486658262       NaN  250000000    Asistida      NaN   Automática  2014   \n",
       "MCO484464269       NaN  390000000    Asistida      NaN   Automática  2019   \n",
       "MCO485693920       NaN  250000000  Hidráulica      NaN   Automática  2016   \n",
       "MCO487847418        Sí  233000000    Asistida      4x4   Automática  2016   \n",
       "MCO482103954        Sí  660000000  Hidráulica      4x4   Automática  2018   \n",
       "MCO482030739       NaN  640000000    Asistida      4x4   Automática  2018   \n",
       "MCO486323614       NaN  370000000  Hidráulica      NaN   Automática  2018   \n",
       "MCO489324879        Sí  235000000    Asistida      4x4   Automática  2016   \n",
       "MCO482893625       NaN  325000000  Hidráulica      NaN   Automática  2014   \n",
       "MCO487791614        Sí  380000000  Hidráulica      NaN   Automática  2016   \n",
       "MCO490074758       NaN  295000000  Hidráulica      NaN   Automática  2018   \n",
       "MCO482424511        No  270000000    Asistida      4x4   Automática  2009   \n",
       "MCO482030619        No  750000000    Asistida      4x2   Automática  2010   \n",
       "MCO484548593       NaN  239000000    Asistida      NaN   Automática  2014   \n",
       "MCO479496878        Sí  379172000    Asistida      4x4   Automática  2017   \n",
       "MCO484871465        Sí  310000000  Hidráulica      4x4   Automática  2015   \n",
       "MCO491109462        No  255000000    Asistida      4x2   Automática  2014   \n",
       "MCO491684529        No  300000000  Hidráulica      4x2     Mecánica  2009   \n",
       "\n",
       "                            State  \\\n",
       "id                                  \n",
       "MCO482030566            Magdalena   \n",
       "MCO487849596             Casanare   \n",
       "MCO487147406          Bogotá D.c.   \n",
       "MCO486658262         Cundinamarca   \n",
       "MCO484464269          Bogotá D.c.   \n",
       "MCO485693920   Norte De Santander   \n",
       "MCO487847418          Bogotá D.c.   \n",
       "MCO482103954          Bogotá D.c.   \n",
       "MCO482030739          Bogotá D.c.   \n",
       "MCO486323614          Bogotá D.c.   \n",
       "MCO489324879      Valle Del Cauca   \n",
       "MCO482893625            Antioquia   \n",
       "MCO487791614          Bogotá D.c.   \n",
       "MCO490074758            Antioquia   \n",
       "MCO482424511          Bogotá D.c.   \n",
       "MCO482030619          Bogotá D.c.   \n",
       "MCO484548593            Santander   \n",
       "MCO479496878          Bogotá D.c.   \n",
       "MCO484871465          Bogotá D.c.   \n",
       "MCO491109462            Antioquia   \n",
       "MCO491684529          Bogotá D.c.   \n",
       "\n",
       "                                                           City  \n",
       "id                                                               \n",
       "MCO482030566  Santa Marta (distrito Turistico Cultural E His...  \n",
       "MCO487849596                                             Yopal   \n",
       "MCO487147406                                            Bogota   \n",
       "MCO486658262                                            Bogota   \n",
       "MCO484464269                                            Bogota   \n",
       "MCO485693920                                            Cúcuta   \n",
       "MCO487847418                                            Bogota   \n",
       "MCO482103954                                            Bogota   \n",
       "MCO482030739                                            Bogota   \n",
       "MCO486323614                                            Bogota   \n",
       "MCO489324879                                           Cartago   \n",
       "MCO482893625                                          Medellín   \n",
       "MCO487791614                                            Bogota   \n",
       "MCO490074758                                          Medellín   \n",
       "MCO482424511                                            Bogota   \n",
       "MCO482030619                                            Bogota   \n",
       "MCO484548593                                       Bucaramanga   \n",
       "MCO479496878                                            Bogota   \n",
       "MCO484871465                                           Usaquén   \n",
       "MCO491109462                                          Medellín   \n",
       "MCO491684529                                           Kennedy   "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['price'] > df['price'].quantile(0.99)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected all this cars are considered luxury cars, which makes sense why this cars are in this price range,\n",
    "Now the next question is, what happen with the other extreme, the incredibly cheap ones, lets see what they are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
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       "        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>brand</th>\n",
       "      <th>color</th>\n",
       "      <th>fuel</th>\n",
       "      <th>mileage</th>\n",
       "      <th>model</th>\n",
       "      <th>onlyOwner</th>\n",
       "      <th>price</th>\n",
       "      <th>steering</th>\n",
       "      <th>traction</th>\n",
       "      <th>transmission</th>\n",
       "      <th>year</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MCO482030786</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>0</td>\n",
       "      <td>Sahara</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2019</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487474462</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Plateado</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>66000</td>\n",
       "      <td>Spark GT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26900</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>2012</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482030578</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>0</td>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>2018</td>\n",
       "      <td>Magdalena</td>\n",
       "      <td>Santa Marta (distrito Turistico Cultural E His...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482030675</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Gris</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>0</td>\n",
       "      <td>Prado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO490476980</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Negro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>196630</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>No</td>\n",
       "      <td>23900</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2006</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Suba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO490071318</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>35800</td>\n",
       "      <td>Sail</td>\n",
       "      <td>Sí</td>\n",
       "      <td>24900</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>2014</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482030575</th>\n",
       "      <td>Lexus</td>\n",
       "      <td>Negro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>0</td>\n",
       "      <td>GX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Magdalena</td>\n",
       "      <td>Santa Marta (distrito Turistico Cultural E His...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487265016</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>80100</td>\n",
       "      <td>X-Trail</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40900</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2011</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO490092297</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Plateado</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>85400</td>\n",
       "      <td>X5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79900</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2009</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Suba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482030808</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>0</td>\n",
       "      <td>Tundra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482030576</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>0</td>\n",
       "      <td>Sahara</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Magdalena</td>\n",
       "      <td>Santa Marta (distrito Turistico Cultural E His...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO490223594</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>0</td>\n",
       "      <td>LC200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2019</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487673886</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>390560</td>\n",
       "      <td>Datsun</td>\n",
       "      <td>No</td>\n",
       "      <td>5500000</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>1980</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487147727</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Plateado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Land Cruiser</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>2018</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO487934678</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>40000</td>\n",
       "      <td>Escape</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64900</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2015</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Usaquén</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO482030565</th>\n",
       "      <td>Lexus</td>\n",
       "      <td>Negro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>LX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO485812437</th>\n",
       "      <td>Lexus</td>\n",
       "      <td>Plateado</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>0</td>\n",
       "      <td>LX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247000</td>\n",
       "      <td>Asistida</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2018</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO485764726</th>\n",
       "      <td>Skoda</td>\n",
       "      <td>Negro</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>81000</td>\n",
       "      <td>Fabia</td>\n",
       "      <td>No</td>\n",
       "      <td>20800</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>2011</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO484280849</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>52000</td>\n",
       "      <td>Fortuner</td>\n",
       "      <td>Sí</td>\n",
       "      <td>150000</td>\n",
       "      <td>Hidráulica</td>\n",
       "      <td>4x4</td>\n",
       "      <td>Automática</td>\n",
       "      <td>2017</td>\n",
       "      <td>Santander</td>\n",
       "      <td>Barrancabermeja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO491681199</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Blanco</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>214143</td>\n",
       "      <td>Festiva</td>\n",
       "      <td>No</td>\n",
       "      <td>5500000</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>1996</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Bogota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO491817832</th>\n",
       "      <td>Mazda</td>\n",
       "      <td>Rojo</td>\n",
       "      <td>Gasolina</td>\n",
       "      <td>235000</td>\n",
       "      <td>323</td>\n",
       "      <td>No</td>\n",
       "      <td>6000000</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>4x2</td>\n",
       "      <td>Mecánica</td>\n",
       "      <td>1988</td>\n",
       "      <td>Bogotá D.c.</td>\n",
       "      <td>Engativa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  brand     color      fuel  mileage         model onlyOwner  \\\n",
       "id                                                                             \n",
       "MCO482030786     Toyota    Blanco    Diesel        0        Sahara       NaN   \n",
       "MCO487474462  Chevrolet  Plateado  Gasolina    66000      Spark GT       NaN   \n",
       "MCO482030578     Toyota    Blanco    Diesel        0  Land Cruiser       NaN   \n",
       "MCO482030675     Toyota      Gris    Diesel        0         Prado       NaN   \n",
       "MCO490476980    Hyundai     Negro  Gasolina   196630        Tucson        No   \n",
       "MCO490071318  Chevrolet    Blanco  Gasolina    35800          Sail        Sí   \n",
       "MCO482030575      Lexus     Negro  Gasolina        0            GX       NaN   \n",
       "MCO487265016     Nissan    Blanco  Gasolina    80100       X-Trail       NaN   \n",
       "MCO490092297        BMW  Plateado  Gasolina    85400            X5       NaN   \n",
       "MCO482030808     Toyota    Blanco  Gasolina        0        Tundra       NaN   \n",
       "MCO482030576     Toyota    Blanco    Diesel        0        Sahara       NaN   \n",
       "MCO490223594     Toyota    Blanco    Diesel        0         LC200       NaN   \n",
       "MCO487673886     Nissan      Otro  Gasolina   390560        Datsun        No   \n",
       "MCO487147727     Toyota  Plateado       NaN        0  Land Cruiser       NaN   \n",
       "MCO487934678       Ford    Blanco  Gasolina    40000        Escape       NaN   \n",
       "MCO482030565      Lexus     Negro       NaN        0            LX       NaN   \n",
       "MCO485812437      Lexus  Plateado  Gasolina        0            LX       NaN   \n",
       "MCO485764726      Skoda     Negro  Gasolina    81000         Fabia        No   \n",
       "MCO484280849     Toyota    Blanco    Diesel    52000      Fortuner        Sí   \n",
       "MCO491681199       Ford    Blanco  Gasolina   214143       Festiva        No   \n",
       "MCO491817832      Mazda      Rojo  Gasolina   235000           323        No   \n",
       "\n",
       "                price    steering traction transmission  year         State  \\\n",
       "id                                                                            \n",
       "MCO482030786   154000  Hidráulica      4x4   Automática  2019   Bogotá D.c.   \n",
       "MCO487474462    26900    Asistida      NaN     Mecánica  2012   Bogotá D.c.   \n",
       "MCO482030578    60000  Hidráulica      4x4     Mecánica  2018     Magdalena   \n",
       "MCO482030675   107000    Asistida      4x4   Automática  2018   Bogotá D.c.   \n",
       "MCO490476980    23900  Hidráulica      4x4   Automática  2006   Bogotá D.c.   \n",
       "MCO490071318    24900  Hidráulica      NaN     Mecánica  2014   Bogotá D.c.   \n",
       "MCO482030575   130000  Hidráulica      4x4   Automática  2018     Magdalena   \n",
       "MCO487265016    40900    Asistida      4x4   Automática  2011   Bogotá D.c.   \n",
       "MCO490092297    79900  Hidráulica      NaN   Automática  2009   Bogotá D.c.   \n",
       "MCO482030808   100000  Hidráulica      4x4   Automática  2018   Bogotá D.c.   \n",
       "MCO482030576   144000  Hidráulica      4x4   Automática  2018     Magdalena   \n",
       "MCO490223594   154000    Asistida      4x4   Automática  2019   Bogotá D.c.   \n",
       "MCO487673886  5500000    Mecánica      4x2     Mecánica  1980   Bogotá D.c.   \n",
       "MCO487147727    60000  Hidráulica      NaN     Mecánica  2018   Bogotá D.c.   \n",
       "MCO487934678    64900  Hidráulica      4x4   Automática  2015   Bogotá D.c.   \n",
       "MCO482030565   247000    Asistida      NaN   Automática  2018   Bogotá D.c.   \n",
       "MCO485812437   247000    Asistida      4x4   Automática  2018   Bogotá D.c.   \n",
       "MCO485764726    20800  Hidráulica      4x2     Mecánica  2011   Bogotá D.c.   \n",
       "MCO484280849   150000  Hidráulica      4x4   Automática  2017     Santander   \n",
       "MCO491681199  5500000    Mecánica      4x2     Mecánica  1996   Bogotá D.c.   \n",
       "MCO491817832  6000000    Mecánica      4x2     Mecánica  1988   Bogotá D.c.   \n",
       "\n",
       "                                                           City  \n",
       "id                                                               \n",
       "MCO482030786                                            Bogota   \n",
       "MCO487474462                                            Bogota   \n",
       "MCO482030578  Santa Marta (distrito Turistico Cultural E His...  \n",
       "MCO482030675                                            Bogota   \n",
       "MCO490476980                                              Suba   \n",
       "MCO490071318                                            Bogota   \n",
       "MCO482030575  Santa Marta (distrito Turistico Cultural E His...  \n",
       "MCO487265016                                            Bogota   \n",
       "MCO490092297                                              Suba   \n",
       "MCO482030808                                            Bogota   \n",
       "MCO482030576  Santa Marta (distrito Turistico Cultural E His...  \n",
       "MCO490223594                                            Bogota   \n",
       "MCO487673886                                            Bogota   \n",
       "MCO487147727                                            Bogota   \n",
       "MCO487934678                                           Usaquén   \n",
       "MCO482030565                                            Bogota   \n",
       "MCO485812437                                            Bogota   \n",
       "MCO485764726                                            Bogota   \n",
       "MCO484280849                                   Barrancabermeja   \n",
       "MCO491681199                                            Bogota   \n",
       "MCO491817832                                          Engativa   "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['price'] < df['price'].quantile(0.01)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this situation we see something slightly different, we observe a list of cars, some of which are known to be economic,\n",
    "but there are also luxury cars that have been wrongly priced.\n",
    "\n",
    "Now lets look at the mileage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mileage (in Kms)\n",
      "Mean   66608.26041666667\n",
      "STD    58054.89558654298\n",
      "Median 58938.0\n",
      "IQR    63500.0\n",
      "Skewness 3.0063560629475568\n",
      "Kurtosis 24.900068176214113\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Mileage (in Kms)')\n",
    "print('Mean   {}'.format(df['mileage'].mean()))\n",
    "print('STD    {}'.format(df['mileage'].std()))\n",
    "print('Median {}'.format(df['mileage'].median()))\n",
    "print('IQR    {}'.format(scipy.stats.iqr(df['mileage'])))\n",
    "\n",
    "print('Skewness {}'.format(scipy.stats.skew(df['mileage'])))\n",
    "print('Kurtosis {}'.format(scipy.stats.kurtosis(df['mileage'])))\n",
    "sns.distplot(df['mileage']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year\n",
      "Mean   2012.3501984126983\n",
      "STD    5.581018874074131\n",
      "Median 2013.0\n",
      "IQR    5.0\n",
      "Skewness -2.2876420735622043\n",
      "Kurtosis 9.633553006260971\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Year')\n",
    "print('Mean   {}'.format(df['year'].mean()))\n",
    "print('STD    {}'.format(df['year'].std()))\n",
    "print('Median {}'.format(df['year'].median()))\n",
    "print('IQR    {}'.format(scipy.stats.iqr(df['year'])))\n",
    "\n",
    "print('Skewness {}'.format(scipy.stats.skew(df['year'])))\n",
    "print('Kurtosis {}'.format(scipy.stats.kurtosis(df['year'])))\n",
    "sns.distplot(df['year']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets see what are the most popular brands in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brand\n",
       "Chevrolet        264.0\n",
       "Ford             184.0\n",
       "Renault          175.0\n",
       "Toyota           166.0\n",
       "Kia              154.0\n",
       "Mazda            135.0\n",
       "Nissan           116.0\n",
       "BMW              101.0\n",
       "Volkswagen       100.0\n",
       "Hyundai           96.0\n",
       "Mercedes Benz     72.0\n",
       "Honda             54.0\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_brands_by_sample_size = df.groupby('brand').size().sort_values(ascending=False)\n",
    "most_popular_brands = sorted_brands_by_sample_size.where(sorted_brands_by_sample_size > 50).dropna()\n",
    "display(most_popular_brands)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets see the relation between **mileage**, **year** and **price** for the most popular brans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fd6e40f71d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 651.875x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reduce_df = df[df['brand'].isin(most_popular_brands.index)]\n",
    "sns.pairplot(data=reduce_df[['price', 'mileage', 'year', 'brand']], hue='brand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between price and mileage is -0.46982726351725984 and its p-value 1.4437257092972059e-89\n",
      "Correlation between price and year is 0.5190113004003902 and its p-value 3.283263018896982e-112\n",
      "Correlation between year and mileage is -0.8502771076223804 and its p-value 0.0\n"
     ]
    }
   ],
   "source": [
    "price_mileage_cor, price_mileage_pv = scipy.stats.spearmanr(reduce_df['price'], reduce_df['mileage'])\n",
    "price_year_cor, price_year_pv = scipy.stats.spearmanr(reduce_df['price'], reduce_df['year'])\n",
    "mileage_year_cor, mileage_year_pv = scipy.stats.spearmanr(reduce_df['mileage'], reduce_df['year'])\n",
    "print('Correlation between price and mileage is {} and its p-value {}'.format(price_mileage_cor, price_mileage_pv))\n",
    "print('Correlation between price and year is {} and its p-value {}'.format(price_year_cor, price_year_pv))\n",
    "print('Correlation between year and mileage is {} and its p-value {}'.format(mileage_year_cor, mileage_year_pv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# removing outliers\n",
    "over = reduce_df['price'].quantile(0.75) + 3*scipy.stats.iqr(reduce_df['price']) # removes values \n",
    "under = reduce_df['price'].quantile(0.01) \n",
    "reduce_df = reduce_df[(reduce_df['price'] > under) & (reduce_df['price'] < over)]\n",
    "ax = sns.boxplot(y='price', data=reduce_df);\n",
    "ax.yaxis.set_major_formatter(formatter);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict\n",
    "\n",
    "For the most popular brands of cars lets make a prediction of a price given the parameters that we have\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin, BaseEstimator\n",
    "\n",
    "class DataFrameImputer(TransformerMixin):\n",
    "\n",
    "    def __init__(self):\n",
    "        \"\"\"Impute missing values.\n",
    "\n",
    "        Columns of dtype object are imputed with the most frequent value \n",
    "        in column.\n",
    "\n",
    "        Columns of other types are imputed with mean of column.\n",
    "\n",
    "        \"\"\"\n",
    "    def fit(self, X, y=None):\n",
    "\n",
    "        self.fill = pd.Series([X[c].value_counts().index[0]\n",
    "            if X[c].dtype == np.dtype('O') else X[c].mean() for c in X],\n",
    "            index=X.columns)\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        return X.fillna(self.fill)\n",
    "    \n",
    "    def inverse_transform(self, X, y=None):\n",
    "        return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiLabelEncoder(TransformerMixin):\n",
    "    def __init__(self):\n",
    "        self.column_encoding = []\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        self.original_column_order = X.columns\n",
    "        return self\n",
    "        \n",
    "    def transform(self, X, y=None):\n",
    "        output = X.copy()\n",
    "        for column in output.columns:\n",
    "            if output[column].dtype.name == 'category':\n",
    "                # Applies dummie encoding\n",
    "                new_df = pd.get_dummies(output[column], prefix=column)\n",
    "                cols = new_df.columns\n",
    "                # Saves information for inverse transform\n",
    "                self.column_encoding.append({\n",
    "                    'column': column,\n",
    "                    'new_columns': cols,\n",
    "                });\n",
    "                # Add the new columns and delete the previus ones.\n",
    "                output = pd.concat([output, new_df], axis=1).drop(columns=[column])\n",
    "        return output\n",
    "    \n",
    "    def inverse_transform(self, X, y=None):\n",
    "        back = X.copy()\n",
    "        for encoding in self.column_encoding:\n",
    "            # Unifie the columns\n",
    "            new_out = back[encoding['new_columns']].idxmax(axis=1)\n",
    "            # Remove the prefix\n",
    "            new_out_clean = new_out.map(lambda x: x.replace(encoding['column']+'_', ''))\n",
    "            # Add the new columns and remove the previeus ones.\n",
    "            new_clean_df = pd.DataFrame(data=new_out_clean, columns=[encoding['column']])\n",
    "            back = pd.concat([new_clean_df, back], axis=1).drop(columns=encoding['new_columns'])\n",
    "        return back[self.original_column_order]\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataFrameCategorical(TransformerMixin):\n",
    "    \n",
    "    def __init__(self, categorical_columns):\n",
    "        super()\n",
    "        self.categorical_columns = categorical_columns\n",
    "        self.encoding = []\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        output = X.copy()\n",
    "        for column in self.categorical_columns:\n",
    "            original_dtype = output[column].dtype\n",
    "            self.encoding.append({\n",
    "                'original_dtype': original_dtype,\n",
    "                'column': column\n",
    "            });\n",
    "            output[column] = output[column].astype('category')\n",
    "        return output\n",
    "    \n",
    "    def inverse_transform(self, X):\n",
    "        output = X.copy()\n",
    "        print(self.encoding)\n",
    "        for enc in self.encoding:\n",
    "            output[enc['column']] = output[enc['column']].astype(enc['original_dtype'])\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "categorical_columns=['brand', 'color', 'fuel', 'onlyOwner', 'steering', 'traction', 'State', 'City', 'transmission', 'model']\n",
    "\n",
    "pipe = [\n",
    "    ('imputer', DataFrameImputer()), \n",
    "    ('Categorical', DataFrameCategorical(categorical_columns=categorical_columns)),\n",
    "    ('labelEncoder', MultiLabelEncoder())\n",
    "]\n",
    "data_pipe = Pipeline(pipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "df_data = data_pipe.fit_transform(reduce_df)\n",
    "train, test = train_test_split(df_data, test_size=0.2)\n",
    "X_train = train.drop(columns=['price'])\n",
    "Y_train = train['price']\n",
    "X_test = test.drop(columns=['price'])\n",
    "Y_test = test['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, presort='auto', random_state=None,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearReg = LinearRegression()\n",
    "linearReg.fit(X_train, Y_train)\n",
    "dummyReg = DummyRegressor()\n",
    "dummyReg.fit(X_train, Y_train)\n",
    "gbt = GradientBoostingRegressor()\n",
    "gbt.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7368972441021977e+26"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1102700021396245.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "198446613968551.38"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "display(mean_squared_error(Y_test, linearReg.predict(X_test)))\n",
    "display(mean_squared_error(Y_test, dummyReg.predict(X_test)))\n",
    "display(mean_squared_error(Y_test, gbt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boosted tree offers a superior estimator by a long shoot, so we are gona use random search to fine tune the parameters and see if we can improve it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "          estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, presort='auto', random_state=None,\n",
       "             subsample=1.0, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=200, n_jobs=-1,\n",
       "          param_distributions={'learning_rate': array([0.1  , 0.101, ..., 0.399, 0.4  ]), 'n_estimators': array([100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220,\n",
       "       230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350,\n",
       "       360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480,\n",
       "       490]), 'max_features': ['auto', 'sqrt', 'log2']},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_dist = {\"learning_rate\": np.arange(0.1, 0.4, 0.001),\n",
    "              \"n_estimators\": np.arange(100, 500, 10),\n",
    "              \"max_features\": [\"auto\", \"sqrt\", \"log2\"]\n",
    "             }\n",
    "\n",
    "# run randomized search\n",
    "n_iter_search = 200\n",
    "clf = GradientBoostingRegressor()\n",
    "random_search = RandomizedSearchCV(clf, param_distributions=param_dist,\n",
    "                                   n_iter=n_iter_search, cv=5, n_jobs=-1)\n",
    "random_search.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120461527361944.38"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(Y_test, random_search.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.17600000000000007,\n",
       " 'max_features': 'auto',\n",
       " 'n_estimators': 480}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['random_search.joblib']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(random_search, 'random_search.joblib') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = joblib.load('random_search.joblib')"
   ]
  }
 ],
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