12_price_car_sale_Rusty_Bargain_ok.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Venta de autos usados Rusty Bargain\n",
    "\n",
    "# Descripción\n",
    "El servicio de venta de autos usados *Rusty Bargain* está desarrollando una aplicación para atraer nuevos clientes. Gracias a esa app, puedes averiguar rápidamente el valor de mercado de tu coche. \n",
    "\n",
    "Tenemos acceso al historial: especificaciones técnicas, versiones de equipamiento y precios. \n",
    "\n",
    "\n",
    "## Objetivo\n",
    "\n",
    "- Crear un modelo de machine learning que determine el valor de mercado. \n",
    "- Evaluar nuestro modelo en base a:\n",
    "    - la **calidad** de la predicción\n",
    "    - la **velocidad** de la predicción\n",
    "    - el **tiempo** requerido para el entrenamiento"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparación de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2023-05-09T21:29:10.844610-05:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.9.15\n",
      "IPython version      : 8.12.0\n",
      "\n",
      "Compiler    : MSC v.1929 64 bit (AMD64)\n",
      "OS          : Windows\n",
      "Release     : 10\n",
      "Machine     : AMD64\n",
      "Processor   : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel\n",
      "CPU cores   : 12\n",
      "Architecture: 64bit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model    import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics         import mean_squared_error, make_scorer\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos y visualizamos los datos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carga de datos\n",
    "try:\n",
    "    df = pd.read_csv('car_data.csv')\n",
    "except:\n",
    "    df = pd.read_csv('/datasets/car_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>DateCrawled</th>\n",
       "      <th>Price</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Gearbox</th>\n",
       "      <th>Power</th>\n",
       "      <th>Model</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Brand</th>\n",
       "      <th>NotRepaired</th>\n",
       "      <th>DateCreated</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>LastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/03/2016 11:52</td>\n",
       "      <td>480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>07/04/2016 03:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24/03/2016 10:58</td>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</td>\n",
       "      <td>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>07/04/2016 01:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14/03/2016 12:52</td>\n",
       "      <td>9800</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>auto</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>05/04/2016 12:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17/03/2016 16:54</td>\n",
       "      <td>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>17/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>17/03/2016 17:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31/03/2016 17:25</td>\n",
       "      <td>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>no</td>\n",
       "      <td>31/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>06/04/2016 10:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DateCrawled  Price VehicleType  RegistrationYear Gearbox  Power   \n",
       "0  24/03/2016 11:52    480         NaN              1993  manual      0  \\\n",
       "1  24/03/2016 10:58  18300       coupe              2011  manual    190   \n",
       "2  14/03/2016 12:52   9800         suv              2004    auto    163   \n",
       "3  17/03/2016 16:54   1500       small              2001  manual     75   \n",
       "4  31/03/2016 17:25   3600       small              2008  manual     69   \n",
       "\n",
       "   Model  Mileage  RegistrationMonth  FuelType       Brand NotRepaired   \n",
       "0   golf   150000                  0    petrol  volkswagen         NaN  \\\n",
       "1    NaN   125000                  5  gasoline        audi         yes   \n",
       "2  grand   125000                  8  gasoline        jeep         NaN   \n",
       "3   golf   150000                  6    petrol  volkswagen          no   \n",
       "4  fabia    90000                  7  gasoline       skoda          no   \n",
       "\n",
       "        DateCreated  NumberOfPictures  PostalCode          LastSeen  \n",
       "0  24/03/2016 00:00                 0       70435  07/04/2016 03:16  \n",
       "1  24/03/2016 00:00                 0       66954  07/04/2016 01:46  \n",
       "2  14/03/2016 00:00                 0       90480  05/04/2016 12:47  \n",
       "3  17/03/2016 00:00                 0       91074  17/03/2016 17:40  \n",
       "4  31/03/2016 00:00                 0       60437  06/04/2016 10:17  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El dataset contiene la siguiente información:\n",
    "\n",
    "**Características**\n",
    "- `DateCrawled` — fecha en la que se descargó el perfil de la base de datos\n",
    "- `VehicleType` — tipo de carrocería del vehículo\n",
    "- `RegistrationYear` — año de matriculación del vehículo\n",
    "- `Gearbox` — tipo de caja de cambios\n",
    "- `Power` — potencia (CV)\n",
    "- `Model` — modelo del vehículo\n",
    "- `Mileage` — kilometraje (medido en km de acuerdo con las especificidades regionales del conjunto de datos)\n",
    "- `RegistrationMonth` — mes de matriculación del vehículo\n",
    "- `FuelType` — tipo de combustible\n",
    "- `Brand` — marca del vehículo\n",
    "- `NotRepaired` — vehículo con o sin reparación\n",
    "- `DateCreated` — fecha de creación del perfil\n",
    "- `NumberOfPictures` — número de fotos del vehículo\n",
    "- `PostalCode` — código postal del propietario del perfil (usuario)\n",
    "- `LastSeen` — fecha de la última vez que el usuario estuvo activo\n",
    "\n",
    "**Objetivo**\n",
    "- `Price` — precio (en euros)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revisemos de nuestros datos el tamaño del dataset, tipos de datos y conteo de registros por columna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 354369 entries, 0 to 354368\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count   Dtype \n",
      "---  ------             --------------   ----- \n",
      " 0   DateCrawled        354369 non-null  object\n",
      " 1   Price              354369 non-null  int64 \n",
      " 2   VehicleType        316879 non-null  object\n",
      " 3   RegistrationYear   354369 non-null  int64 \n",
      " 4   Gearbox            334536 non-null  object\n",
      " 5   Power              354369 non-null  int64 \n",
      " 6   Model              334664 non-null  object\n",
      " 7   Mileage            354369 non-null  int64 \n",
      " 8   RegistrationMonth  354369 non-null  int64 \n",
      " 9   FuelType           321474 non-null  object\n",
      " 10  Brand              354369 non-null  object\n",
      " 11  NotRepaired        283215 non-null  object\n",
      " 12  DateCreated        354369 non-null  object\n",
      " 13  NumberOfPictures   354369 non-null  int64 \n",
      " 14  PostalCode         354369 non-null  int64 \n",
      " 15  LastSeen           354369 non-null  object\n",
      "dtypes: int64(7), object(9)\n",
      "memory usage: 43.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tenemos un dataset con un tamaño inicial de 354.369 registros y 16 columnas. Observamos en el conteo de registros por columnas que tenemos ausencia de datos en 5 columnas que se deberán abordar más adelante. También tipos de datos que debemos cambiar."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renombramos las columnas de acuerdo a la convención *snake_case*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'DateCrawled':'date_crawled', \n",
    "                        'Price':'price', \n",
    "                        'VehicleType':'vehicle_type', \n",
    "                        'RegistrationYear':'registration_year', \n",
    "                        'Gearbox':'gearbox',\n",
    "                        'Power':'power', \n",
    "                        'Model':'model', \n",
    "                        'Mileage':'mileage', \n",
    "                        'RegistrationMonth':'registration_month', \n",
    "                        'FuelType': 'fuel_type', \n",
    "                        'Brand':'brand',\n",
    "                        'NotRepaired':'not_repaired', \n",
    "                        'DateCreated':'date_created', \n",
    "                        'NumberOfPictures':'number_of_pictures', \n",
    "                        'PostalCode':'postal_code',\n",
    "                        'LastSeen':'last_seen'\n",
    "                        }\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>date_crawled</th>\n",
       "      <th>price</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>not_repaired</th>\n",
       "      <th>date_created</th>\n",
       "      <th>number_of_pictures</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>last_seen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/03/2016 11:52</td>\n",
       "      <td>480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>07/04/2016 03:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24/03/2016 10:58</td>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</td>\n",
       "      <td>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>07/04/2016 01:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14/03/2016 12:52</td>\n",
       "      <td>9800</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>auto</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>05/04/2016 12:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17/03/2016 16:54</td>\n",
       "      <td>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>17/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>17/03/2016 17:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31/03/2016 17:25</td>\n",
       "      <td>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>no</td>\n",
       "      <td>31/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>06/04/2016 10:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date_crawled  price vehicle_type  registration_year gearbox  power   \n",
       "0  24/03/2016 11:52    480          NaN               1993  manual      0  \\\n",
       "1  24/03/2016 10:58  18300        coupe               2011  manual    190   \n",
       "2  14/03/2016 12:52   9800          suv               2004    auto    163   \n",
       "3  17/03/2016 16:54   1500        small               2001  manual     75   \n",
       "4  31/03/2016 17:25   3600        small               2008  manual     69   \n",
       "\n",
       "   model  mileage  registration_month fuel_type       brand not_repaired   \n",
       "0   golf   150000                   0    petrol  volkswagen          NaN  \\\n",
       "1    NaN   125000                   5  gasoline        audi          yes   \n",
       "2  grand   125000                   8  gasoline        jeep          NaN   \n",
       "3   golf   150000                   6    petrol  volkswagen           no   \n",
       "4  fabia    90000                   7  gasoline       skoda           no   \n",
       "\n",
       "       date_created  number_of_pictures  postal_code         last_seen  \n",
       "0  24/03/2016 00:00                   0        70435  07/04/2016 03:16  \n",
       "1  24/03/2016 00:00                   0        66954  07/04/2016 01:46  \n",
       "2  14/03/2016 00:00                   0        90480  05/04/2016 12:47  \n",
       "3  17/03/2016 00:00                   0        91074  17/03/2016 17:40  \n",
       "4  31/03/2016 00:00                   0        60437  06/04/2016 10:17  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminamos registros duplicados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007393423239617461"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum() / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(354107, 16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cambiamos tipos de datos de columnas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date_crawled'] = pd.to_datetime(df['date_crawled'], format='%d/%m/%Y %H:%M')\n",
    "df['date_created'] = pd.to_datetime(df['date_created'], format='%d/%m/%Y %H:%M')\n",
    "df['last_seen'] = pd.to_datetime(df['last_seen'], format='%d/%m/%Y %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 354107 entries, 0 to 354106\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   date_crawled        354107 non-null  datetime64[ns]\n",
      " 1   price               354107 non-null  int64         \n",
      " 2   vehicle_type        316623 non-null  object        \n",
      " 3   registration_year   354107 non-null  int64         \n",
      " 4   gearbox             334277 non-null  object        \n",
      " 5   power               354107 non-null  int64         \n",
      " 6   model               334406 non-null  object        \n",
      " 7   mileage             354107 non-null  int64         \n",
      " 8   registration_month  354107 non-null  int64         \n",
      " 9   fuel_type           321218 non-null  object        \n",
      " 10  brand               354107 non-null  object        \n",
      " 11  not_repaired        282962 non-null  object        \n",
      " 12  date_created        354107 non-null  datetime64[ns]\n",
      " 13  number_of_pictures  354107 non-null  int64         \n",
      " 14  postal_code         354107 non-null  int64         \n",
      " 15  last_seen           354107 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](3), int64(7), object(6)\n",
      "memory usage: 43.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revisemos estadísticas descriptivas por columna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>date_crawled</th>\n",
       "      <th>price</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>power</th>\n",
       "      <th>mileage</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>date_created</th>\n",
       "      <th>number_of_pictures</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>last_seen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>354107</td>\n",
       "      <td>354107.000000</td>\n",
       "      <td>354107.000000</td>\n",
       "      <td>354107.000000</td>\n",
       "      <td>354107.000000</td>\n",
       "      <td>354107.000000</td>\n",
       "      <td>354107</td>\n",
       "      <td>354107.0</td>\n",
       "      <td>354107.000000</td>\n",
       "      <td>354107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016-03-21 12:56:48.735947008</td>\n",
       "      <td>4416.433287</td>\n",
       "      <td>2004.235355</td>\n",
       "      <td>110.089651</td>\n",
       "      <td>128211.811684</td>\n",
       "      <td>5.714182</td>\n",
       "      <td>2016-03-20 19:11:13.738728960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50507.145030</td>\n",
       "      <td>2016-03-29 23:51:12.374903808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016-03-05 14:06:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2014-03-10 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067.000000</td>\n",
       "      <td>2016-03-05 14:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016-03-13 11:52:00</td>\n",
       "      <td>1050.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2016-03-13 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30165.000000</td>\n",
       "      <td>2016-03-23 02:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016-03-21 17:50:00</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2016-03-21 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49406.000000</td>\n",
       "      <td>2016-04-03 15:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016-03-29 14:36:00</td>\n",
       "      <td>6400.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2016-03-29 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71083.000000</td>\n",
       "      <td>2016-04-06 10:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016-04-07 14:36:00</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2016-04-07 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99998.000000</td>\n",
       "      <td>2016-04-07 14:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4514.338584</td>\n",
       "      <td>90.261168</td>\n",
       "      <td>189.914972</td>\n",
       "      <td>37906.590101</td>\n",
       "      <td>3.726682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25784.212094</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        date_crawled          price  registration_year   \n",
       "count                         354107  354107.000000      354107.000000  \\\n",
       "mean   2016-03-21 12:56:48.735947008    4416.433287        2004.235355   \n",
       "min              2016-03-05 14:06:00       0.000000        1000.000000   \n",
       "25%              2016-03-13 11:52:00    1050.000000        1999.000000   \n",
       "50%              2016-03-21 17:50:00    2700.000000        2003.000000   \n",
       "75%              2016-03-29 14:36:00    6400.000000        2008.000000   \n",
       "max              2016-04-07 14:36:00   20000.000000        9999.000000   \n",
       "std                              NaN    4514.338584          90.261168   \n",
       "\n",
       "               power        mileage  registration_month   \n",
       "count  354107.000000  354107.000000       354107.000000  \\\n",
       "mean      110.089651  128211.811684            5.714182   \n",
       "min         0.000000    5000.000000            0.000000   \n",
       "25%        69.000000  125000.000000            3.000000   \n",
       "50%       105.000000  150000.000000            6.000000   \n",
       "75%       143.000000  150000.000000            9.000000   \n",
       "max     20000.000000  150000.000000           12.000000   \n",
       "std       189.914972   37906.590101            3.726682   \n",
       "\n",
       "                        date_created  number_of_pictures    postal_code   \n",
       "count                         354107            354107.0  354107.000000  \\\n",
       "mean   2016-03-20 19:11:13.738728960                 0.0   50507.145030   \n",
       "min              2014-03-10 00:00:00                 0.0    1067.000000   \n",
       "25%              2016-03-13 00:00:00                 0.0   30165.000000   \n",
       "50%              2016-03-21 00:00:00                 0.0   49406.000000   \n",
       "75%              2016-03-29 00:00:00                 0.0   71083.000000   \n",
       "max              2016-04-07 00:00:00                 0.0   99998.000000   \n",
       "std                              NaN                 0.0   25784.212094   \n",
       "\n",
       "                           last_seen  \n",
       "count                         354107  \n",
       "mean   2016-03-29 23:51:12.374903808  \n",
       "min              2016-03-05 14:15:00  \n",
       "25%              2016-03-23 02:50:00  \n",
       "50%              2016-04-03 15:15:00  \n",
       "75%              2016-04-06 10:06:00  \n",
       "max              2016-04-07 14:58:00  \n",
       "std                              NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>vehicle_type</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>model</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>not_repaired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>316623</td>\n",
       "      <td>334277</td>\n",
       "      <td>334406</td>\n",
       "      <td>321218</td>\n",
       "      <td>354107</td>\n",
       "      <td>282962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>250</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>sedan</td>\n",
       "      <td>manual</td>\n",
       "      <td>golf</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>91399</td>\n",
       "      <td>268034</td>\n",
       "      <td>29215</td>\n",
       "      <td>216161</td>\n",
       "      <td>76960</td>\n",
       "      <td>246927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       vehicle_type gearbox   model fuel_type       brand not_repaired\n",
       "count        316623  334277  334406    321218      354107       282962\n",
       "unique            8       2     250         7          40            2\n",
       "top           sedan  manual    golf    petrol  volkswagen           no\n",
       "freq          91399  268034   29215    216161       76960       246927"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['vehicle_type', 'gearbox','model', 'fuel_type', 'brand','not_repaired']].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con las estadísticas por columnas podemos revisar rápidamente como están nuestros datos. \n",
    "- `number_of_pictures` contiene solo valores cero, esta información no aporta nada, por lo que es una columna que podemos eliminar.\n",
    "- `price` medio de los vehículos es de 4.416 euros. Desde valores en 0 hasta 20.0000.\n",
    "- `mileage` medio de los vehículos es de 128.211 Km.\n",
    "- `vehicle_type` 8 categorías, el más popular el sedan.\n",
    "- `gearbox` 2 categorias, el más popular el manual.\n",
    "- `fueltype` 7 categorias, el más popular el petrol.\n",
    "- `brand` 40 categorías, la más popular volkswagen.\n",
    "- `notrepaired` el mas popular no.\n",
    "- revisaremos las columnas de fechas dejando lo necesario."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminamos columnas innecesarias:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revisaremos primero cuales son las columnas a eliminar, realizaremos lo siguiente:\n",
    "-  Comprobaremos si las columnas `date_crawled` y `date_created` tienen las mismas fechas, de ser cierto, eliminaremos `date_crawled` y nos quedaremos con `date_created`.\n",
    "-  La columna `registration_month` nos da el mes de matriculación, información que consideramos irrelevante para este trabajo, por lo que se eliminará.\n",
    "-  La columna `number_of_pictures` vimos que teniamos solo ceros, por lo que se eliminará también.\n",
    "-  La columna `last_seen` tiene información que consideramos irrelevante, por lo que la eliminaremos.\n",
    "-  La columna `postal_code` también la eliminaremos, ya que sin tener información sobre el pais de donde son los vehículos, no servirá de mucho."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(354107, 354107)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.date_crawled.dt.date == df.date_created), len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number_of_pictures\n",
       "0    354107\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.number_of_pictures.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['date_crawled', 'registration_month', 'number_of_pictures', 'last_seen', 'postal_code'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(354107, 11)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>not_repaired</th>\n",
       "      <th>date_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</td>\n",
       "      <td>2016-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9800</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>auto</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price vehicle_type  registration_year gearbox  power  model  mileage   \n",
       "0    480          NaN               1993  manual      0   golf   150000  \\\n",
       "1  18300        coupe               2011  manual    190    NaN   125000   \n",
       "2   9800          suv               2004    auto    163  grand   125000   \n",
       "3   1500        small               2001  manual     75   golf   150000   \n",
       "4   3600        small               2008  manual     69  fabia    90000   \n",
       "\n",
       "  fuel_type       brand not_repaired date_created  \n",
       "0    petrol  volkswagen          NaN   2016-03-24  \n",
       "1  gasoline        audi          yes   2016-03-24  \n",
       "2  gasoline        jeep          NaN   2016-03-14  \n",
       "3    petrol  volkswagen           no   2016-03-17  \n",
       "4  gasoline       skoda           no   2016-03-31  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamiento de valores ausentes\n",
    "\n",
    "Revisemos valores ausentes por columna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaN per columns\n",
      "price                0.00\n",
      "vehicle_type         0.11\n",
      "registration_year    0.00\n",
      "gearbox              0.06\n",
      "power                0.00\n",
      "model                0.06\n",
      "mileage              0.00\n",
      "fuel_type            0.09\n",
      "brand                0.00\n",
      "not_repaired         0.20\n",
      "date_created         0.00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('NaN per columns')\n",
    "print(round(df.isna().sum() / len(df),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vehicle_type\n",
       "sedan          0.26\n",
       "small          0.23\n",
       "wagon          0.18\n",
       "NaN            0.11\n",
       "bus            0.08\n",
       "convertible    0.06\n",
       "coupe          0.05\n",
       "suv            0.03\n",
       "other          0.01\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- - - - - - - - - - - - - - \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "gearbox\n",
       "manual    0.76\n",
       "auto      0.19\n",
       "NaN       0.06\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- - - - - - - - - - - - - - \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "model\n",
       "golf                  0.08\n",
       "other                 0.07\n",
       "3er                   0.06\n",
       "NaN                   0.06\n",
       "polo                  0.04\n",
       "                      ... \n",
       "i3                    0.00\n",
       "serie_3               0.00\n",
       "rangerover            0.00\n",
       "range_rover_evoque    0.00\n",
       "serie_1               0.00\n",
       "Name: proportion, Length: 251, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- - - - - - - - - - - - - - \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "fuel_type\n",
       "petrol      0.61\n",
       "gasoline    0.28\n",
       "NaN         0.09\n",
       "lpg         0.01\n",
       "cng         0.00\n",
       "hybrid      0.00\n",
       "other       0.00\n",
       "electric    0.00\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- - - - - - - - - - - - - - \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "not_repaired\n",
       "no     0.7\n",
       "NaN    0.2\n",
       "yes    0.1\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- - - - - - - - - - - - - - \n"
     ]
    }
   ],
   "source": [
    "# Review categorical columns including NaN\n",
    "\n",
    "column_with_nan = ['vehicle_type', 'gearbox', 'model', 'fuel_type', 'not_repaired']\n",
    "\n",
    "for column in column_with_nan:\n",
    "    display(round(df[column].value_counts(dropna=False, normalize=True),2))\n",
    "    print('- - - - - - - - - - - - - - ')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver el detalle como están distribuidos los datos por categoría, incluyendo los NaN. \n",
    "\n",
    "En las 5 columnas categóricas tenemos:\n",
    "- `vehicle_type` 10% de NaN.\n",
    "- `gearbox` 5% de NaN.\n",
    "- `model` 5% de NaN.\n",
    "- `fuel_type` 9% de NaN.\n",
    "- `not_repaired` 20% de NaN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a) Vamos a rellenar con una nueva categoría llamada \"*unspecified*\" en 4 columnas `vehicle_type`, `gearbox`, `model` y `fuel_type`, puesto que estas columnas son categóricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill NaN per columns with NaN\n",
    "for column in ['vehicle_type', 'gearbox', 'model', 'fuel_type']:\n",
    "    df[column].fillna('unspecified', inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b)  En la columna `not_repaired` consideramos que debe ser clara la respuesta, reparado o no reparado, por nos eliminaremos los NaN. Los NaN en esta columna representan el 20% de los registros del dataset. Antes de eliminar, revisaremos las distribuciones de los datos con y sin NaN; en el caso que no hayan cambios en la distribución procederemos a eliminar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(282962, 11)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data without NaN\n",
    "df_no_nan = df.query('not not_repaired.isna()')\n",
    "df_no_nan.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veremos gráficamente las distribuciones de los datos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots to review the distributions on complete data with NaN and data without NaN.\n",
    "\n",
    "fig, ((a,b,c),(d,e,f),(g,h,i))= plt.subplots(figsize=(16,15), nrows=3, ncols=3)\n",
    "\n",
    "# Gráfico 1\n",
    "df['price'].hist(       ax=a, edgecolor='black', color='green', alpha=0.5, density=True, label='with NaN', bins=100)\n",
    "df_no_nan['price'].hist(ax=a, edgecolor='black', color='orange', alpha=0.5, density=True, label='No NaN', bins=100)\n",
    "a.legend()\n",
    "a.set_xlabel('price')\n",
    "a.set_ylabel('count')\n",
    "a.set_title('\"price\" with and without NaN')\n",
    "\n",
    "# Gráfico 2\n",
    "df['vehicle_type'].value_counts().plot(       ax=b, kind='bar', edgecolor='black', color='green', alpha=0.5, label='with NaN')\n",
    "df_no_nan['vehicle_type'].value_counts().plot(ax=b, kind='bar', edgecolor='black', color='orange', alpha=0.5, label='No NaN')\n",
    "b.legend()\n",
    "b.set_xlabel('vehicle_type')\n",
    "b.set_ylabel('count')\n",
    "b.set_title('\"vehicle type\" with and without NaN')\n",
    "\n",
    "# Gráfico 3\n",
    "condition_1 = 'registration_year > 2010 and registration_year <= 20016'\n",
    "df.query(condition_1)['registration_year'].value_counts().plot(       ax=c, kind='bar', edgecolor='black', color='green', alpha=0.5, label='with NaN')\n",
    "df_no_nan.query(condition_1)['registration_year'].value_counts().plot(ax=c, kind='bar', edgecolor='black', color='orange', alpha=0.5, label='No NaN')\n",
    "c.legend()\n",
    "c.set_xlabel('year')\n",
    "c.set_ylabel('count')\n",
    "c.set_title('\"registration year\" with and without NaN')\n",
    "\n",
    "# Gráfico 4\n",
    "df['gearbox'].value_counts().plot(       ax=d, kind='bar', edgecolor='black', color='green', alpha=0.5, label='with NaN')\n",
    "df_no_nan['gearbox'].value_counts().plot(ax=d, kind='bar', edgecolor='black', color='orange', alpha=0.5, label='No NaN')\n",
    "d.legend()\n",
    "d.set_xlabel('gearbox')\n",
    "d.set_ylabel('count')\n",
    "d.set_title('\"gearbox\" with and without NaN')\n",
    "\n",
    "# Gráfico 5\n",
    "df['power'].hist(       ax=e, bins=100, edgecolor='black', color='green', alpha=0.5, density=True, label='with NaN', range=(0,500))\n",
    "df_no_nan['power'].hist(ax=e, bins=100, edgecolor='black', color='orange', alpha=0.5, density=True, label='No NaN', range=(0,500) )\n",
    "e.legend()\n",
    "e.set_xlabel('power')\n",
    "e.set_ylabel('count')\n",
    "e.set_title('\"power\" with and without NaN')\n",
    "\n",
    "# Gráfico 6\n",
    "df['model'].value_counts().sort_values(ascending=False).head(20).plot(       ax=f, edgecolor='black', kind='bar', color='green', alpha=0.5, label='with NaN')\n",
    "df_no_nan['model'].value_counts().sort_values(ascending=False).head(20).plot(ax=f, edgecolor='black', kind='bar', color='orange', alpha=0.5, label='No NaN')\n",
    "f.legend()\n",
    "f.set_xlabel('model')\n",
    "f.set_ylabel('count')\n",
    "f.set_title('\"model\" with and without NaN')\n",
    "\n",
    "# Gráfico 7\n",
    "df['mileage'].hist(       ax=g, edgecolor='black', color='green', alpha=0.5, density=True, label='with NaN', bins=10)\n",
    "df_no_nan['mileage'].hist(ax=g, edgecolor='black', color='orange', alpha=0.5, density=True, label='No NaN', bins=11)\n",
    "g.legend()\n",
    "g.set_xlabel('mileage (Km)')\n",
    "g.set_ylabel('count')\n",
    "g.set_title('\"mileage\" with and without NaN')\n",
    "\n",
    "# Gráfico 8\n",
    "df['fuel_type'].value_counts().plot(       ax=h, kind='bar', edgecolor='black', color='green', alpha=0.5, label='with NaN')\n",
    "df_no_nan['fuel_type'].value_counts().plot(ax=h, kind='bar', edgecolor='black', color='orange', alpha=0.5, label='No NaN')\n",
    "h.legend()\n",
    "h.set_xlabel('fuel_type')\n",
    "h.set_ylabel('count')\n",
    "h.set_title('\"fuel_type\" with and without NaN')\n",
    "\n",
    "# Gráfico 9\n",
    "df['brand'].value_counts().sort_values(ascending=False).head(20).plot(       ax=i, kind='bar', edgecolor='black', color='green', alpha=0.5, label='with NaN')\n",
    "df_no_nan['brand'].value_counts().sort_values(ascending=False).head(20).plot(ax=i, kind='bar', edgecolor='black', color='orange', alpha=0.5, label='No NaN')\n",
    "i.legend()\n",
    "i.set_xlabel('brand')\n",
    "i.set_ylabel('count')\n",
    "i.set_title('\"brand\" with and without NaN')\n",
    "\n",
    "# Ajustes\n",
    "plt.subplots_adjust(left=0.125,\n",
    "                    bottom=0.2, \n",
    "                    right=1, \n",
    "                    top=1, \n",
    "                    wspace=0.2, \n",
    "                    hspace=0.45)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos podido revisar como se encuentran distribuidos los datos en cada característica desde el dataset total y el dataset sin registros NaN en la columna 'no_repaired', demostrando que los datos no cambian su distribución, por lo que procedemos a remover los registros que contienen NaN en `not_repaired`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove NaN in column not_repaired\n",
    "df = df.dropna(subset=['not_repaired'])\n",
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 282962 entries, 0 to 282961\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   price              282962 non-null  int64         \n",
      " 1   vehicle_type       282962 non-null  object        \n",
      " 2   registration_year  282962 non-null  int64         \n",
      " 3   gearbox            282962 non-null  object        \n",
      " 4   power              282962 non-null  int64         \n",
      " 5   model              282962 non-null  object        \n",
      " 6   mileage            282962 non-null  int64         \n",
      " 7   fuel_type          282962 non-null  object        \n",
      " 8   brand              282962 non-null  object        \n",
      " 9   not_repaired       282962 non-null  bool          \n",
      " 10  date_created       282962 non-null  datetime64[ns]\n",
      "dtypes: bool(1), datetime64[ns](1), int64(4), object(5)\n",
      "memory usage: 21.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# Change data type bool, yes = True, no = False\n",
    "df['not_repaired'] = df['not_repaired'] == 'yes'\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(282962, 11)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price                0\n",
       "vehicle_type         0\n",
       "registration_year    0\n",
       "gearbox              0\n",
       "power                0\n",
       "model                0\n",
       "mileage              0\n",
       "fuel_type            0\n",
       "brand                0\n",
       "not_repaired         0\n",
       "date_created         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check NaN\n",
    "df.isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro dataset está limpio de valores ausentes."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis exploratorio de datos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a explorar nuestros datos:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tipo de vehículo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.vehicle_type.value_counts().plot(kind='pie')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predominan 3 tipos de carros, sedan, wagon y small."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Año de registro\n",
    "\n",
    "Describe el año de matricula del vehículo. Con esta información podremos determinar un periodo válido de los años de matriculación de vehículo, por ejemplo un periodo de 20 años."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000, 1234, 1500, 1600, 1602, 1800, 1910, 1919, 1923, 1925, 1929,\n",
       "       1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1940, 1941,\n",
       "       1942, 1943, 1944, 1945, 1947, 1948, 1950, 1951, 1952, 1953, 1954,\n",
       "       1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,\n",
       "       1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,\n",
       "       1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
       "       1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
       "       1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 3000,\n",
       "       4000, 4500, 5000, 6000, 6500, 7500, 9000, 9999], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.registration_year.sort_values().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "registration_year\n",
       "1999    17854\n",
       "2005    17642\n",
       "2006    17597\n",
       "2000    17191\n",
       "2003    16665\n",
       "2004    16605\n",
       "2001    16491\n",
       "2002    15917\n",
       "2007    15349\n",
       "2008    13933\n",
       "1998    13815\n",
       "2009    13260\n",
       "1997    11087\n",
       "2010     9871\n",
       "2011     9213\n",
       "1996     8077\n",
       "2012     6716\n",
       "1995     6275\n",
       "2017     5847\n",
       "2016     5172\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.registration_year.value_counts().head(20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encontramos vehículos con registros desde el año 1000 hasta el 9999, no tiene sentido tener registros debajo de los años 90 y por encima del 2016, siendo el 2016 el año máximo, puesto que esta base de datos fue extraida en el año 2016. Dicho esto es muy prudente considerar nuestro dataset en un periodo de 20 años, desde 1996 hasta el 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>not_repaired</th>\n",
       "      <th>date_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>650</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1995</td>\n",
       "      <td>manual</td>\n",
       "      <td>102</td>\n",
       "      <td>3er</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>bmw</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1980</td>\n",
       "      <td>manual</td>\n",
       "      <td>50</td>\n",
       "      <td>other</td>\n",
       "      <td>40000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2900</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>2018</td>\n",
       "      <td>manual</td>\n",
       "      <td>90</td>\n",
       "      <td>meriva</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>opel</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>500</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1990</td>\n",
       "      <td>manual</td>\n",
       "      <td>118</td>\n",
       "      <td>other</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5555</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>2017</td>\n",
       "      <td>manual</td>\n",
       "      <td>125</td>\n",
       "      <td>c4</td>\n",
       "      <td>125000</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>citroen</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282905</th>\n",
       "      <td>5000</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>2017</td>\n",
       "      <td>manual</td>\n",
       "      <td>120</td>\n",
       "      <td>other</td>\n",
       "      <td>150000</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>citroen</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-03-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282914</th>\n",
       "      <td>850</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1989</td>\n",
       "      <td>manual</td>\n",
       "      <td>113</td>\n",
       "      <td>80</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>audi</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-03-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282922</th>\n",
       "      <td>1550</td>\n",
       "      <td>small</td>\n",
       "      <td>1990</td>\n",
       "      <td>manual</td>\n",
       "      <td>54</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282925</th>\n",
       "      <td>3300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>1957</td>\n",
       "      <td>manual</td>\n",
       "      <td>40</td>\n",
       "      <td>other</td>\n",
       "      <td>100000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>trabant</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282932</th>\n",
       "      <td>12800</td>\n",
       "      <td>convertible</td>\n",
       "      <td>1991</td>\n",
       "      <td>manual</td>\n",
       "      <td>211</td>\n",
       "      <td>other</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>porsche</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33243 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price vehicle_type  registration_year gearbox  power   model  mileage   \n",
       "3         650        sedan               1995  manual    102     3er   150000  \\\n",
       "5           0        sedan               1980  manual     50   other    40000   \n",
       "15       2900  unspecified               2018  manual     90  meriva   150000   \n",
       "17        500        sedan               1990  manual    118   other   150000   \n",
       "19       5555  unspecified               2017  manual    125      c4   125000   \n",
       "...       ...          ...                ...     ...    ...     ...      ...   \n",
       "282905   5000  unspecified               2017  manual    120   other   150000   \n",
       "282914    850        sedan               1989  manual    113      80   150000   \n",
       "282922   1550        small               1990  manual     54    golf   150000   \n",
       "282925   3300        coupe               1957  manual     40   other   100000   \n",
       "282932  12800  convertible               1991  manual    211   other   150000   \n",
       "\n",
       "          fuel_type          brand  not_repaired date_created  \n",
       "3            petrol            bmw          True   2016-04-04  \n",
       "5            petrol     volkswagen         False   2016-03-21  \n",
       "15           petrol           opel         False   2016-03-23  \n",
       "17           petrol  mercedes_benz          True   2016-03-13  \n",
       "19      unspecified        citroen         False   2016-03-10  \n",
       "...             ...            ...           ...          ...  \n",
       "282905  unspecified        citroen          True   2016-03-05  \n",
       "282914       petrol           audi          True   2016-03-05  \n",
       "282922       petrol     volkswagen         False   2016-04-04  \n",
       "282925       petrol        trabant         False   2016-03-07  \n",
       "282932       petrol        porsche         False   2016-03-12  \n",
       "\n",
       "[33243 rows x 11 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check \n",
    "df.query('registration_year < 1996 or registration_year > 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% 12\n"
     ]
    }
   ],
   "source": [
    "# Percentage of cars registered outside the period of years 1996-2016\n",
    "print('%', round(len(df.query('registration_year < 1996 or registration_year > 2016')) / len(df) * 100))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El 12% de los registros están fuera del periodo 1996 - 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter year 1996 - 2016\n",
    "df = df.query('registration_year >= 1996 and registration_year <= 2016')\n",
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(249719, 11)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos gráficamente los vehículos distribuidos según el año de matriculación (periodo 1996 - 2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['registration_year']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kilometraje\n",
    "Revisaremos la información sobre el kilometraje del vehículo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.mileage.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La mayor parte de los vehículos que están en venta tienen un kilometraje por encima de los 125.000 km."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Potencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2,     3,     4,     5,     6,     7,     8,\n",
       "           9,    10,    11,    12,    13,    14,    15,    16,    17,\n",
       "          18,    19,    20,    22,    23,    24,    25,    26,    29,\n",
       "          30,    31,    32,    33,    34,    35,    36,    37,    38,\n",
       "          39,    40,    41,    42,    43,    44,    45,    46,    47,\n",
       "          48,    49,    50,    51,    52,    53,    54,    55,    56,\n",
       "          57,    58,    59,    60,    61,    62,    63,    64,    65,\n",
       "          66,    67,    68,    69,    70,    71,    72,    73,    74,\n",
       "          75,    76,    77,    78,    79,    80,    81,    82,    83,\n",
       "          84,    85,    86,    87,    88,    89,    90,    91,    92,\n",
       "          93,    94,    95,    96,    97,    98,    99,   100,   101,\n",
       "         102,   103,   104,   105,   106,   107,   108,   109,   110,\n",
       "         111,   112,   113,   114,   115,   116,   117,   118,   119,\n",
       "         120,   121,   122,   123,   124,   125,   126,   127,   128,\n",
       "         129,   130,   131,   132,   133,   134,   135,   136,   137,\n",
       "         138,   139,   140,   141,   142,   143,   144,   145,   146,\n",
       "         147,   148,   149,   150,   151,   152,   153,   154,   155,\n",
       "         156,   157,   158,   159,   160,   161,   162,   163,   164,\n",
       "         165,   166,   167,   168,   169,   170,   171,   172,   173,\n",
       "         174,   175,   176,   177,   178,   179,   180,   181,   182,\n",
       "         183,   184,   185,   186,   187,   188,   189,   190,   191,\n",
       "         192,   193,   194,   195,   196,   197,   198,   199,   200,\n",
       "         201,   202,   203,   204,   205,   206,   207,   208,   209,\n",
       "         210,   211,   212,   213,   214,   215,   216,   217,   218,\n",
       "         219,   220,   222,   223,   224,   225,   226,   227,   228,\n",
       "         229,   230,   231,   232,   233,   234,   235,   236,   237,\n",
       "         238,   239,   240,   241,   242,   243,   244,   245,   246,\n",
       "         247,   248,   249,   250,   251,   252,   253,   254,   255,\n",
       "         256,   257,   258,   259,   260,   261,   262,   264,   265,\n",
       "         266,   268,   269,   270,   271,   272,   273,   274,   275,\n",
       "         276,   277,   278,   279,   280,   281,   283,   284,   285,\n",
       "         286,   287,   288,   289,   290,   292,   293,   294,   295,\n",
       "         296,   297,   298,   299,   300,   301,   303,   304,   305,\n",
       "         306,   307,   308,   309,   310,   311,   313,   314,   315,\n",
       "         316,   317,   318,   320,   321,   322,   323,   324,   325,\n",
       "         326,   328,   329,   330,   331,   332,   333,   334,   335,\n",
       "         336,   337,   338,   339,   340,   341,   343,   344,   345,\n",
       "         346,   347,   348,   349,   350,   351,   353,   354,   355,\n",
       "         356,   357,   358,   360,   361,   362,   363,   364,   365,\n",
       "         367,   368,   370,   371,   374,   376,   377,   379,   380,\n",
       "         381,   382,   385,   386,   387,   388,   390,   392,   394,\n",
       "         396,   399,   400,   401,   405,   408,   409,   411,   416,\n",
       "         420,   421,   426,   428,   430,   431,   435,   442,   445,\n",
       "         450,   457,   459,   475,   476,   485,   487,   489,   490,\n",
       "         500,   504,   505,   507,   508,   510,   514,   515,   517,\n",
       "         519,   520,   521,   525,   550,   553,   560,   579,   585,\n",
       "         600,   601,   602,   603,   606,   650,   682,   696,   702,\n",
       "         732,   750,   751,   776,   800,   871,   900,   902,   903,\n",
       "         907,   909,   923,   950,   952,   953,   999,  1000,  1001,\n",
       "        1002,  1003,  1004,  1005,  1011,  1012,  1016,  1021,  1024,\n",
       "        1054,  1055,  1056,  1062,  1079,  1090,  1100,  1102,  1111,\n",
       "        1115,  1120,  1149,  1158,  1160,  1162,  1164,  1199,  1223,\n",
       "        1240,  1241,  1275,  1288,  1300,  1317,  1339,  1360,  1362,\n",
       "        1363,  1398,  1399,  1400,  1403,  1405,  1416,  1432,  1433,\n",
       "        1436,  1500,  1502,  1506,  1521,  1595,  1598,  1600,  1689,\n",
       "        1700,  1701,  1703,  1704,  1707,  1753,  1779,  1780,  1781,\n",
       "        1783,  1793,  1796,  1799,  1800,  1801,  1900,  1910,  1920,\n",
       "        1933,  1968,  1995,  1998,  2000,  2004,  2005,  2007,  2009,\n",
       "        2016,  2017,  2018,  2200,  2331,  2340,  2389,  2402,  2729,\n",
       "        3750,  4400,  4507,  4700,  5411,  5420,  5815,  5867,  6006,\n",
       "        6010,  6011,  6045,  6062,  6226,  6920,  7508,  7511,  7518,\n",
       "        8259,  8500,  9007, 10000, 10311, 10317, 10520, 10710, 10910,\n",
       "       11011, 11509, 11530, 12510, 13636, 14009, 15001, 15020, 15033,\n",
       "       16311, 16312, 17700, 19208, 19312, 20000], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['power'].sort_values().unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existen vehiculos con potencias desde 0 hasta 2000. Grafiquemos esta distribución, e identifiquemos outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['power'].hist(bins=50, range=(0,1000), edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>not_repaired</th>\n",
       "      <th>date_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1500</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>2016</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>0</td>\n",
       "      <td>kangoo</td>\n",
       "      <td>150000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>renault</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4700</td>\n",
       "      <td>wagon</td>\n",
       "      <td>2005</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>signum</td>\n",
       "      <td>150000</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>opel</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>550</td>\n",
       "      <td>wagon</td>\n",
       "      <td>1999</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>astra</td>\n",
       "      <td>150000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>opel</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-03-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4290</td>\n",
       "      <td>bus</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>combo</td>\n",
       "      <td>150000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>opel</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>8000</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>2009</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>100000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249692</th>\n",
       "      <td>7900</td>\n",
       "      <td>bus</td>\n",
       "      <td>2007</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>b_klasse</td>\n",
       "      <td>125000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>mercedes_benz</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249695</th>\n",
       "      <td>390</td>\n",
       "      <td>small</td>\n",
       "      <td>1997</td>\n",
       "      <td>auto</td>\n",
       "      <td>0</td>\n",
       "      <td>corsa</td>\n",
       "      <td>100000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>opel</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249712</th>\n",
       "      <td>3999</td>\n",
       "      <td>wagon</td>\n",
       "      <td>2005</td>\n",
       "      <td>manual</td>\n",
       "      <td>3</td>\n",
       "      <td>3er</td>\n",
       "      <td>150000</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>bmw</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249715</th>\n",
       "      <td>1150</td>\n",
       "      <td>bus</td>\n",
       "      <td>2000</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>zafira</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>opel</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249716</th>\n",
       "      <td>0</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>2005</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>colt</td>\n",
       "      <td>150000</td>\n",
       "      <td>petrol</td>\n",
       "      <td>mitsubishi</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-03-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14702 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price vehicle_type  registration_year      gearbox  power     model   \n",
       "20       1500  unspecified               2016  unspecified      0    kangoo  \\\n",
       "27       4700        wagon               2005       manual      0    signum   \n",
       "28        550        wagon               1999       manual      0     astra   \n",
       "56       4290          bus               2008       manual      0     combo   \n",
       "73       8000  unspecified               2009       manual      0      golf   \n",
       "...       ...          ...                ...          ...    ...       ...   \n",
       "249692   7900          bus               2007       manual      0  b_klasse   \n",
       "249695    390        small               1997         auto      0     corsa   \n",
       "249712   3999        wagon               2005       manual      3       3er   \n",
       "249715   1150          bus               2000       manual      0    zafira   \n",
       "249716      0  unspecified               2005       manual      0      colt   \n",
       "\n",
       "        mileage    fuel_type          brand  not_repaired date_created  \n",
       "20       150000     gasoline        renault         False   2016-03-28  \n",
       "27       150000  unspecified           opel         False   2016-03-17  \n",
       "28       150000     gasoline           opel          True   2016-03-26  \n",
       "56       150000     gasoline           opel         False   2016-03-11  \n",
       "73       100000       petrol     volkswagen         False   2016-03-20  \n",
       "...         ...          ...            ...           ...          ...  \n",
       "249692   125000       petrol  mercedes_benz         False   2016-03-13  \n",
       "249695   100000       petrol           opel          True   2016-04-04  \n",
       "249712   150000     gasoline            bmw         False   2016-04-02  \n",
       "249715   150000       petrol           opel         False   2016-03-27  \n",
       "249716   150000       petrol     mitsubishi          True   2016-03-21  \n",
       "\n",
       "[14702 rows x 11 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('power <= 10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "power\n",
       "75      0.065906\n",
       "0       0.058278\n",
       "60      0.043581\n",
       "150     0.043161\n",
       "140     0.043144\n",
       "          ...   \n",
       "519     0.000004\n",
       "585     0.000004\n",
       "6006    0.000004\n",
       "5815    0.000004\n",
       "1241    0.000004\n",
       "Name: proportion, Length: 573, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.power.value_counts(normalize=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que el 5,8% de registros tiene power=0, estos deben ser eliminados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249719"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235166, 11)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove power = 0\n",
    "df = df.drop(df[df.power == 0].index)\n",
    "df = df.reset_index(drop=True)\n",
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que los datos en power tienen un sesgo a la derecha, es decir valores muy grandes con una frecuencia baja. A continuacion buscaremos y eliminaremos outliers de ser el caso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# funtion clean_outliers\n",
    "def clean_outliers(df, col):\n",
    "\n",
    "    Q1 = df[col].quantile(0.25)\n",
    "    Q3 = df[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    min = (Q1 - 1.5 * IQR).astype('int')\n",
    "    max = (Q3 + 1.5 * IQR).astype('int')\n",
    "\n",
    "    indexes = df[(df[col] <= min) | (df[col] >= max)].index\n",
    "    df = df.drop(indexes)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['power']].query('power < 500').boxplot()\n",
    "plt.title('Boxplot distribution \"power\"')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235166"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "removed outliers 2.46% \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Clean outliers in \"power\"\n",
    "total = len(df)\n",
    "df = clean_outliers(df, 'power')\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "percent = (total - len(df)) / total\n",
    "print(f'removed outliers {percent:.2%} \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229390"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizamos los datos de \"power\" después de eliminar el 2.46% de registros outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['power']].boxplot()\n",
    "plt.title('Boxplot distribution \"power\"')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro dataset queda saneado con valores mayores a 0 y menos a 250 en la columna \"power\"."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Marca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 brand car')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.brand.value_counts(ascending=False).head(10).plot(kind='bar', rot=25)\n",
    "plt.title('Top 10 brand car')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.price.hist(bins=120, edgecolor='black', figsize=(10,4))\n",
    "plt.title('Boxplot distribution \"price\"')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos observar en el histograma una distribución con una asimetría positiva, los vehículos más se ponen en venta están por debajo de 3000 euros, y entre mayor es precio menor la frecuencia de venta, podemos observar vehículos hasta en 20.000 euros. También podemos observar vehículos que valen menos que 100 euros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02\n"
     ]
    }
   ],
   "source": [
    "print(round(len(df.query('price < 100')) / len(df),2))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El valor mínimo que se pone en venta un vehículo deberá estar validado en la app de venta de carros, sin embargo para fines de este estudio podríamos establecer como valor mínimo de venta de 100 euros. Por lo que tomaremos los valores menores a 100 y los removeremos de nuestro dataset, estos representan el 2% de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(229390, 11)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225605, 11)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove price < 100 euros\n",
    "df = df.drop(df[df.price < 100].index)\n",
    "df = df.reset_index(drop=True)\n",
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, hemos eliminado los registros con precios menores a 100 euros. Nuestro dataset ha disminuido un 2%."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlaciones\n",
    "\n",
    "Revisemos la interdependencia del precio con respecto a otras características, mediante la correlación:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>registration_year</th>\n",
       "      <th>power</th>\n",
       "      <th>mileage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.672759</td>\n",
       "      <td>0.495534</td>\n",
       "      <td>-0.425847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registration_year</th>\n",
       "      <td>0.672759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.139703</td>\n",
       "      <td>-0.483183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>0.495534</td>\n",
       "      <td>0.139703</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mileage</th>\n",
       "      <td>-0.425847</td>\n",
       "      <td>-0.483183</td>\n",
       "      <td>0.122896</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      price  registration_year     power   mileage\n",
       "price              1.000000           0.672759  0.495534 -0.425847\n",
       "registration_year  0.672759           1.000000  0.139703 -0.483183\n",
       "power              0.495534           0.139703  1.000000  0.122896\n",
       "mileage           -0.425847          -0.483183  0.122896  1.000000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['price','registration_year','power','mileage']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='price', y='registration_year', kind='scatter', alpha=0.3) \n",
    "plt.title('Correlation price - year')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos la correlacion entre precio y varias características del vehículo. Podemos ver que hay conexiones muy débiles ya que son cercanas a cero, y conexiones medianamente fuerte price con registration_year con un 0.67 de correlación."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Procesamiento de datos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Escalado de datos\n",
    "Antes de empezar a entrenar nuestro modelo vamos a aplicar un escalado en los datos con la estandarización de datos usando `StandardScaler` a las siguientes columnas numéricas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy dataframe df to df_scaler\n",
    "df_scaler = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numerical feature scaling\n",
    "numeric = ['registration_year', 'power', 'mileage']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df_scaler[numeric])\n",
    "\n",
    "df_scaler[numeric] = scaler.transform(df_scaler[numeric])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>not_repaired</th>\n",
       "      <th>date_created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>1.390021</td>\n",
       "      <td>manual</td>\n",
       "      <td>1.555349</td>\n",
       "      <td>unspecified</td>\n",
       "      <td>-0.042657</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>True</td>\n",
       "      <td>2016-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>-0.680925</td>\n",
       "      <td>manual</td>\n",
       "      <td>-0.964598</td>\n",
       "      <td>golf</td>\n",
       "      <td>0.626120</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>0.768737</td>\n",
       "      <td>manual</td>\n",
       "      <td>-1.096073</td>\n",
       "      <td>fabia</td>\n",
       "      <td>-0.978945</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2200</td>\n",
       "      <td>convertible</td>\n",
       "      <td>-0.059641</td>\n",
       "      <td>manual</td>\n",
       "      <td>-0.219570</td>\n",
       "      <td>2_reihe</td>\n",
       "      <td>0.626120</td>\n",
       "      <td>petrol</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>sedan</td>\n",
       "      <td>-0.059641</td>\n",
       "      <td>manual</td>\n",
       "      <td>-0.307220</td>\n",
       "      <td>3_reihe</td>\n",
       "      <td>0.626120</td>\n",
       "      <td>petrol</td>\n",
       "      <td>mazda</td>\n",
       "      <td>False</td>\n",
       "      <td>2016-03-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price vehicle_type  registration_year gearbox     power        model   \n",
       "0  18300        coupe           1.390021  manual  1.555349  unspecified  \\\n",
       "1   1500        small          -0.680925  manual -0.964598         golf   \n",
       "2   3600        small           0.768737  manual -1.096073        fabia   \n",
       "3   2200  convertible          -0.059641  manual -0.219570      2_reihe   \n",
       "4   2000        sedan          -0.059641  manual -0.307220      3_reihe   \n",
       "\n",
       "    mileage fuel_type       brand  not_repaired date_created  \n",
       "0 -0.042657  gasoline        audi          True   2016-03-24  \n",
       "1  0.626120    petrol  volkswagen         False   2016-03-17  \n",
       "2 -0.978945  gasoline       skoda         False   2016-03-31  \n",
       "3  0.626120    petrol     peugeot         False   2016-04-01  \n",
       "4  0.626120    petrol       mazda         False   2016-03-26  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaler.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Codificar características categóricas\n",
    "Realizaremos codificacion de variables categóricas usando *One-Hot Encoding*. Las columnas categóricas a las que se aplicará la codificación son las siguientes: 'vehicle_type', 'gearbox', 'model', 'fuel_type', y 'brand'. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero identificamos las características y objetivo: `features`, `target`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# identify characteristics and target\n",
    "features = df_scaler.drop(['price','date_created'], axis=1)\n",
    "target   = df_scaler['price']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guardamos nuestras características con codificación One-Hot en: `features_ohe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical features with One-Hot encoding\n",
    "features_ohe = pd.get_dummies(data = features, \n",
    "                              columns=['vehicle_type', 'gearbox', 'model', 'fuel_type', 'brand'], \n",
    "                              drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((225605, 9), (225605, 304))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape, features_ohe.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver el tamaño de nuestras características con y sin One-Hot, las que se le aplicó la codificación aumentaron considerablemente el número de columnas, con razón puesto que teníamos mucha variedad en la marca y el modelo del vehículo."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segmentamos nuestro dataset en datos para entrenamiento, validación y test, proporción: 4:2:2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, X_train_ohe, X_valid_ohe, y_train, y_valid = train_test_split(features, features_ohe, target, test_size=0.40, random_state=12345)\n",
    "X_valid, X_test,  X_valid_ohe, X_test_ohe,  y_valid, y_test  = train_test_split(X_valid,  X_valid_ohe, y_valid, test_size=0.50, random_state=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size\n",
      "- data train: features(135363, 9),features ohe: (135363, 304),target(135363,)\n",
      "- data valid: features(45121, 9), features ohe: (45121, 304), target(45121,)\n",
      "- data test:  features(45121, 9), features ohe: (45121, 304), target(45121,)\n"
     ]
    }
   ],
   "source": [
    "print('Size')\n",
    "print(f'- data train: features{X_train.shape},features ohe: {X_train_ohe.shape},target{y_train.shape}')\n",
    "print(f'- data valid: features{X_valid.shape}, features ohe: {X_valid_ohe.shape}, target{y_valid.shape}')\n",
    "print(f'- data test:  features{X_test.shape}, features ohe: {X_test_ohe.shape}, target{y_test.shape}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entrenamiento del modelo "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a entrenar varios modelos para predecir el precio de un vehículo. Buscaremos el mejor con el menor error usando la métrica RMSE para evaluar la calidad de los modelos. Además revisaremos la velocidad de predicción y el tiempo para el entrenamiento. Nuestros modelos probarán técnicas de potenciación de gradiente, y un modelo basado en árboles, todos ajustando hiperparámetros. Además, realizaremos una prueba de cordura, para ella realizaremos un modelo usando regresión lineal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a data frame named \"summary_models\" where we will save information about our different models\n",
    "summary_models = pd.DataFrame(columns=['model', 'score_rmse', 'training_time_sec', 'prediction_speed_sec'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo base con Regresión lineal\n",
    "Entrenaremos un modelo de regresión lineal como prueba de cordura para el mejor modelo que entrenemos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Linear regression\n",
      "RMSE= 2149.98,    time train: 2.54s,    time prediction: 0.08s\n"
     ]
    }
   ],
   "source": [
    "# Linear Regression\n",
    "model_lr = LinearRegression()\n",
    "start_time = time.time()\n",
    "model_lr.fit(X_train_ohe, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Prediction valid data\n",
    "start_time = time.time()\n",
    "y_predict = model_lr.predict(X_valid_ohe)\n",
    "pred_time = time.time() - start_time\n",
    "\n",
    "# Metric\n",
    "rmse = mean_squared_error(y_valid, y_predict, squared=False) \n",
    "print('Model Linear regression')\n",
    "\n",
    "# Print the times of train and predict\n",
    "print(\"RMSE= {:.2f},    time train: {:.2f}s,    time prediction: {:.2f}s\".format(rmse, train_time, pred_time))\n",
    "\n",
    "# Save info\n",
    "summary_models.loc['LinearRegression'] = {'model': model_lr, \n",
    "                                          'score_rmse': rmse, \n",
    "                                          'training_time_sec':train_time, \n",
    "                                          'prediction_speed_sec':pred_time}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro modelo base con Regresion Lineal, tiene un `RMSE` 2149, es bastate alto. Por lo que el modelo que vamos a entrenar debe ser mejor que este, debemos minimizar este error."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelo Bosque Aleatorio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor, adjusting hyperparameters:\n",
      "RMSE= 2402.08,    tree: 4,    max_depth: 4\n",
      "RMSE= 1921.40,    tree: 4,    max_depth: 8\n",
      "RMSE= 1681.73,    tree: 4,    max_depth: 12\n",
      "RMSE= 1566.12,    tree: 4,    max_depth: 16\n",
      "RMSE= 2407.96,    tree: 6,    max_depth: 4\n",
      "RMSE= 1912.09,    tree: 6,    max_depth: 8\n",
      "RMSE= 1671.76,    tree: 6,    max_depth: 12\n",
      "RMSE= 1551.47,    tree: 6,    max_depth: 16\n",
      "RMSE= 2405.59,    tree: 8,    max_depth: 4\n",
      "RMSE= 1908.77,    tree: 8,    max_depth: 8\n",
      "RMSE= 1666.35,    tree: 8,    max_depth: 12\n",
      "RMSE= 1542.55,    tree: 8,    max_depth: 16\n",
      "RMSE= 2411.84,    tree: 10,    max_depth: 4\n",
      "RMSE= 1910.24,    tree: 10,    max_depth: 8\n",
      "RMSE= 1664.78,    tree: 10,    max_depth: 12\n",
      "RMSE= 1539.97,    tree: 10,    max_depth: 16\n",
      "\n",
      "Best RMSE 1539.9743616298827\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=16, n_estimators=10, random_state=12345)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=16, n_estimators=10, random_state=12345)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=16, n_estimators=10, random_state=12345)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjusting hyperparameters to Randon Forest:\n",
    "\n",
    "print('Random Forest Regressor, adjusting hyperparameters:')\n",
    "\n",
    "best_score = float('inf')\n",
    "for tree in range(4, 11, 2):\n",
    "    for depth in range(4, 17, 4):\n",
    "        model_rf = RandomForestRegressor(random_state = 12345, \n",
    "                                         n_estimators = tree, \n",
    "                                         max_depth = depth)\n",
    "\n",
    "        # Training\n",
    "        model_rf.fit(X_train_ohe, y_train)\n",
    "\n",
    "        # predicción para datos de valiación\n",
    "        y_pred = model_rf.predict(X_valid_ohe)\n",
    "\n",
    "        # Metric\n",
    "        rmse = mean_squared_error(y_valid, y_pred, squared=False) \n",
    "        print(\"RMSE= {:.2f},    tree: {},    max_depth: {}\".format(rmse, tree, depth))\n",
    "\n",
    "        if rmse < best_score:\n",
    "            best_score = rmse\n",
    "            best_model = model_rf\n",
    "\n",
    "print(f'\\nBest RMSE {best_score}')\n",
    "display(best_model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, hemos obtenido nuestros mejores hiperparámetros. A continuación realizaremos el entrenamiento final de este modelo RandomForest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Random Forest Regressor\n",
      "RMSE= 1539.97,    time train: 15.29s,    time prediction: 0.11s\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Regressor:\n",
    "\n",
    "print('Model Random Forest Regressor')\n",
    "\n",
    "model_rf = RandomForestRegressor(random_state=12345, \n",
    "                                 n_estimators=10, \n",
    "                                 max_depth=16)\n",
    "\n",
    "# Training\n",
    "start_time = time.time()\n",
    "model_rf.fit(X_train_ohe, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Predict\n",
    "start_time = time.time()\n",
    "y_pred = model_rf.predict(X_valid_ohe)\n",
    "pred_time = time.time() - start_time\n",
    "\n",
    "# Metric\n",
    "rmse = mean_squared_error(y_valid, y_pred, squared=False) \n",
    "\n",
    "print(\"RMSE= {:.2f},    time train: {:.2f}s,    time prediction: {:.2f}s\".format(rmse, train_time, pred_time))\n",
    "\n",
    "# Save info\n",
    "summary_models.loc['RandomForestRegressor'] = {'model': model_rf, \n",
    "                                               'score_rmse': rmse, \n",
    "                                               'training_time_sec':train_time, \n",
    "                                               'prediction_speed_sec':pred_time}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro modelo RandomForestRegressor obtuvo un `RMSE` 1539 mejor que nuestro modelo base."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelos con potenciación de gradiente (Gradient boosting)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo CatBoostRegressor\n",
    "\n",
    "Entrenaremos un modelo con CatBoostRegressor; este está basado en árboles de decisión. Realizaremos un ajuste de hiperparámetros con GridSearchCV, donde variaremos la profundidad del árbol, tasa de aprendizaje y el número de iteraciones.\n",
    "\n",
    "Además usaremos nuestras características sin codificación ohe `X_train, X_valid`, ya que CatBoost aplica internamente técnicas de codificación para características categóricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 3539.6593718\ttotal: 171ms\tremaining: 1.53s\n",
      "9:\tlearn: 1903.8110332\ttotal: 350ms\tremaining: 0us\n",
      "0:\tlearn: 3549.9211421\ttotal: 23.1ms\tremaining: 208ms\n",
      "9:\tlearn: 1907.6558277\ttotal: 193ms\tremaining: 0us\n",
      "0:\tlearn: 3535.9414445\ttotal: 18.8ms\tremaining: 169ms\n",
      "9:\tlearn: 1883.6858118\ttotal: 183ms\tremaining: 0us\n",
      "0:\tlearn: 4038.0953836\ttotal: 21.8ms\tremaining: 196ms\n",
      "9:\tlearn: 2227.2652321\ttotal: 185ms\tremaining: 0us\n",
      "0:\tlearn: 4049.9920566\ttotal: 18.7ms\tremaining: 169ms\n",
      "9:\tlearn: 2220.3933193\ttotal: 181ms\tremaining: 0us\n",
      "0:\tlearn: 4037.4381234\ttotal: 19.3ms\tremaining: 174ms\n",
      "9:\tlearn: 2216.5329960\ttotal: 189ms\tremaining: 0us\n",
      "0:\tlearn: 3539.6593718\ttotal: 19.2ms\tremaining: 1.9s\n",
      "99:\tlearn: 1605.6061474\ttotal: 1.77s\tremaining: 0us\n",
      "0:\tlearn: 3549.9211421\ttotal: 19.6ms\tremaining: 1.94s\n",
      "99:\tlearn: 1585.5350443\ttotal: 1.83s\tremaining: 0us\n",
      "0:\tlearn: 3535.9414445\ttotal: 19.8ms\tremaining: 1.96s\n",
      "99:\tlearn: 1586.9943518\ttotal: 1.86s\tremaining: 0us\n",
      "0:\tlearn: 4038.0953836\ttotal: 18.7ms\tremaining: 1.85s\n",
      "99:\tlearn: 1653.6325426\ttotal: 1.75s\tremaining: 0us\n",
      "0:\tlearn: 4049.9920566\ttotal: 18.3ms\tremaining: 1.81s\n",
      "99:\tlearn: 1638.1783052\ttotal: 1.74s\tremaining: 0us\n",
      "0:\tlearn: 4037.4381234\ttotal: 19.2ms\tremaining: 1.9s\n",
      "99:\tlearn: 1638.2294168\ttotal: 1.74s\tremaining: 0us\n",
      "0:\tlearn: 3561.8699662\ttotal: 28.2ms\tremaining: 5.61s\n",
      "100:\tlearn: 1591.3283263\ttotal: 2.65s\tremaining: 2.6s\n",
      "199:\tlearn: 1537.5253500\ttotal: 5.24s\tremaining: 0us\n",
      "0:\tlearn: 3556.6831619\ttotal: 28.9ms\tremaining: 5.75s\n",
      "100:\tlearn: 1580.6629076\ttotal: 2.57s\tremaining: 2.52s\n",
      "199:\tlearn: 1522.2995263\ttotal: 4.98s\tremaining: 0us\n",
      "0:\tlearn: 3565.8062562\ttotal: 28.1ms\tremaining: 5.58s\n",
      "100:\tlearn: 1584.9599092\ttotal: 2.62s\tremaining: 2.57s\n",
      "199:\tlearn: 1532.9484925\ttotal: 4.98s\tremaining: 0us\n",
      "0:\tlearn: 4049.0867420\ttotal: 28.2ms\tremaining: 5.61s\n",
      "100:\tlearn: 1632.6058121\ttotal: 2.49s\tremaining: 2.44s\n",
      "199:\tlearn: 1575.1974529\ttotal: 4.86s\tremaining: 0us\n",
      "0:\tlearn: 4053.3348872\ttotal: 30.7ms\tremaining: 6.12s\n",
      "100:\tlearn: 1620.8373339\ttotal: 2.64s\tremaining: 2.59s\n",
      "199:\tlearn: 1563.9107355\ttotal: 5.06s\tremaining: 0us\n",
      "0:\tlearn: 4052.2049099\ttotal: 29.7ms\tremaining: 5.91s\n",
      "100:\tlearn: 1624.8038641\ttotal: 2.64s\tremaining: 2.59s\n",
      "199:\tlearn: 1564.0615006\ttotal: 5.07s\tremaining: 0us\n",
      "0:\tlearn: 3430.6616387\ttotal: 27.1ms\tremaining: 244ms\n",
      "9:\tlearn: 1788.4777908\ttotal: 271ms\tremaining: 0us\n",
      "0:\tlearn: 3445.1588426\ttotal: 25.9ms\tremaining: 233ms\n",
      "9:\tlearn: 1755.9409790\ttotal: 274ms\tremaining: 0us\n",
      "0:\tlearn: 3425.6439406\ttotal: 27.2ms\tremaining: 245ms\n",
      "9:\tlearn: 1761.3838341\ttotal: 276ms\tremaining: 0us\n",
      "0:\tlearn: 3984.8585218\ttotal: 27.2ms\tremaining: 245ms\n",
      "9:\tlearn: 2042.3134702\ttotal: 267ms\tremaining: 0us\n",
      "0:\tlearn: 3998.7931032\ttotal: 25.8ms\tremaining: 232ms\n",
      "9:\tlearn: 2024.8960908\ttotal: 258ms\tremaining: 0us\n",
      "0:\tlearn: 3983.6196034\ttotal: 25.1ms\tremaining: 226ms\n",
      "9:\tlearn: 2040.0685728\ttotal: 282ms\tremaining: 0us\n",
      "0:\tlearn: 3430.6616387\ttotal: 24.7ms\tremaining: 2.45s\n",
      "99:\tlearn: 1500.4579139\ttotal: 2.53s\tremaining: 0us\n",
      "0:\tlearn: 3445.1588426\ttotal: 25.9ms\tremaining: 2.56s\n",
      "99:\tlearn: 1486.1753424\ttotal: 2.45s\tremaining: 0us\n",
      "0:\tlearn: 3425.6439406\ttotal: 25.3ms\tremaining: 2.5s\n",
      "99:\tlearn: 1492.2364024\ttotal: 2.45s\tremaining: 0us\n",
      "0:\tlearn: 3984.8585218\ttotal: 24.9ms\tremaining: 2.46s\n",
      "99:\tlearn: 1560.9491908\ttotal: 2.47s\tremaining: 0us\n",
      "0:\tlearn: 3998.7931032\ttotal: 24.7ms\tremaining: 2.44s\n",
      "99:\tlearn: 1547.4210699\ttotal: 2.48s\tremaining: 0us\n",
      "0:\tlearn: 3983.6196034\ttotal: 25.5ms\tremaining: 2.53s\n",
      "99:\tlearn: 1548.4248029\ttotal: 2.58s\tremaining: 0us\n",
      "0:\tlearn: 3425.9257344\ttotal: 45.1ms\tremaining: 8.97s\n",
      "100:\tlearn: 1494.8346546\ttotal: 3.91s\tremaining: 3.83s\n",
      "199:\tlearn: 1434.2881881\ttotal: 7.71s\tremaining: 0us\n",
      "0:\tlearn: 3439.1689262\ttotal: 44.8ms\tremaining: 8.91s\n",
      "100:\tlearn: 1482.8317777\ttotal: 3.91s\tremaining: 3.83s\n",
      "199:\tlearn: 1421.1207300\ttotal: 7.68s\tremaining: 0us\n",
      "0:\tlearn: 3430.6478941\ttotal: 45.6ms\tremaining: 9.08s\n",
      "100:\tlearn: 1483.3977691\ttotal: 3.85s\tremaining: 3.77s\n",
      "199:\tlearn: 1422.3604838\ttotal: 7.67s\tremaining: 0us\n",
      "0:\tlearn: 3982.6463761\ttotal: 44.7ms\tremaining: 8.9s\n",
      "100:\tlearn: 1546.5569915\ttotal: 4.06s\tremaining: 3.98s\n",
      "199:\tlearn: 1488.0208098\ttotal: 7.78s\tremaining: 0us\n",
      "0:\tlearn: 3995.9175937\ttotal: 43.5ms\tremaining: 8.65s\n",
      "100:\tlearn: 1535.3448430\ttotal: 3.82s\tremaining: 3.75s\n",
      "199:\tlearn: 1470.9165709\ttotal: 7.67s\tremaining: 0us\n",
      "0:\tlearn: 3986.1180600\ttotal: 46.4ms\tremaining: 9.23s\n",
      "100:\tlearn: 1538.4969187\ttotal: 3.89s\tremaining: 3.81s\n",
      "199:\tlearn: 1474.9219260\ttotal: 7.72s\tremaining: 0us\n",
      "0:\tlearn: 3357.5748619\ttotal: 33ms\tremaining: 297ms\n",
      "9:\tlearn: 1703.9639298\ttotal: 356ms\tremaining: 0us\n",
      "0:\tlearn: 3361.8463463\ttotal: 32.1ms\tremaining: 289ms\n",
      "9:\tlearn: 1683.6570210\ttotal: 331ms\tremaining: 0us\n",
      "0:\tlearn: 3353.8146740\ttotal: 32.1ms\tremaining: 289ms\n",
      "9:\tlearn: 1687.0153113\ttotal: 336ms\tremaining: 0us\n",
      "0:\tlearn: 3949.7834469\ttotal: 33ms\tremaining: 297ms\n",
      "9:\tlearn: 1950.2745355\ttotal: 328ms\tremaining: 0us\n",
      "0:\tlearn: 3958.8413976\ttotal: 32.5ms\tremaining: 292ms\n",
      "9:\tlearn: 1922.6098689\ttotal: 327ms\tremaining: 0us\n",
      "0:\tlearn: 3949.1813307\ttotal: 32.1ms\tremaining: 289ms\n",
      "9:\tlearn: 1926.6691676\ttotal: 333ms\tremaining: 0us\n",
      "0:\tlearn: 3357.5748619\ttotal: 33.2ms\tremaining: 3.29s\n",
      "99:\tlearn: 1407.9984351\ttotal: 3.42s\tremaining: 0us\n",
      "0:\tlearn: 3361.8463463\ttotal: 33.9ms\tremaining: 3.35s\n",
      "99:\tlearn: 1399.3399224\ttotal: 3.37s\tremaining: 0us\n",
      "0:\tlearn: 3353.8146740\ttotal: 34ms\tremaining: 3.36s\n",
      "99:\tlearn: 1398.3098453\ttotal: 3.4s\tremaining: 0us\n",
      "0:\tlearn: 3949.7834469\ttotal: 35.3ms\tremaining: 3.49s\n",
      "99:\tlearn: 1477.2564611\ttotal: 3.33s\tremaining: 0us\n",
      "0:\tlearn: 3958.8413976\ttotal: 33ms\tremaining: 3.26s\n",
      "99:\tlearn: 1458.7374913\ttotal: 3.5s\tremaining: 0us\n",
      "0:\tlearn: 3949.1813307\ttotal: 34.7ms\tremaining: 3.43s\n",
      "99:\tlearn: 1467.3797200\ttotal: 3.48s\tremaining: 0us\n",
      "0:\tlearn: 3345.4706233\ttotal: 64.9ms\tremaining: 12.9s\n",
      "100:\tlearn: 1401.6946257\ttotal: 5.32s\tremaining: 5.21s\n",
      "199:\tlearn: 1317.7711134\ttotal: 10.9s\tremaining: 0us\n",
      "0:\tlearn: 3360.3701028\ttotal: 63.3ms\tremaining: 12.6s\n",
      "100:\tlearn: 1391.2216334\ttotal: 5.39s\tremaining: 5.28s\n",
      "199:\tlearn: 1320.6881061\ttotal: 11.1s\tremaining: 0us\n",
      "0:\tlearn: 3348.1895242\ttotal: 64.4ms\tremaining: 12.8s\n",
      "100:\tlearn: 1400.7681282\ttotal: 5.82s\tremaining: 5.7s\n",
      "199:\tlearn: 1317.7849492\ttotal: 11.5s\tremaining: 0us\n",
      "0:\tlearn: 3944.0856626\ttotal: 64.4ms\tremaining: 12.8s\n",
      "100:\tlearn: 1464.4309849\ttotal: 5.8s\tremaining: 5.69s\n",
      "199:\tlearn: 1389.3312124\ttotal: 11.5s\tremaining: 0us\n",
      "0:\tlearn: 3958.1627665\ttotal: 59.6ms\tremaining: 11.9s\n",
      "100:\tlearn: 1443.5909988\ttotal: 5.64s\tremaining: 5.53s\n",
      "199:\tlearn: 1379.1855665\ttotal: 11.3s\tremaining: 0us\n",
      "0:\tlearn: 3946.5809904\ttotal: 67.1ms\tremaining: 13.3s\n",
      "100:\tlearn: 1451.8140174\ttotal: 5.44s\tremaining: 5.33s\n",
      "199:\tlearn: 1380.9560852\ttotal: 10.8s\tremaining: 0us\n",
      "0:\tlearn: 3375.8998631\ttotal: 80.4ms\tremaining: 16s\n",
      "100:\tlearn: 1405.3414524\ttotal: 6.97s\tremaining: 6.83s\n",
      "199:\tlearn: 1341.3760612\ttotal: 13.8s\tremaining: 0us\n",
      "{'depth': 8, 'iterations': 200, 'learning_rate': 0.4}\n",
      "1475.8674121295894\n"
     ]
    }
   ],
   "source": [
    "# Adjusting hyperparameters to CatBoostRegressor with GridSearchCV\n",
    "\n",
    "parameters = {'depth': [4, 6, 8],\n",
    "              'iterations': [10, 100, 200],\n",
    "              'learning_rate': [0.4, 0.2]\n",
    "              }\n",
    "\n",
    "grid_search = GridSearchCV(estimator = CatBoostRegressor(), \n",
    "                           param_grid = parameters, \n",
    "                           cv = 3, \n",
    "                           scoring = 'neg_mean_squared_error')\n",
    "\n",
    "cat_features = ['vehicle_type','gearbox','model','fuel_type','brand']\n",
    "\n",
    "start_time = time.time()\n",
    "grid_search.fit(X_train, y_train, cat_features=cat_features, verbose=100)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Best model and best parameters\n",
    "best_model_cb = grid_search.best_estimator_\n",
    "best_parameters  = grid_search.best_params_\n",
    "\n",
    "print(best_parameters)\n",
    "print(np.sqrt(-grid_search.best_score_))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, hemos obtenido nuestros mejores hiperparámetros. A continuación realizaremos el entrenamiento final de este modelo CatBoostRegressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 3375.8998631\ttotal: 81.5ms\tremaining: 16.2s\n",
      "100:\tlearn: 1405.3414524\ttotal: 6.8s\tremaining: 6.67s\n",
      "199:\tlearn: 1341.3760612\ttotal: 13.8s\tremaining: 0us\n",
      "\n",
      "RMSE= 1441.32,    time train: 14.16s,    time prediction: 0.10s\n"
     ]
    }
   ],
   "source": [
    "# Model CatBoostRegressor\n",
    "\n",
    "cat_features = ['vehicle_type','gearbox','model','fuel_type','brand']\n",
    "\n",
    "# Training\n",
    "start_time = time.time()\n",
    "best_model_cb.fit(X_train, y_train, cat_features=cat_features, verbose=100)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Predict \n",
    "start_time = time.time()\n",
    "y_predicts = best_model_cb.predict(X_valid)\n",
    "pred_time = time.time() - start_time\n",
    "\n",
    "# Metric\n",
    "rmse = mean_squared_error(y_valid, y_predicts, squared=False)\n",
    "print(\"\\nRMSE= {:.2f},    time train: {:.2f}s,    time prediction: {:.2f}s\".format(rmse, train_time, pred_time))\n",
    "\n",
    "# Save info\n",
    "summary_models.loc['CatBoostRegressor'] = {'model': best_model_cb, \n",
    "                                           'score_rmse': rmse, \n",
    "                                           'training_time_sec':train_time, \n",
    "                                           'prediction_speed_sec':pred_time}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro modelo con CatBoostRegressor tuvo un `RMSE` 1441, hasta el momento el mejor modelo. Sigamos entrenando un par de modelos más."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo XGBRegressor\n",
    "\n",
    "Continuamos probando otro modelo con XGBXGBRegressor, también usa potenciación de gradiente. Usaremos nuestras características a las cuales aplicamos la codificación One-Hot `X_train_ohe`, `X_valid_ohe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.2, 'max_depth': 8, 'n_estimators': 200}\n",
      "1478.5046497864537\n"
     ]
    }
   ],
   "source": [
    "# Adjusting hyperparameters to XGBRegressor with GridSearchCV\n",
    "\n",
    "parameters = {'max_depth': [4, 6, 8],\n",
    "              'n_estimators': [10, 100, 200],\n",
    "              'learning_rate': [0.4, 0.2]}\n",
    "\n",
    "grid_search = GridSearchCV(estimator = XGBRegressor(), \n",
    "                           param_grid = parameters, \n",
    "                           cv = 2, \n",
    "                           scoring = 'neg_mean_squared_error')\n",
    "\n",
    "grid_search.fit(X_train_ohe, y_train, verbose=100) \n",
    "\n",
    "# Best model and best parameters \n",
    "best_model_xgb = grid_search.best_estimator_\n",
    "best_parameters = grid_search.best_params_\n",
    "\n",
    "print(best_parameters)\n",
    "print(np.sqrt(-grid_search.best_score_))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, hemos obtenido nuestros mejores hiperparámetros. A continuación realizaremos el entrenamiento final de este modelo XGBRegressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE= 1431.13,    time train: 47.22s,    time prediction: 0.62s\n"
     ]
    }
   ],
   "source": [
    "#  Model XGBRegressor\n",
    "\n",
    "# Training\n",
    "start_time = time.time()\n",
    "best_model_xgb.fit(X_train_ohe, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Predict\n",
    "start_time = time.time()\n",
    "y_predict = best_model_xgb.predict(X_valid_ohe)\n",
    "pred_time = time.time() - start_time\n",
    "\n",
    "# Metric \n",
    "rmse = mean_squared_error(y_valid, y_predict, squared=False)\n",
    "print(\"\\nRMSE= {:.2f},    time train: {:.2f}s,    time prediction: {:.2f}s\".format(rmse, train_time, pred_time))\n",
    "\n",
    "# Save info\n",
    "summary_models.loc['XGBRegressor'] = {'model': best_model_xgb, \n",
    "                                      'score_rmse': rmse, \n",
    "                                      'training_time_sec':train_time, \n",
    "                                      'prediction_speed_sec':pred_time}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, hemos obtenido nuestros mejores hiperparámetros. A continuación realizaremos el entrenamiento final de este modelo CatBoostRegressor."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo LGBMRegressor\n",
    "LGBMRegressor es también un modelo con potenciación de gradiente y funciona directamente con características categóricas al igual que CatBoost."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes del entrenamiento, vamos a cambiar el tipo de datos de nuestras variables categóricas, al tipo `category`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change categorical feature  \"object\" to \"category\"\n",
    "cat_features = ['vehicle_type','gearbox','model','fuel_type','brand']\n",
    "\n",
    "for col in cat_features:\n",
    "    X_train[col] = X_train[col].astype('category')\n",
    "    X_valid[col] = X_valid[col].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.2, 'max_depth': 8, 'n_estimators': 200}\n",
      "1451.8387235779196\n"
     ]
    }
   ],
   "source": [
    "# Adjusting hyperparameters to LGBMRegressor with GridSearchCV\n",
    "\n",
    "parameters = {'max_depth':  [4, 6, 8],\n",
    "              'n_estimators': [10, 100, 200],\n",
    "              'learning_rate': [0.4, 0.2]}\n",
    "\n",
    "grid_search = GridSearchCV(estimator = LGBMRegressor(), \n",
    "                           param_grid = parameters, \n",
    "                           scoring='neg_mean_squared_error', \n",
    "                           cv=3)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Best model y parameters \n",
    "best_model_lgbm = grid_search.best_estimator_\n",
    "best_parameters = grid_search.best_params_\n",
    "\n",
    "print(best_parameters)\n",
    "print(np.sqrt(-grid_search.best_score_))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, hemos obtenido nuestros mejores hiperparámetros. A continuación realizaremos el entrenamiento final de este modelo CatBoostRegressor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE= 1579.44,    time train: 0.14s,    time prediction: 0.03s\n"
     ]
    }
   ],
   "source": [
    "#  Model LGBMRegressor\n",
    "best_model_lgbm = LGBMRegressor(max_depth = 10,\n",
    "                         n_estimators = 12,\n",
    "                         learning_rate = 0.5)\n",
    "\n",
    "# Entrenamos el modelo en el conjunto de entrenamiento\n",
    "start_time = time.time()\n",
    "best_model_lgbm.fit(X_train, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Realizamos predicciones en el conjunto de prueba\n",
    "start_time = time.time()\n",
    "y_predict = best_model_lgbm.predict(X_valid)\n",
    "pred_time = time.time() - start_time\n",
    "\n",
    "# Medimos la precisión del modelo en el conjunto de prueba utilizando el RMSE\n",
    "rmse = mean_squared_error(y_valid, y_predict, squared=False)\n",
    "\n",
    "# Print\n",
    "print(\"\\nRMSE= {:.2f},    time train: {:.2f}s,    time prediction: {:.2f}s\".format(rmse, train_time, pred_time))\n",
    "\n",
    "# Save info\n",
    "summary_models.loc['LGBMRegressor'] = {'model': best_model_lgbm, \n",
    "                                      'score_rmse': rmse, \n",
    "                                      'training_time_sec':train_time, \n",
    "                                      'prediction_speed_sec':pred_time}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis del modelo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revisemos un resumen de nuestros mejores modelos. Nos interesa saber:\n",
    "- la calidad de la predicción\n",
    "- la velocidad de la predicción\n",
    "- el tiempo requerido para el entrenamiento\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>model</th>\n",
       "      <th>score_rmse</th>\n",
       "      <th>training_time_sec</th>\n",
       "      <th>prediction_speed_sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LinearRegression</th>\n",
       "      <td>LinearRegression()</td>\n",
       "      <td>2149.977537</td>\n",
       "      <td>2.537752</td>\n",
       "      <td>0.077259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestRegressor</th>\n",
       "      <td>(DecisionTreeRegressor(max_depth=16, max_featu...</td>\n",
       "      <td>1539.974362</td>\n",
       "      <td>15.293413</td>\n",
       "      <td>0.109743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CatBoostRegressor</th>\n",
       "      <td>&lt;catboost.core.CatBoostRegressor object at 0x0...</td>\n",
       "      <td>1441.316760</td>\n",
       "      <td>14.156814</td>\n",
       "      <td>0.101458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBRegressor</th>\n",
       "      <td>XGBRegressor(base_score=None, booster=None, ca...</td>\n",
       "      <td>1431.128795</td>\n",
       "      <td>47.222970</td>\n",
       "      <td>0.617821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBMRegressor</th>\n",
       "      <td>LGBMRegressor(learning_rate=0.5, max_depth=10,...</td>\n",
       "      <td>1579.438238</td>\n",
       "      <td>0.141744</td>\n",
       "      <td>0.030173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   model   \n",
       "LinearRegression                                      LinearRegression()  \\\n",
       "RandomForestRegressor  (DecisionTreeRegressor(max_depth=16, max_featu...   \n",
       "CatBoostRegressor      <catboost.core.CatBoostRegressor object at 0x0...   \n",
       "XGBRegressor           XGBRegressor(base_score=None, booster=None, ca...   \n",
       "LGBMRegressor          LGBMRegressor(learning_rate=0.5, max_depth=10,...   \n",
       "\n",
       "                        score_rmse  training_time_sec  prediction_speed_sec  \n",
       "LinearRegression       2149.977537           2.537752              0.077259  \n",
       "RandomForestRegressor  1539.974362          15.293413              0.109743  \n",
       "CatBoostRegressor      1441.316760          14.156814              0.101458  \n",
       "XGBRegressor           1431.128795          47.222970              0.617821  \n",
       "LGBMRegressor          1579.438238           0.141744              0.030173  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos observar en resumen el entrenamiento de nuestros modelos a los cuales pudimos evaluar la calidad, tiempo de entrenamiento y tiempo de predicción. Obteniendo las siguientes conclusiones:\n",
    "\n",
    "- El modelo con mejor ajuste de acuerdo a su score RMSE fue el `XGBRegressor` con 46 sec de tiempo de entrenamiento, y 0.66 sec de tiempo de predicción. Este modelo es el más lento respecto al resto que entrenamos.\n",
    "\n",
    "- El modelo más veloz en su entrenamiento y predicción fue el `LGBMRegressor` CON 0.16 sec de tiempo de entrenamiento, y 0.04 sec de tiempo de predicción, realmente rápido, con un RMSE de 1579, tiene una diferencia entre sus socre de 148.\n",
    "\n",
    "- Nuestra prueba de cordura es aprobada, puesto que el modelo de regresión lineal sigue siendo el peor ajustado con un score de 2149 (RMSE)\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Conclusion general\n",
    " \n",
    "En este trabajo realizamos la preparación de datos realizando tareas como limpieza de registros duplicados, columnas innecesarias, tratamiento de valores ausentes de los cuales se decidió rellenar una parte de ellos y otra se eliminaron sin afectar la distribución de los datos en el resto de columnas. \n",
    "\n",
    "Luego, realizamos un análisis exploratorio de los datos, establecimos un periodo válido de los útlimos 20 años de los vehículos, se acuerdo a su año de registro (1996-2016), eliminamos valores outliers en la columna \"power\" lo cual ayudó a mejorar la distribución de los datos ene esta característica. En la columna \"precio\", se filtraron solo los registros con más de 100 euros de valor, ya que tenimos un 2% de registros cercanos o iguales a 0, lo que consideramos como ilógico.\n",
    "\n",
    "En el procesamiento de los datos, realizamos acciones para ayudar a nuestro modelos, por lo que se decidió estandarizar la escala de las columnas numéricas usando StandardScaler. Además se prepararon las caracteristicas con y sin codificación categórica usando One-Hot Encoding, esto con el fin de poder usarlas en los distintos modelos de entrenamiento de acuerdo a la necesidad.\n",
    "\n",
    "Finalmente, entrenamos varios modelos para la predicción del precio de los vehículos, de los cuales pudimos comparar varios métodos de potenciación del gradiente, bosque aleatorio, y regresión lineal, realizando un ajuste de hiperparámetros para encontrar mejores modelos.\n",
    "\n",
    "De acuerdo a la calidad y velocidad computacional, tuvimos que:\n",
    "- XGBRegressor: Modelo con excelente precisión, y el más lento.\n",
    "- CatBoostRegressor: Modelo con buena precisión y medianamente rápido.\n",
    "- LGBMRegressor: Modelo precisión aceptable y el más rápido.\n",
    "\n",
    "Podriamos recomendar a `CatBoostRegressor`, por su buena calidad y tiempo de procesamiento."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.15"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}