0. INTRODUCCION
Este trabajo esta basado en un dataset creado por Datamarket cuya muestra gratuita se puede encontrar en la plataforma kaggle, https://www.kaggle.com/datamarket/venta-de-coches.
A partir de estos datos que son una selección de anuncios de venta de coches de segunda mando provenientes de las principales plataformas de internet, y en los cuales vienen coches de todas las marcas y modelos, incluidas diversas variables de los mismos con sus diversos precios de venta.
Hemos limpiado y transformado los mismos, para posteriormente seleccionar de este gran conjunto de datos que aglutinan unas 50000 observaciones, un subconjunto nuevo de datos de unas 12000 observaciones en el que hemos pretendido hacer referencia solo a los coches que se encuentren en un rango de precios entre 6000 y 12000 euros para a posteriori hacer un estudio y análisis exploratorio con estos datos y finalmente hacer un modelo con los mismos para proceder a predecir el precio de venta de los vehículos.
Estos datos pertenecen a una muestra gratuita sin suscripción, algunos de los datos de las columnas están encriptados para cumplir con la GDPR, aunque no nos ha hecho falta utilizarlos para este trabajo.
VARIABLES QUE CONTIENE EL DATASET
color: Color del vehículo.
company: Web de donde se ha realizado la extracción del anuncio
(encriptado).Estará disponible tras la suscripción al dataset.
country: País donde se vende el vehículo.
dealer: Vendedor del vehículo. En el caso de vendedores particulares (no concesionarios), esta información está encriptada en el dataset para cumplir con la GDPR.
fuel: Tipo de combustible del vehículo (diésel, gasolina, eléctrico,
híbrido).
insert_date: Fecha de extracción de la información.
is_professional: Indica si el vendedor es profesional(un concesionario).
kms: Kilometraje del vehículo.
make: Marca del coche.
model: Modelo del vehículo.
photos: Número de fotografías del vehículo disponibles en el anuncio.
power: Potencia del vehículo.
price: Precio de venta del vehículo.
price_financed: Precio si el coche está financiado.
province: Provincia donde se vende el vehículo.
publish_date: Fecha de publicación del anuncio.
shift: Tipo de cambio (Automático/Manual).
url: Url del coche de segunda mano en venta.
version: Versión del vehículo.
year: Año de fabricación del vehículo.
EXPOSICION DEL ESTUDIO
Se realizan las siguientes tareas:
-CARGA,LIMPIEZA, TRANSFORMACION Y FILTRADO DE DATOS A PARTIR DEL CONJUNTO ORIGINAL.
-ANALISIS EXPLORATORIO DE LOS DATOS A PARTIR DEL SUBCONJUNTO DE DATOS.
-MODELO PREDICCION PRECIO DE VENTA CREADO A PARTIR DEL SUBCONJUNTO DE DATOS.
1. CARGA,LIMPIEZA,TRANSFORMACION Y FILTRADO DE DATOS A PARTIR DEL CONJUNTO ORIGINAL
library(tidyverse)
library(plotly)
library(scales)
library(VIM)
library(corrplot)
library(treemap)
library(devtools)
library(d3treeR)#install_github("timelyportfolio/d3treeR")/use devtools.
library(caret)
library(plotrix)
library(knitr)
library(kableExtra)dat <- read.csv("coches-de-segunda-mano-sample.csv")
str(dat)## 'data.frame': 50000 obs. of 21 variables:
## $ url : chr "e158ae0ca53119ca199c28c36b5c2fcd" "ff267ebb7e700246f47f84f3db660b4b" "de4b02db28ea7786c622b969be10c7c7" "0449972a4d07594acf92e9a7dd28b39c" ...
## $ company : chr "9881bcdd5a0ad4733037b3fb25e69c3a" "9881bcdd5a0ad4733037b3fb25e69c3a" "9881bcdd5a0ad4733037b3fb25e69c3a" "9881bcdd5a0ad4733037b3fb25e69c3a" ...
## $ make : chr "SEAT" "CITROEN" "FORD" "VOLKSWAGEN" ...
## $ model : chr "Toledo" "C1" "Transit Connect" "Caravelle" ...
## $ version : chr "SEAT Toledo 4p." "CITROEN C1 PureTech 60KW 82CV Feel 5p." "FORD Transit Connect Van 1.5 TDCi 100cv Ambiente 200 L1" "VOLKSWAGEN Caravelle Largo 2.0 TDI 140 Comfortlin Edition BMT" ...
## $ price : int 950 6200 7851 19426 22850 11490 28500 8200 12100 6300 ...
## $ price_financed : int NA NA 7024 NA 22800 10490 26220 NA NA NA ...
## $ fuel : chr "Diésel" "Gasolina" "Diésel" "Diésel" ...
## $ year : int 2000 2017 2016 2014 2017 2016 2017 2012 2018 2016 ...
## $ kms : int 227000 50071 103000 120000 107000 78665 36238 203000 45000 77000 ...
## $ power : int NA 82 100 140 130 130 150 150 110 80 ...
## $ doors : int 4 5 4 4 2 5 5 5 5 5 ...
## $ shift : chr "Manual" "Manual" "Manual" "Manual" ...
## $ color : chr "Verde" "Blanco" "Blanco" "Blanco" ...
## $ photos : int 5 6 10 9 4 32 47 15 6 6 ...
## $ is_professional: chr "False" "True" "True" "True" ...
## $ dealer : chr "0f4bb8455d27349b8273109b66a847f3" "Autos Raymara" "Auto 96" "Inniauto" ...
## $ province : chr "Navarra" "Tenerife" "Barcelona" "Navarra" ...
## $ country : chr "Spain" "Spain" "Spain" "Spain" ...
## $ publish_date : chr "2020-12-18 10:47:13" "2021-01-02 11:25:40" "2020-12-16 10:51:45" "2020-11-25 11:09:14" ...
## $ insert_date : chr "2021-01-15 00:00:00" "2021-01-15 00:00:00" "2021-01-15 00:00:00" "2021-01-15 00:00:00" ...Eliminamos variables que no vamos a considerar en nuestro estudio por no ser significativas:
dat$url <- NULL
dat$company <- NULL
dat$photos <- NULL
dat$dealer <- NULL
dat$insert_date <- NULL
dat$country <- NULL
dat$version <- NULL
dat$publish_date <- NULL
dat$color <- NULL
glimpse(dat)## Rows: 50,000
## Columns: 12
## $ make <chr> "SEAT", "CITROEN", "FORD", "VOLKSWAGEN", "FORD", "PEUG~
## $ model <chr> "Toledo", "C1", "Transit Connect", "Caravelle", "Trans~
## $ price <int> 950, 6200, 7851, 19426, 22850, 11490, 28500, 8200, 121~
## $ price_financed <int> NA, NA, 7024, NA, 22800, 10490, 26220, NA, NA, NA, 129~
## $ fuel <chr> "Diésel", "Gasolina", "Diésel", "Diésel", "Diésel"~
## $ year <int> 2000, 2017, 2016, 2014, 2017, 2016, 2017, 2012, 2018, ~
## $ kms <int> 227000, 50071, 103000, 120000, 107000, 78665, 36238, 2~
## $ power <int> NA, 82, 100, 140, 130, 130, 150, 150, 110, 80, 100, NA~
## $ doors <int> 4, 5, 4, 4, 2, 5, 5, 5, 5, 5, 5, 3, 3, 2, 5, 5, 5, 4, ~
## $ shift <chr> "Manual", "Manual", "Manual", "Manual", "Manual", "Man~
## $ is_professional <chr> "False", "True", "True", "True", "True", "True", "True~
## $ province <chr> "Navarra", "Tenerife", "Barcelona", "Navarra", "Sevill~kable(head(dat,6), booktabs = T) %>%
kable_styling(font_size=11)| make | model | price | price_financed | fuel | year | kms | power | doors | shift | is_professional | province |
|---|---|---|---|---|---|---|---|---|---|---|---|
| SEAT | Toledo | 950 | NA | Diésel | 2000 | 227000 | NA | 4 | Manual | False | Navarra |
| CITROEN | C1 | 6200 | NA | Gasolina | 2017 | 50071 | 82 | 5 | Manual | True | Tenerife |
| FORD | Transit Connect | 7851 | 7024 | Diésel | 2016 | 103000 | 100 | 4 | Manual | True | Barcelona |
| VOLKSWAGEN | Caravelle | 19426 | NA | Diésel | 2014 | 120000 | 140 | 4 | Manual | True | Navarra |
| FORD | Transit | 22850 | 22800 | Diésel | 2017 | 107000 | 130 | 2 | Manual | True | Sevilla |
| PEUGEOT | 3008 | 11490 | 10490 | Gasolina | 2016 | 78665 | 130 | 5 | Manual | True | Madrid |
Ahora en vista de su visualizacion y estructura trataremos de limpiar los datos con caracteres especiales.
En lugar de usar expresiones regulares para eliminar esos caracteres especiales, simplemente los vamos a convertir a ASCII, lo que eliminará los acentos, pero conservará las letras.
dat$fuel <- iconv(dat$fuel, from = 'UTF-8', to = 'ASCII//TRANSLIT')
dat$model <-iconv(dat$model, from = 'UTF-8', to = 'ASCII//TRANSLIT')
dat$shift <- iconv(dat$shift, from = 'UTF-8', to = 'ASCII//TRANSLIT')
dat$province <- iconv(dat$province, from = 'UTF-8', to = 'ASCII//TRANSLIT')
kable(head(dat,6), booktabs = T) %>%
kable_styling(font_size=11)| make | model | price | price_financed | fuel | year | kms | power | doors | shift | is_professional | province |
|---|---|---|---|---|---|---|---|---|---|---|---|
| SEAT | Toledo | 950 | NA | Diesel | 2000 | 227000 | NA | 4 | Manual | False | Navarra |
| CITROEN | C1 | 6200 | NA | Gasolina | 2017 | 50071 | 82 | 5 | Manual | True | Tenerife |
| FORD | Transit Connect | 7851 | 7024 | Diesel | 2016 | 103000 | 100 | 4 | Manual | True | Barcelona |
| VOLKSWAGEN | Caravelle | 19426 | NA | Diesel | 2014 | 120000 | 140 | 4 | Manual | True | Navarra |
| FORD | Transit | 22850 | 22800 | Diesel | 2017 | 107000 | 130 | 2 | Manual | True | Sevilla |
| PEUGEOT | 3008 | 11490 | 10490 | Gasolina | 2016 | 78665 | 130 | 5 | Manual | True | Madrid |
En funcion de su estructura tambien vemos aparte de los NA latentes estructuras de casillas vacias en muchas variables de esta forma ““.
Asi pues vamos a convertir a NA esos valores.
dat$make[dat$make==""] <- NA
dat$model[dat$model==""] <- NA
dat$fuel[dat$fuel==""] <- NA
dat$shift[dat$shift==""] <- NA
dat$province[dat$province==""] <- NAVamos ahora a ver los NAs encontrados en cada variable.
colSums(is.na(dat))## make model price price_financed fuel
## 2 5 0 26437 46
## year kms power doors shift
## 2 0 8528 0 111
## is_professional province
## 0 6Como vemos hay muchas variables con NA, pero salvando las variables “price_financed” y “power” con mas NAS, las demas tienen muy pocas variables cada una de las mismas con estos valores con respecto al dataset, con lo cual eliminaremos estas pocas filas que representan estas variables de los datos.
dat <- dat[!is.na(dat$make),]
dat <- dat[!is.na(dat$model),]
dat <- dat[!is.na(dat$fuel),]
dat <- dat[!is.na(dat$year),]
dat <- dat[!is.na(dat$shift),]
dat <- dat[!is.na(dat$province),]Ahora como vemos a posteriori solo queda resolver el problema de las NAs de “price_financed” y “power”.
colSums(is.na(dat))## make model price price_financed fuel
## 0 0 0 26318 0
## year kms power doors shift
## 0 0 8480 0 0
## is_professional province
## 0 0Ahora vamos a añadir una variable que se llama “best_price” teniendo en cuenta para este caso que el precio financiado es mejor que el precio de venta, y lo vamos a conseguir fusionando las columnas “price” y “price_financed” de forma que los valores NA de “price_financed”, que se considera que no tiene financiancion al no tener precio, se sustituyan por los valores de “price” y solo estos dejando los demas valores de la variable “price_financed tal cual.
datm <- dat %>%
mutate(best_price = ifelse(is.na(price_financed), price, price_financed))A partir de aqui vamos a eliminar las variables “price” y “price_financed” dejando como nuestra variable objetivo “best_price”.
datm$price <- NULL
datm$price_financed <- NULL
kable(head(datm,6), booktabs = T) %>%
kable_styling(font_size=11)| make | model | fuel | year | kms | power | doors | shift | is_professional | province | best_price |
|---|---|---|---|---|---|---|---|---|---|---|
| SEAT | Toledo | Diesel | 2000 | 227000 | NA | 4 | Manual | False | Navarra | 950 |
| CITROEN | C1 | Gasolina | 2017 | 50071 | 82 | 5 | Manual | True | Tenerife | 6200 |
| FORD | Transit Connect | Diesel | 2016 | 103000 | 100 | 4 | Manual | True | Barcelona | 7024 |
| VOLKSWAGEN | Caravelle | Diesel | 2014 | 120000 | 140 | 4 | Manual | True | Navarra | 19426 |
| FORD | Transit | Diesel | 2017 | 107000 | 130 | 2 | Manual | True | Sevilla | 22800 |
| PEUGEOT | 3008 | Gasolina | 2016 | 78665 | 130 | 5 | Manual | True | Madrid | 10490 |
Ahora vamos a realizar la imputacion de los valores de los NA de la variable “power” que nos queda para que nos halle los valores faltantes en dicha variable.
Escogemos el sistema de imputacion KNN de la libreria VIM (Visualizacion e imputacion de valores perdidos), para resolverlo.
dat2<-kNN(datm,variable ="power",k=sqrt(nrow(dat)))
dat2$power_imp <- NULLComprobamos ahora si quedan rastro de los NAs en nuestros datos.
colSums(is.na(dat2))## make model fuel year kms
## 0 0 0 0 0
## power doors shift is_professional province
## 0 0 0 0 0
## best_price
## 0kable(head(dat2,6), booktabs = T) %>%
kable_styling(font_size=11)| make | model | fuel | year | kms | power | doors | shift | is_professional | province | best_price |
|---|---|---|---|---|---|---|---|---|---|---|
| SEAT | Toledo | Diesel | 2000 | 227000 | 120 | 4 | Manual | False | Navarra | 950 |
| CITROEN | C1 | Gasolina | 2017 | 50071 | 82 | 5 | Manual | True | Tenerife | 6200 |
| FORD | Transit Connect | Diesel | 2016 | 103000 | 100 | 4 | Manual | True | Barcelona | 7024 |
| VOLKSWAGEN | Caravelle | Diesel | 2014 | 120000 | 140 | 4 | Manual | True | Navarra | 19426 |
| FORD | Transit | Diesel | 2017 | 107000 | 130 | 2 | Manual | True | Sevilla | 22800 |
| PEUGEOT | 3008 | Gasolina | 2016 | 78665 | 130 | 5 | Manual | True | Madrid | 10490 |
y vemos que ya no tenemos NAs en nuestras variables.
Vamos a reducir nuestro dataset con el objetivo de determinar solamente los coches que esten en una franja de precios entre 6000 y 12000 euros.
datfilter <- dat2%>%filter(best_price<=12000 & best_price>6000)Ahora vamos a considerar como factores las variables que correspondan en nuestros datos.
datfilter$make <- as.factor(datfilter$make)
datfilter$model <- as.factor(datfilter$model)
datfilter$fuel <- as.factor(datfilter$fuel)
datfilter$shift <- as.factor(datfilter$shift)
datfilter$is_professional <- as.factor(datfilter$is_professional)
datfilter$province <- as.factor(datfilter$province)%>%fct_recode("A Coruña"="A Coruna")
str(datfilter)## 'data.frame': 12800 obs. of 11 variables:
## $ make : Factor w/ 53 levels "ABARTH","ALFA ROMEO",..: 9 14 35 15 33 30 13 13 14 9 ...
## $ model : Factor w/ 483 levels "100","106","108",..: 82 438 19 119 323 123 331 174 80 83 ...
## $ fuel : Factor w/ 7 levels "Diesel","Electrico",..: 5 1 5 1 5 5 1 1 5 5 ...
## $ year : int 2017 2016 2016 2012 2016 2001 2017 2010 2018 2019 ...
## $ kms : int 50071 103000 78665 203000 77000 169450 101623 135000 53000 13000 ...
## $ power : int 82 100 130 150 80 306 95 120 125 68 ...
## $ doors : int 5 4 5 5 5 2 5 5 5 5 ...
## $ shift : Factor w/ 2 levels "Automatico","Manual": 2 2 2 2 2 1 2 2 2 2 ...
## $ is_professional: Factor w/ 2 levels "False","True": 2 2 2 1 1 2 2 1 2 2 ...
## $ province : Factor w/ 52 levels "A Coruña","Alava",..: 45 10 32 10 10 33 32 36 4 21 ...
## $ best_price : int 6200 7024 10490 8200 6300 6900 9500 8000 11380 10990 ...Vamos a escoger las variables numericas para hacer la matriz de correlacion y comprobar que las variables no esten muy correlacinadas entre si.
dat3 <- datfilter%>%select(c("year", "kms", "power", "doors"))dat.cor <- cor(dat3, method = "pearson")
corrplot(dat.cor, method = "shade",
shade.col = NA, tl.col = "black",
tl.srt = 45,
addCoef.col = "black", addcolorlabel = "no",
order = "AOE")
Como se puede ver en la matriz las variables independientes entre si no exceden el 0.7 de correlacion por tanto no se muestran problemas en este sentido.
Vamos tambien a cambiar a factor la variable doors y tambien el nombre de sus niveles:
datfilter$doors <- as.factor(datfilter$doors)
levels(datfilter$doors) <- c("2p", "3p", "4p", "5p")
str(datfilter)## 'data.frame': 12800 obs. of 11 variables:
## $ make : Factor w/ 53 levels "ABARTH","ALFA ROMEO",..: 9 14 35 15 33 30 13 13 14 9 ...
## $ model : Factor w/ 483 levels "100","106","108",..: 82 438 19 119 323 123 331 174 80 83 ...
## $ fuel : Factor w/ 7 levels "Diesel","Electrico",..: 5 1 5 1 5 5 1 1 5 5 ...
## $ year : int 2017 2016 2016 2012 2016 2001 2017 2010 2018 2019 ...
## $ kms : int 50071 103000 78665 203000 77000 169450 101623 135000 53000 13000 ...
## $ power : int 82 100 130 150 80 306 95 120 125 68 ...
## $ doors : Factor w/ 4 levels "2p","3p","4p",..: 4 3 4 4 4 1 4 4 4 4 ...
## $ shift : Factor w/ 2 levels "Automatico","Manual": 2 2 2 2 2 1 2 2 2 2 ...
## $ is_professional: Factor w/ 2 levels "False","True": 2 2 2 1 1 2 2 1 2 2 ...
## $ province : Factor w/ 52 levels "A Coruña","Alava",..: 45 10 32 10 10 33 32 36 4 21 ...
## $ best_price : int 6200 7024 10490 8200 6300 6900 9500 8000 11380 10990 ...A continuacion para el desarrollo de nuestros analisis vamos a crear una nueva variable que junte, make con model.
datc <- datfilter%>%mutate(unite(datfilter,make_model,c(1:2),sep="_", remove=F))
datc$make_model <- as.factor(datc$make_model)2. ANALISIS EXPLORATORIO DE LOS DATOS A PARTIR DEL SUBCONJUNTO DE DATOS
Mostramos la tabla con la cantidad de anuncios ofertados en funcion de la marca de coches dentro del rango solicitado y visualizamos los 10 ultimos(mas ofertados).
table1 <- kable(sort(table(datc$make)))
tail(table1,10)## [1] "<table>\n <thead>\n <tr>\n <th style=\"text-align:left;\"> Var1 </th>\n <th style=\"text-align:right;\"> Freq </th>\n </tr>\n </thead>\n<tbody>\n <tr>\n <td style=\"text-align:left;\"> AUSTIN </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ISUZU </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LADA </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LDV </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PIAGGIO </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PONTIAC </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TATA </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> UMM </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> INFINITI </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SANTANA </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAHINDRA </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ROVER </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CADILLAC </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DODGE </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LANCIA </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> IVECO </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SAAB </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ABARTH </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUBARU </td>\n <td style=\"text-align:right;\"> 16 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DS </td>\n <td style=\"text-align:right;\"> 18 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHRYSLER </td>\n <td style=\"text-align:right;\"> 24 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PORSCHE </td>\n <td style=\"text-align:right;\"> 25 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JAGUAR </td>\n <td style=\"text-align:right;\"> 40 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUZUKI </td>\n <td style=\"text-align:right;\"> 40 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JEEP </td>\n <td style=\"text-align:right;\"> 43 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS </td>\n <td style=\"text-align:right;\"> 48 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LAND-ROVER </td>\n <td style=\"text-align:right;\"> 56 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ALFA ROMEO </td>\n <td style=\"text-align:right;\"> 62 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET </td>\n <td style=\"text-align:right;\"> 62 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SMART </td>\n <td style=\"text-align:right;\"> 73 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG </td>\n <td style=\"text-align:right;\"> 75 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA </td>\n <td style=\"text-align:right;\"> 110 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA </td>\n <td style=\"text-align:right;\"> 111 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO </td>\n <td style=\"text-align:right;\"> 117 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI </td>\n <td style=\"text-align:right;\"> 122 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DACIA </td>\n <td style=\"text-align:right;\"> 178 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA </td>\n <td style=\"text-align:right;\"> 187 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MINI </td>\n <td style=\"text-align:right;\"> 232 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA </td>\n <td style=\"text-align:right;\"> 281 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI </td>\n <td style=\"text-align:right;\"> 377 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN </td>\n <td style=\"text-align:right;\"> 511 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA </td>\n <td style=\"text-align:right;\"> 531 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ </td>\n <td style=\"text-align:right;\"> 644 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT </td>\n <td style=\"text-align:right;\"> 664 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI </td>\n <td style=\"text-align:right;\"> 679 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT </td>\n <td style=\"text-align:right;\"> 697 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN </td>\n <td style=\"text-align:right;\"> 835 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW </td>\n <td style=\"text-align:right;\"> 874 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT </td>\n <td style=\"text-align:right;\"> 918 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL </td>\n <td style=\"text-align:right;\"> 927 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT </td>\n <td style=\"text-align:right;\"> 937 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD </td>\n <td style=\"text-align:right;\"> 973 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN </td>\n <td style=\"text-align:right;\"> 1235 </td>\n </tr>\n</tbody>\n</table>"Asimismo tambien mostramos la tabla con la cantidad de anuncios ofertados por modelo de coche tambien dentro de este rango y visualizamos los 10 ultimos(mas ofertados).
table2 <- kable(sort(table(datc$make_model)))
tail(table2,10)## [1] "<table>\n <thead>\n <tr>\n <th style=\"text-align:left;\"> Var1 </th>\n <th style=\"text-align:right;\"> Freq </th>\n </tr>\n </thead>\n<tbody>\n <tr>\n <td style=\"text-align:left;\"> ABARTH_Punto EVO </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ALFA ROMEO_GT </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_Q3 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_S6 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_S8 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUSTIN_Mini </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Serie 2 Gran Tourer </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CADILLAC_CTS </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CADILLAC_El Dorado </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET_Aveo </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET_Corvette </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET_Malibu </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHRYSLER_Sebring 200C </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_2CV </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C3 Pluriel </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C4 Spacetourer </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_Saxo </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Barchetta </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Coupe </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Courier </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Mustang </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_Prelude </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_H-1 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_H-1 Travel </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_IONIQ </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_Sonata </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> INFINITI_FX </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> INFINITI_M </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ISUZU_D-Max </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JAGUAR_Serie XK </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JEEP_Renegade </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_k2500 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LADA_Niva </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LANCIA_Thema </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LANCIA_Voyager </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LDV_Maxus </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_GS </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_IS250 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_IS300 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_RX </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_RX300 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_SC430 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAHINDRA_Quanto </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_180D </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase V </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Transporter </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Eclipse </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Space Gear </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_CAMION </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_CUBE </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_e-NV200 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Interstar </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Patrol </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Cabrio </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Corsa-e </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Manta </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Vectra </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_106 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_205 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_206 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_207 + </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_208 XAD </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_309 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PIAGGIO_Porter </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PONTIAC_Firebird </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PORSCHE_928 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Clio 4 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Maxity </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_R11 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Talisman </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Wind </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SANTANA_350 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SANTANA_Anibal </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Malaga </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG_Korando KJ </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUBARU_XV </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUZUKI_Samurai </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUZUKI_Vitara </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TATA_Aria </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Corolla </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Corolla Sedan </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Dyna </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_iQ </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Land Cruiser 100 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> UMM_100 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Lupo </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_960 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_S80 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_V70 </td>\n <td style=\"text-align:right;\"> 1 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ABARTH_500C </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_A4 Allroad Quattro </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_A6 allroad quattro </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_Cabriolet </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Compact </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Serie 2 Active Tourer </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET_Camaro </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHRYSLER_Sebring </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C8 </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DODGE_Nitro </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Bravo </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Capri </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Ranger </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Sierra </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Transit Custom </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_FR-V </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_Terracan </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JAGUAR_X-Type </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JEEP_Patriot </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Carnival </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Ceed Tourer </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LANCIA_Ypsilon </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_GS450h </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_RX350 </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAHINDRA_KUV100 </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_CX-9 </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_280 </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_300 </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase GL </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase SL R129 </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_EVALIA </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Pick-up </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Ampera </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_407 SW </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_607 </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Grand Espace </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Grand Kangoo Combi </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Kangoo </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Kangoo Z.E. </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Altea Freetrack </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA_Scout </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SMART_roadster </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_4Runner </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Celica </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Hilux </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Proace </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Urban Cruiser </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_LT </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_up! </td>\n <td style=\"text-align:right;\"> 2 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ALFA ROMEO_159 </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_S4 </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DODGE_Avenger </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DS_DS 5 </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Connect </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_CR-Z </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_Insight </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_Jazz </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_TUCSON </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JEEP_Commander </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_190 </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase G </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MINI_Paceman </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Montero Sport </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_350Z </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Kadett </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Karl </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Movano </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_RCZ </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PORSCHE_944 </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PORSCHE_Boxster </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Latitude </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Mascott </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ROVER_Mini </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG_Tivoli </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUBARU_Forester </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUZUKI_Swift </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_California </td>\n <td style=\"text-align:right;\"> 3 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_Coupe </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_Q5 </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CADILLAC_SRX </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHRYSLER_Crossfire </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C-Zero </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C6 </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_DS5 </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_CRX </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_ix55 </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_Kona </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Stonic </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LAND-ROVER_Defender </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_CX-3 </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase GLK </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Grandis </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Cabstar </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_GTC </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_R5 </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG_Rexton </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUBARU_Impreza </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUBARU_Outback </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUBARU_Tribeca </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Crafter </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_S40 </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_V40 Cross Country </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_XC70 </td>\n <td style=\"text-align:right;\"> 4 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ALFA ROMEO_Brera </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET_Trax </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DODGE_Journey </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Ducato </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_KA </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Tourneo Connect </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JEEP_Cherokee </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Montero iO </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Murano </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Primastar </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Terrano </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_407 </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_807 </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_ion </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Clio III </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUZUKI_SX4 S-Cross </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Land Cruiser </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_V50 </td>\n <td style=\"text-align:right;\"> 5 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C-Crosser </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DS_DS 3 </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Doblo Panorama </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Soul </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Venga </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LANCIA_Delta </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_GS300 </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_IS220d </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_CX-5 </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_Mazda5 </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_200 </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MINI_CLUBMAN </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Pathfinder </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Patrol GR </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_Bipper </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Kadjar </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Alhambra </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA_Citigo </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA_Rapid </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA_Yeti </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Phaeton </td>\n <td style=\"text-align:right;\"> 6 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_Allroad Quattro </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Z3 </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHRYSLER_300C </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_i20 Active </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_Veloster </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JAGUAR_S-Type </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Optima </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Pro Ceed </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_RX400h </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_RX-8 </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase SL </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_L200 </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Navara </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_4007 </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Arona </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG_Actyon </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Corolla Verso </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_CC </td>\n <td style=\"text-align:right;\"> 7 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_S3 </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET_Orlando </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Freemont </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_Elantra </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JAGUAR_XJ </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JEEP_Compass </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_ceed Sportswagon </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Terrano II </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Mii </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG_Rexton II </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Caravelle </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_C70 </td>\n <td style=\"text-align:right;\"> 8 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ABARTH_500 </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DS_DS 4 </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_Santa Fe </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JEEP_Wrangler </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_CT </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase CL </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Fluence </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG_Kyron </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUZUKI_SX4 </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Land Cruiser 90 </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Golf Plus </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_New Beetle </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Sharan </td>\n <td style=\"text-align:right;\"> 9 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Serie 6 </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHRYSLER_Grand Voyager </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Galaxy </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JAGUAR_Serie XJ </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LAND-ROVER_Discovery </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LEXUS_IS </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Twingo </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUZUKI_Jimny </td>\n <td style=\"text-align:right;\"> 10 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> IVECO_Daily </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LAND-ROVER_Range Rover Sport </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_MX-5 </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase CLC </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Koleos </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SAAB_9-3 </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SUZUKI_Grand Vitara </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Beetle </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Jetta </td>\n <td style=\"text-align:right;\"> 11 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Z4 </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C4 Aircross </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_DS3 </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JAGUAR_XF </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Viano </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_C30 </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_V60 </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_XC60 </td>\n <td style=\"text-align:right;\"> 12 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Serie 7 </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_Nemo </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DACIA_Logan </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Sorento </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LAND-ROVER_Range Rover </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_Mazda2 </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_Boxer </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Toledo </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Multivan </td>\n <td style=\"text-align:right;\"> 13 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_A8 </td>\n <td style=\"text-align:right;\"> 14 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C3 Picasso </td>\n <td style=\"text-align:right;\"> 14 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_DS4 </td>\n <td style=\"text-align:right;\"> 14 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_Jumpy </td>\n <td style=\"text-align:right;\"> 14 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_220 </td>\n <td style=\"text-align:right;\"> 14 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ALFA ROMEO_MiTo </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Punto </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_B-MAX </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> JEEP_Grand Cherokee </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Lancer </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_X-TRAIL </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Zafira </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_207 </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Prius </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_S60 </td>\n <td style=\"text-align:right;\"> 15 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_Mazda3 </td>\n <td style=\"text-align:right;\"> 16 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Outlander </td>\n <td style=\"text-align:right;\"> 16 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_LEAF </td>\n <td style=\"text-align:right;\"> 16 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_Expert </td>\n <td style=\"text-align:right;\"> 16 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Master </td>\n <td style=\"text-align:right;\"> 16 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Eos </td>\n <td style=\"text-align:right;\"> 16 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Scudo </td>\n <td style=\"text-align:right;\"> 17 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_ix20 </td>\n <td style=\"text-align:right;\"> 17 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA_Spaceback </td>\n <td style=\"text-align:right;\"> 17 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_V40 </td>\n <td style=\"text-align:right;\"> 17 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_Jumper </td>\n <td style=\"text-align:right;\"> 18 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Qubo </td>\n <td style=\"text-align:right;\"> 18 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> LAND-ROVER_Freelander </td>\n <td style=\"text-align:right;\"> 18 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Sprinter </td>\n <td style=\"text-align:right;\"> 18 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Vivaro </td>\n <td style=\"text-align:right;\"> 18 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PORSCHE_Cayenne </td>\n <td style=\"text-align:right;\"> 18 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Golf Sportsvan </td>\n <td style=\"text-align:right;\"> 18 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Space Star </td>\n <td style=\"text-align:right;\"> 19 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_NOTE </td>\n <td style=\"text-align:right;\"> 19 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA_Superb </td>\n <td style=\"text-align:right;\"> 19 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG_Korando </td>\n <td style=\"text-align:right;\"> 19 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET_Cruze </td>\n <td style=\"text-align:right;\"> 20 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_NV200 </td>\n <td style=\"text-align:right;\"> 20 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C3 Aircross </td>\n <td style=\"text-align:right;\"> 21 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_500X </td>\n <td style=\"text-align:right;\"> 21 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Doblo Cargo </td>\n <td style=\"text-align:right;\"> 21 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase R </td>\n <td style=\"text-align:right;\"> 21 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLVO_XC90 </td>\n <td style=\"text-align:right;\"> 21 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Fiorino </td>\n <td style=\"text-align:right;\"> 22 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_CX-7 </td>\n <td style=\"text-align:right;\"> 22 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_Montero </td>\n <td style=\"text-align:right;\"> 22 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Trafic </td>\n <td style=\"text-align:right;\"> 22 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_S-MAX </td>\n <td style=\"text-align:right;\"> 23 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SMART_forfour </td>\n <td style=\"text-align:right;\"> 23 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CHEVROLET_Captiva </td>\n <td style=\"text-align:right;\"> 24 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Tourneo Courier </td>\n <td style=\"text-align:right;\"> 24 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Transit </td>\n <td style=\"text-align:right;\"> 24 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MAZDA_Mazda6 </td>\n <td style=\"text-align:right;\"> 24 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_QASHQAI+2 </td>\n <td style=\"text-align:right;\"> 24 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Zoe </td>\n <td style=\"text-align:right;\"> 24 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SSANGYONG_Rodius </td>\n <td style=\"text-align:right;\"> 24 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DACIA_Lodgy </td>\n <td style=\"text-align:right;\"> 25 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_Accord </td>\n <td style=\"text-align:right;\"> 26 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase S </td>\n <td style=\"text-align:right;\"> 26 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_PULSAR </td>\n <td style=\"text-align:right;\"> 26 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Meriva </td>\n <td style=\"text-align:right;\"> 26 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_ALTEA </td>\n <td style=\"text-align:right;\"> 26 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase A </td>\n <td style=\"text-align:right;\"> 28 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Antara </td>\n <td style=\"text-align:right;\"> 28 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Mokka X </td>\n <td style=\"text-align:right;\"> 28 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Zafira Tourer </td>\n <td style=\"text-align:right;\"> 28 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_EcoSport </td>\n <td style=\"text-align:right;\"> 29 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase SLK </td>\n <td style=\"text-align:right;\"> 29 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MITSUBISHI_ASX </td>\n <td style=\"text-align:right;\"> 29 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Grand C-Max </td>\n <td style=\"text-align:right;\"> 30 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Carens </td>\n <td style=\"text-align:right;\"> 30 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_108 </td>\n <td style=\"text-align:right;\"> 30 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Passat CC </td>\n <td style=\"text-align:right;\"> 30 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_Q7 </td>\n <td style=\"text-align:right;\"> 31 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_X1 </td>\n <td style=\"text-align:right;\"> 31 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MINI_Countryman </td>\n <td style=\"text-align:right;\"> 31 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_CR-V </td>\n <td style=\"text-align:right;\"> 32 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Citan </td>\n <td style=\"text-align:right;\"> 32 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Ka+ </td>\n <td style=\"text-align:right;\"> 33 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Transit Connect </td>\n <td style=\"text-align:right;\"> 33 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase CLS </td>\n <td style=\"text-align:right;\"> 33 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DACIA_Dokker </td>\n <td style=\"text-align:right;\"> 34 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Altea XL </td>\n <td style=\"text-align:right;\"> 34 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_TT </td>\n <td style=\"text-align:right;\"> 35 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DACIA_Duster </td>\n <td style=\"text-align:right;\"> 35 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Combo </td>\n <td style=\"text-align:right;\"> 35 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Mokka </td>\n <td style=\"text-align:right;\"> 35 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Exeo </td>\n <td style=\"text-align:right;\"> 35 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Tiguan </td>\n <td style=\"text-align:right;\"> 35 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Doblo </td>\n <td style=\"text-align:right;\"> 36 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HONDA_Civic </td>\n <td style=\"text-align:right;\"> 36 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase M </td>\n <td style=\"text-align:right;\"> 36 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Transit Courier </td>\n <td style=\"text-align:right;\"> 37 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Transporter </td>\n <td style=\"text-align:right;\"> 37 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> ALFA ROMEO_Giulietta </td>\n <td style=\"text-align:right;\"> 38 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Kangoo Furgon </td>\n <td style=\"text-align:right;\"> 38 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Laguna </td>\n <td style=\"text-align:right;\"> 38 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase CLK </td>\n <td style=\"text-align:right;\"> 40 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Scenic </td>\n <td style=\"text-align:right;\"> 41 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Rav4 </td>\n <td style=\"text-align:right;\"> 41 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C5 </td>\n <td style=\"text-align:right;\"> 42 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_500L </td>\n <td style=\"text-align:right;\"> 42 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Verso </td>\n <td style=\"text-align:right;\"> 42 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Rio </td>\n <td style=\"text-align:right;\"> 43 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Grand Scenic </td>\n <td style=\"text-align:right;\"> 43 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Avensis </td>\n <td style=\"text-align:right;\"> 43 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Sportage </td>\n <td style=\"text-align:right;\"> 44 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_X5 </td>\n <td style=\"text-align:right;\"> 45 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_500C </td>\n <td style=\"text-align:right;\"> 45 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Vito </td>\n <td style=\"text-align:right;\"> 45 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Touareg </td>\n <td style=\"text-align:right;\"> 45 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C1 </td>\n <td style=\"text-align:right;\"> 46 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Panda </td>\n <td style=\"text-align:right;\"> 47 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Adam </td>\n <td style=\"text-align:right;\"> 47 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_A5 </td>\n <td style=\"text-align:right;\"> 48 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SMART_fortwo </td>\n <td style=\"text-align:right;\"> 48 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase B </td>\n <td style=\"text-align:right;\"> 50 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_i40 </td>\n <td style=\"text-align:right;\"> 51 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_ix35 </td>\n <td style=\"text-align:right;\"> 51 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_i10 </td>\n <td style=\"text-align:right;\"> 53 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_ceed </td>\n <td style=\"text-align:right;\"> 53 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_X3 </td>\n <td style=\"text-align:right;\"> 54 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA_Octavia </td>\n <td style=\"text-align:right;\"> 54 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> KIA_Picanto </td>\n <td style=\"text-align:right;\"> 55 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_3008 </td>\n <td style=\"text-align:right;\"> 55 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C4 Picasso </td>\n <td style=\"text-align:right;\"> 58 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Kuga </td>\n <td style=\"text-align:right;\"> 58 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_Grand C4 Picasso </td>\n <td style=\"text-align:right;\"> 60 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_A1 </td>\n <td style=\"text-align:right;\"> 61 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Mondeo </td>\n <td style=\"text-align:right;\"> 62 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Scirocco </td>\n <td style=\"text-align:right;\"> 63 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase E </td>\n <td style=\"text-align:right;\"> 66 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Captur </td>\n <td style=\"text-align:right;\"> 66 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_A6 </td>\n <td style=\"text-align:right;\"> 67 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C-Elysee </td>\n <td style=\"text-align:right;\"> 68 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_Micra </td>\n <td style=\"text-align:right;\"> 70 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Kangoo Combi </td>\n <td style=\"text-align:right;\"> 70 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Aygo </td>\n <td style=\"text-align:right;\"> 70 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> DACIA_Sandero </td>\n <td style=\"text-align:right;\"> 71 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Touran </td>\n <td style=\"text-align:right;\"> 73 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_i20 </td>\n <td style=\"text-align:right;\"> 74 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_5008 </td>\n <td style=\"text-align:right;\"> 74 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Caddy </td>\n <td style=\"text-align:right;\"> 75 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SKODA_Fabia </td>\n <td style=\"text-align:right;\"> 77 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> HYUNDAI_i30 </td>\n <td style=\"text-align:right;\"> 83 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C4 Cactus </td>\n <td style=\"text-align:right;\"> 87 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_Tipo </td>\n <td style=\"text-align:right;\"> 94 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_508 </td>\n <td style=\"text-align:right;\"> 96 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C4 </td>\n <td style=\"text-align:right;\"> 98 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_2008 </td>\n <td style=\"text-align:right;\"> 98 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_Partner </td>\n <td style=\"text-align:right;\"> 99 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_Berlingo </td>\n <td style=\"text-align:right;\"> 110 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_C-Max </td>\n <td style=\"text-align:right;\"> 113 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Yaris </td>\n <td style=\"text-align:right;\"> 120 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> CITROEN_C3 </td>\n <td style=\"text-align:right;\"> 124 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_JUKE </td>\n <td style=\"text-align:right;\"> 128 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> NISSAN_QASHQAI </td>\n <td style=\"text-align:right;\"> 135 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Serie 5 </td>\n <td style=\"text-align:right;\"> 138 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MERCEDES-BENZ_Clase C </td>\n <td style=\"text-align:right;\"> 140 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Passat </td>\n <td style=\"text-align:right;\"> 151 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> TOYOTA_Auris </td>\n <td style=\"text-align:right;\"> 164 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_208 </td>\n <td style=\"text-align:right;\"> 176 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_A3 </td>\n <td style=\"text-align:right;\"> 183 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Fiesta </td>\n <td style=\"text-align:right;\"> 186 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> MINI_MINI </td>\n <td style=\"text-align:right;\"> 192 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Corsa </td>\n <td style=\"text-align:right;\"> 202 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Insignia </td>\n <td style=\"text-align:right;\"> 202 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> AUDI_A4 </td>\n <td style=\"text-align:right;\"> 205 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> PEUGEOT_308 </td>\n <td style=\"text-align:right;\"> 224 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Leon </td>\n <td style=\"text-align:right;\"> 235 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Polo </td>\n <td style=\"text-align:right;\"> 236 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Serie 1 </td>\n <td style=\"text-align:right;\"> 237 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> OPEL_Astra </td>\n <td style=\"text-align:right;\"> 244 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Megane </td>\n <td style=\"text-align:right;\"> 245 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> RENAULT_Clio </td>\n <td style=\"text-align:right;\"> 251 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FORD_Focus </td>\n <td style=\"text-align:right;\"> 273 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> FIAT_500 </td>\n <td style=\"text-align:right;\"> 296 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> SEAT_Ibiza </td>\n <td style=\"text-align:right;\"> 297 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> BMW_Serie 3 </td>\n <td style=\"text-align:right;\"> 322 </td>\n </tr>\n <tr>\n <td style=\"text-align:left;\"> VOLKSWAGEN_Golf </td>\n <td style=\"text-align:right;\"> 361 </td>\n </tr>\n</tbody>\n</table>"A continuacion vamos a visualizar el TOP 10 de la cantidad de anuncios ofertados por marca de coche dentro de ese rango.
datft <- datc %>% mutate(make_redux=fct_lump_n(make, n=10, other_level = "OTHER"))
dropft <- datft$make_redux%>%droplevels("OTHER")
dropt <- as.data.frame(dropft)
dropl <- na.omit(dropt)Top10_marca <- count(dropl, dropft) %>% ggplot(aes(reorder(dropft,-n), n,fill=dropft,text=paste("Marca:", reorder(dropft,-n), "<br>", "Count:", n, "<br>")))+geom_col()+
theme(axis.text.x = element_text(face = "bold", family = "Courier New",size = 9,color = "azure4"),
axis.text.y = element_text(face= "italic", family = "Courier",color="azure4",
size = 9))+labs(x='MARCA DE COCHES', y='Numero de Coches ofertados por Marca')+
ggtitle('TOP 10 COCHES A LA VENTA POR MARCA')+ theme(legend.title = element_blank())
ggplotly(Top10_marca,tooltip=c("text"))Despues vamos a visualizar el TOP 10 de la cantidad de anuncios ofertados por marca y modelo de coches dentro de ese rango.
datft2 <- datc %>% mutate(model_redux=fct_lump_n(make_model, n=10, other_level = "Other"))
dropft2 <- datft2$model_redux%>%droplevels("Other")
dropt2 <- as.data.frame(dropft2)
dropl2 <- na.omit(dropt2)Top10_marcmod <- count(dropl2, dropft2) %>% ggplot(aes(reorder(dropft2,n), n,fill=dropft2,text=paste("Marca+model:", reorder(dropft2,-n), "<br>", "Count:", n, "<br>")))+geom_col()+coord_flip()+
theme(axis.text.x = element_text(face = "bold", family = "Courier New",size = 9,color = "azure4"),
axis.text.y = element_text(face= "italic", family = "Courier",color="azure4",
size = 9))+labs(x='MARCA DE COCHES', y='Numero de Coches ofertados por Marca')+
ggtitle('TOP 10 COCHES A LA VENTA POR MARCA Y MODELO')+ theme(legend.title = element_blank())
ggplotly(Top10_marcmod,tooltip=c("text"))Tambien veremos el promedio de precio de los coches electricos que hay en la relacion por orden, marca y modelo de los mismos.
med_elect <- datc%>%
filter(fuel=="Electrico")%>%
group_by(make_model,fuel)%>%
summarise(avgelect=round(mean(`best_price`)))%>%
ggplot(aes(reorder(make_model,-avgelect),avgelect,fill=avgelect,text=paste("Marca_model:", reorder(make_model,-avgelect), "<br>", "avgprice:", avgelect, "<br>")))+geom_col()+
theme(axis.text.x = element_text(face = "bold", family = "Courier New",size = 9,angle=45,color = "azure4"),
axis.text.y = element_text(face= "italic", family = "Courier",color="azure4",
size = 9))+labs(x='MARCA Y MODELO', y='Promedio Precio Coches Electricos')+
ggtitle('PRECIOS DE COCHES ELECTRICOS POR MARCA Y MODELO')+ theme(legend.title = element_blank())## `summarise()` has grouped output by 'make_model'. You can override using the `.groups` argument.ggplotly(med_elect,tooltip=c("text"))Vamos a ver ahora el promedio de precios por marca de los coches ofertados de segunda mano de nuestros datos.
dat2avg <- datc%>%
group_by(make)%>%
summarise(totalaverage=round(mean(`best_price`)))treemap(dat2avg, index = c("make","totalaverage"),
vSize = "totalaverage", type = "index", fontsize.labels=7,fontface.labels= c("bold.italic","bold"),algorithm="pivotSize",
align.labels = list(c("center","center"),c("right","bottom")),title="PROMEDIO DE PRECIOS POR MARCA DE COCHES OFERTADOS")
A continuacion veremos por marca y modelo tambien el promedio de precios de cada uno.
dat3avg <- datc%>%
group_by(make,model)%>%
summarise(totalaverage3=round(mean(`best_price`),digits=0))## `summarise()` has grouped output by 'make'. You can override using the `.groups` argument.d3tree2(
treemap(
dat3avg
,index=c("make", "model", "totalaverage3")
,vSize="totalaverage3"
,vColor="totalaverage3"
,type="index"
,title="PROMEDIO DE PRECIO POR MARCA Y MODELO"
)
, rootname = "Marca"
)
Vamos ahora a determinar el porcentaje de coches por tipo de fuel o combustible determinado por cada provincia que ofrece la venta de vehiculos.
f_fuel <- datc%>%
group_by(province,fuel)%>%
summarise(count =n())%>%
mutate(perc_fuel= (count/sum(count))*100)## `summarise()` has grouped output by 'province'. You can override using the `.groups` argument.fuel <- ggplot(f_fuel,aes(x=province,y=perc_fuel,fill=fuel,
label=scales::percent_format(accuracy=0.01,scale=1,suffix="%")(perc_fuel),
text=paste('percent', scales::percent_format(accuracy=0.01,scale=1,suffix="%")(perc_fuel))))+
geom_bar(position="fill",stat="identity")+scale_y_continuous(labels=percent_format())+
theme(axis.text.x = element_text(face = "bold", family = "Courier New",angle=90,size = 9,vjust=0.5,color = "azure4"),
axis.text.y = element_text(face= "italic", family = "Courier",color="azure4",
size = 9))+labs(x='PROVINCIA', y='Porcentaje Fuel/Provincia')+
ggtitle('PORCENTAJE DE COCHES POR TIPO DE FUEL Y PROVINCIA')+ theme(legend.title = element_blank())
ggplotly(fuel,tooltip=c("text","province","fuel"))Asimismo vamos a determinar el porcentaje de coches por tipo de cambio determinado por cada provincia que ofrece la venta de vehiculos.
s_shift <- datc%>%
group_by(province,shift)%>%
summarise(count =n())%>%
mutate(perc_shift= (count/sum(count))*100)## `summarise()` has grouped output by 'province'. You can override using the `.groups` argument.shift <- ggplot(s_shift,aes(x=province,y=perc_shift,fill=shift,
label=scales::percent_format(accuracy=0.01,scale=1,suffix="%")(perc_shift),
text=paste('percent', scales::percent_format(accuracy=0.01,scale=1,suffix="%")(perc_shift))))+
geom_bar(position="fill",stat="identity")+scale_y_continuous(labels=percent_format())+
theme(axis.text.x = element_text(face = "bold", family = "Courier New",angle=90,size = 9,vjust=0.5,color = "azure4"),
axis.text.y = element_text(face= "italic", family = "Courier",color="azure4",
size = 9))+labs(x='PROVINCIA', y='Porcentaje Tipo de Cambio/Provincia')+
ggtitle('PORCENTAJE DE COCHES POR TIPO DE CAMBIO Y PROVINCIA')+ theme(legend.title = element_blank())
ggplotly(shift,tooltip=c("text","province","shift"))Tambien vamos a determinar el porcentaje de coches en funcion de si el que lo vende es profesional(concesionario) o particular dentro de cada provincia que oferta estos vehiculos.
datc$is_professional <- factor(datc$is_professional, labels=c("No Profesional","Profesional"))
p_isprof <- datc%>%
group_by(province,is_professional)%>%
summarise(count =n())%>%
mutate(perc_isprof= (count/sum(count))*100)## `summarise()` has grouped output by 'province'. You can override using the `.groups` argument.isprof <- ggplot(p_isprof,aes(x=province,y=perc_isprof,fill=is_professional,
label=scales::percent_format(accuracy=0.01,scale=1,suffix="%")(perc_isprof),
text=paste('percent', scales::percent_format(accuracy=0.01,scale=1,suffix="%")(perc_isprof))))+
geom_bar(position="fill",stat="identity")+scale_y_continuous(labels=percent_format())+
theme(axis.text.x = element_text(face = "bold", family = "Courier New",angle=90,size = 9,vjust=0.5,color = "azure4"),
axis.text.y = element_text(face= "italic", family = "Courier",color="azure4",
size = 9))+labs(x='PROVINCIA', y='Porcentaje Tipo de Vendedor/ Provincia')+
ggtitle('PORCENTAJE DE COCHES POR TIPO DE VENDEDOR Y PROVINCIA')+ theme(legend.title = element_blank())
ggplotly(isprof,tooltip=c("text","province","is_professional"))Vamos a visualizar ahora el porcentaje total por tipo de combustible de los coches de segunda mano.
dat4count <- as.data.frame(table(datc$fuel))%>%
mutate(porcentaje=scales::percent(Freq/sum(Freq),accuracy=0.01))
dat4count## Var1 Freq porcentaje
## 1 Diesel 8034 62.77%
## 2 Electrico 71 0.55%
## 3 Gas licuado (GLP) 61 0.48%
## 4 Gas natural (CNG) 24 0.19%
## 5 Gasolina 4457 34.82%
## 6 Hibrido 150 1.17%
## 7 Hibrido enchufable 3 0.02%totalfuel <- plot_ly(dat4count, labels = ~Var1, values = ~Freq, type = 'pie')
totalfuel <- totalfuel %>% layout(title = 'TOTAL FUEL COCHES 2ª MANO',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
totalfuelSeguidamente veremos el porcentaje total por tipo de cambio de los coches recogidos en nuestros datos.
dat4count2 <- as.data.frame(table(datc$shift))%>%
mutate(porcentaje=scales::percent(Freq/sum(Freq),accuracy=0.01))
dat4count2## Var1 Freq porcentaje
## 1 Automatico 2221 17.35%
## 2 Manual 10579 82.65%pie3D(dat4count2$Freq,labels=dat4count2$porcentaje,main="TIPO DE CAMBIO UTILIZADO COCHES DE SEGUNDA MANO",radius=0.95,explode = 0.1,labelcex=0.7,theta=55*pi/180,height=0.1)
par(xpd=TRUE)
legend(1,0.7,legend=dat4count2$Var1,cex=0.7,yjust=0.2, xjust = -0.1,
fill = rainbow(length(dat4count2$porcentaje)))
A continuacion veremos el porcentaje total de los vendedores que ofertan coches de segunda mano, segun sean profesionales(concesionario) o particulares.
dat4count3 <- as.data.frame(table(datc$is_professional))%>%
mutate(porcentaje=scales::percent(Freq/sum(Freq),accuracy=0.01))
dat4count3## Var1 Freq porcentaje
## 1 No Profesional 3948 30.84%
## 2 Profesional 8852 69.16%pie3D(dat4count3$Freq,labels=dat4count3$porcentaje,main="VENDEDOR PROFESIONAL O PARTICULAR",radius=0.95,explode = 0.1,labelcex=0.7,theta=55*pi/180,height=0.1)
par(xpd=TRUE)
legend(1,0.7,legend=dat4count3$Var1,cex=0.7,yjust=0.2, xjust = -0.1,
fill = rainbow(length(dat4count3$porcentaje)))
Vamos a visualizar tambien la fecha media de fabricacion por marca de los vehiculos.
dat4avg <- datc%>%
group_by(make)%>%
summarise(avgyear=round(mean(`year`),digits=0))Fechmed <- ggplot(dat4avg,aes(x=reorder(make,desc(factor(avgyear))),y=factor(avgyear),text=paste("Marca:", reorder(make,desc(factor(avgyear))), "<br>", "Avgyear:", factor(avgyear), "<br>")))+geom_col(fill="blue")+
theme(axis.text.x = element_text(face = "bold", family = "Courier New",angle=90,size = 9,color = "azure4"),
axis.text.y = element_text(face= "italic", family = "Courier",color="azure4",
size = 9))+labs(x='MARCA DE COCHES', y='Años')+
ggtitle('FECHA MEDIA DE FABRICACION POR MARCA DE VEHICULOS')+ theme(legend.title = element_blank())
ggplotly(Fechmed,tooltip=c("text"))Tambien veremos la frecuencia del kilometraje de los coches de segunda mano que estan en venta.
freqkilm <- ggplot(datc, aes(x=kms)) +
geom_histogram(col="black",
fill="purple",
alpha = .2) +
labs(title="HISTOGRAMA KMS", x="KMS", y="Frequency")+
xlim(c(0,500000))
ggplotly(freqkilm)3. MODELIZACION
DIVISION DE LOS DATOS DE ENTRENAMIENTO Y PRUEBA DE LOS MODELOS
Creamos las particiones de entrenamiento y prueba, 70% y 30% respectivamente.
set.seed(85)
partition <- createDataPartition(y=datfilter$best_price, p=0.7, list=F)
trainingSet <- datfilter[partition,]
testingSet <- datfilter[-partition,]Vamos a realizar esta modelizacion a traves de 4 modelos diferentes con sus correspondientes algoritmos, como son:
Regresion lineal(LM), ExtraGradientBoosting(XGBOOST), Random Forest(RFOREST) y KNN.
Procedemos a continuacion ahora a hacer la validacion cruzada 10 veces con 3 repeticiones.
trainControl <- trainControl(method="repeatedcv", number = 10,repeats=3)
metric <- "RMSE"Ahora procedemos a realizar el entrenamiento con los modelos anteriormente dichos:
LM
set.seed(85)
lm <- train(best_price~., data = trainingSet, method = "lm", metric=metric,
preProc=c("center", "scale"),trControl=trainControl)XGBOOST
set.seed(85)
xgbst <- train(best_price~., data = trainingSet, method = "xgbLinear", metric=metric,preProc=c("center", "scale"),trControl=trainControl)RFOREST
set.seed(85)
rforest <- train(trainingSet[,1:10],trainingSet[,11], method = "ranger", metric=metric,num.trees=100,
preProc=c("center", "scale"),trControl=trainControl,respect.unordered.factors = TRUE)KNN
set.seed(85)
knn <- train(best_price~., data = trainingSet, method = "knn", metric=metric,
preProc=c("center", "scale"),trControl=trainControl)EVALUACION Y COMPARACION DE LOS ALGORITMOS QUE UTILIZAMOS
set.seed(85)
Results <- resamples(list(LM=lm, XGBOOST= xgbst, RFOREST=rforest, KNN=knn))
summary(Results)##
## Call:
## summary.resamples(object = Results)
##
## Models: LM, XGBOOST, RFOREST, KNN
## Number of resamples: 30
##
## MAE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## LM 944.4070 976.0758 983.9539 987.2817 1000.1184 1023.6205 0
## XGBOOST 851.6686 866.8550 875.0349 879.3448 891.7567 927.9521 0
## RFOREST 881.6983 907.8964 920.8578 922.4130 941.0051 963.8679 0
## KNN 1198.1311 1247.0183 1256.0821 1256.4307 1269.4564 1290.5144 0
##
## RMSE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## LM 1203.511 1267.361 1283.736 1318.221 1328.411 1617.281 0
## XGBOOST 1067.448 1105.380 1122.026 1126.278 1143.300 1193.735 0
## RFOREST 1121.472 1169.630 1191.168 1190.116 1209.772 1244.293 0
## KNN 1470.157 1519.602 1529.158 1532.347 1558.090 1572.528 0
##
## Rsquared
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## LM 0.2883438 0.3932986 0.4070434 0.4042316 0.4338683 0.4775843 0
## XGBOOST 0.5021648 0.5361225 0.5520001 0.5477383 0.5629324 0.5896132 0
## RFOREST 0.4457864 0.4765231 0.4929557 0.4949056 0.5055908 0.5563069 0
## KNN 0.1327529 0.1548898 0.1707678 0.1716328 0.1836352 0.2241544 0dotplot(Results)
Como puede verse los 2 algoritmos con diferencia mas destacados (RMSE) en el entramiento son XGBOOST y RANDOM FOREST, destacando el primero sobre el segundo ligeramente.
OPTIMIZACION DE HIPERPARAMETROS
Vamos a optimizar los modelos con los resultados de RMSE mas bajos, en este caso muy cercanos,los modelos de XGBOOST y RANDOM FOREST.
Para poder aplicar los hiperparametros a los modelos que hemos dicho, vamos a visualizar los hiperparametros optimos que dieron los resultados anteriores, y vamos a establecer nuevos hiperparametros de referencia en torno a estos para ver si podemos mejorar los resultados anteriores. Asi pues:
print(xgbst)## eXtreme Gradient Boosting
##
## 8961 samples
## 10 predictor
##
## Pre-processing: centered (599), scaled (599)
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 8065, 8065, 8065, 8066, 8065, 8064, ...
## Resampling results across tuning parameters:
##
## lambda alpha nrounds RMSE Rsquared MAE
## 0e+00 0e+00 50 1158.403 0.5234567 920.2527
## 0e+00 0e+00 100 1137.440 0.5385180 892.8081
## 0e+00 0e+00 150 1131.783 0.5434236 882.4207
## 0e+00 1e-04 50 1158.403 0.5234567 920.2527
## 0e+00 1e-04 100 1137.440 0.5385180 892.8081
## 0e+00 1e-04 150 1131.813 0.5433997 882.4151
## 0e+00 1e-01 50 1158.214 0.5236407 919.9824
## 0e+00 1e-01 100 1135.979 0.5396901 891.6553
## 0e+00 1e-01 150 1130.482 0.5444375 880.8093
## 1e-04 0e+00 50 1157.765 0.5239829 919.5535
## 1e-04 0e+00 100 1135.751 0.5398620 891.5633
## 1e-04 0e+00 150 1130.309 0.5446285 880.6287
## 1e-04 1e-04 50 1157.765 0.5239829 919.5535
## 1e-04 1e-04 100 1135.751 0.5398620 891.5633
## 1e-04 1e-04 150 1130.309 0.5446285 880.6287
## 1e-04 1e-01 50 1158.071 0.5236952 919.6146
## 1e-04 1e-01 100 1136.372 0.5393403 892.0102
## 1e-04 1e-01 150 1129.513 0.5452190 880.3518
## 1e-01 0e+00 50 1157.179 0.5246009 919.9074
## 1e-01 0e+00 100 1132.378 0.5427577 890.4943
## 1e-01 0e+00 150 1126.466 0.5475931 879.4161
## 1e-01 1e-04 50 1157.179 0.5246009 919.9074
## 1e-01 1e-04 100 1132.378 0.5427577 890.4943
## 1e-01 1e-04 150 1126.278 0.5477383 879.3448
## 1e-01 1e-01 50 1156.893 0.5248479 919.8702
## 1e-01 1e-01 100 1132.363 0.5427738 890.4335
## 1e-01 1e-01 150 1126.708 0.5473711 879.2831
##
## Tuning parameter 'eta' was held constant at a value of 0.3
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were nrounds = 150, lambda = 0.1, alpha
## = 1e-04 and eta = 0.3.print(rforest)## Random Forest
##
## 8961 samples
## 10 predictor
##
## Pre-processing: centered (3), scaled (3), ignore (7)
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 8065, 8065, 8065, 8066, 8065, 8064, ...
## Resampling results across tuning parameters:
##
## mtry splitrule RMSE Rsquared MAE
## 2 variance 1196.389 0.4975000 954.5151
## 2 extratrees 1297.173 0.4266905 1059.0382
## 6 variance 1190.116 0.4949056 922.4130
## 6 extratrees 1216.945 0.4721816 951.1000
## 10 variance 1213.070 0.4774561 931.7894
## 10 extratrees 1217.057 0.4718081 941.6542
##
## Tuning parameter 'min.node.size' was held constant at a value of 5
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were mtry = 6, splitrule = variance
## and min.node.size = 5.Despues de distintas experimentaciones nos hemos decantado por los siguientes hiperparametros para mejorar los modelos establecidos.
XGBOOST
hiperparametrosXG <- expand.grid(nrounds=200,
eta=0.3,
lambda=1,
alpha = seq(0.005,0.05,0.005))set.seed(85)
xgbst_opt <- train(best_price~., data = trainingSet, method = "xgbLinear", metric=metric,tuneGrid=hiperparametrosXG,preProc=c("center","scale"),trControl=trainControl)RFOREST
hiperparametrosRF <- expand.grid(mtry = c(1,3,4,6,7,10),
min.node.size = c( 3,5,7,10,25,50,75,100),
splitrule = "variance")set.seed(85)
rf_opt <- train(trainingSet[,1:10],trainingSet[,11], method = "ranger", metric=metric,num.trees=500,tuneGrid=hiperparametrosRF,
preProc=c("center", "scale"),trControl=trainControl,respect.unordered.factors = TRUE)Comprobamos los resultados para evaluar los algoritmos ya optimizados:
set.seed(85)
Results <- resamples(list(XGBOOST=xgbst_opt, RFOREST= rf_opt))
summary(Results)##
## Call:
## summary.resamples(object = Results)
##
## Models: XGBOOST, RFOREST
## Number of resamples: 30
##
## MAE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBOOST 829.9863 855.3278 868.9967 869.5923 875.5534 906.5819 0
## RFOREST 889.2853 908.7524 921.5192 922.5166 935.1776 962.8692 0
##
## RMSE
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBOOST 1058.792 1107.208 1119.675 1118.304 1135.471 1170.046 0
## RFOREST 1118.245 1155.510 1177.678 1173.866 1191.420 1233.721 0
##
## Rsquared
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## XGBOOST 0.5076065 0.5408314 0.5525082 0.5541237 0.5658952 0.5960690 0
## RFOREST 0.4623398 0.4957587 0.5083531 0.5103533 0.5255308 0.5632897 0dotplot(Results)
Como vemos despues de optimizar los algoritmos, sigue siendo XGBOOST con un menor RMSE el mejor algoritmo de la relacion, el cual ha mejorado con respecto al anterior dato del mismo, habiendo tambien una mejoria mucho mas leve para RANDOM FOREST.
Por lo tanto realizamos la predicion para el modelo de XGBOOST y hallamos su RMSE con respecto a nuestro conjunto de test(prueba).
predictions <- predict(xgbst_opt,testingSet)
RMSE(testingSet$best_price, predictions)## [1] 1138.334Como vemos nos sale un RMSE similar o cercano al que nos daba en el entrenamiento.
PLOT PREDICTIONS VS TEST DATA
plot <-testingSet %>% ggplot(aes(best_price, predictions))+
geom_point(position="jitter",alpha=0.5) +
stat_smooth(aes(colour='black')) +
xlab('Actual valor best_price') +
ylab('Valor predicho de best_price')+
theme_bw()
ggplotly(plot)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'PLOT MARCAS PRECIO REAL VS PRECIO PREDICHO
dat5avg <- testingSet%>%
cbind(predictions)%>%
group_by(make)%>%
summarise(real_price=round(mean(`best_price`)),pred_price=round(mean(`predictions`)))plotvscomp <- dat5avg%>%
gather(type_avg,avgprice,c(real_price, pred_price))%>%
ggplot(aes(x=make,y=avgprice,color=type_avg))+
geom_point()+theme(axis.text.x = element_text(face = "bold", family = "Courier New",angle=90,size = 9,vjust=0.5,color = "azure4"),
axis.text.y = element_text(face= "italic", family = "Courier",color="azure4",
size = 9))+labs(x='MARCA', y='PROMEDIO PRECIO')+
ggtitle('REAL VS PREDICCION-PROMEDIOS POR MARCA')+ theme(legend.title = element_blank())
ggplotly(plotvscomp)