Preparación de datos de precios de automóviles para construir modelos de regesión
Rubén Pizarro Gurrola
2022-10-12
1 Objetivo
Preparar datos para la construcción de modelos supervisados en la solución de tareas de predicción de precios de automóviles.
2 Descripción
Se recopilaron datos originales del portal de kaggle: https://www.kaggle.com/code/josetaboada/josetaboada-ds-carros/notebook
Los datos específicos a utilizar se encuentran en el portal de github. https://raw.githubusercontent.com/rpizarrog/Analisis-Inteligente-de-datos/main/datos/CarPrice_Assignment.csv
Se identifican las variables numéricas
Se identifican los valores de correlación en función del precio
3 Desarrollo
3.1 Cargar librerías
# Librerías
library(readr)
library(PerformanceAnalytics) # Para correlaciones gráficas
library(dplyr)
library(knitr) # Para datos tabulares
library(kableExtra) # Para datos tabulares amigables
library(ggplot2) # Para visualizar
library(plotly) # Para visualizar
library(caret) # Para particionar
library(Metrics) # Para determinar rmse
library(rpart) # Para árbol
library(rpart.plot) # Para árbol
library(randomForest) # Para random forest
library(caret) # Para hacer divisiones o particiones
library(reshape) # Para renombrar columnas3.2 Cargar datos
datos <- read.csv("https://raw.githubusercontent.com/rpizarrog/Analisis-Inteligente-de-datos/main/datos/CarPrice_Assignment.csv",
fileEncoding = "UTF-8",
stringsAsFactors = TRUE)3.3 Exploración de datos
Hay 205 observaciones y 26 variables de las cuales se eligen las variables numéricas.
str(datos)## 'data.frame': 205 obs. of 26 variables:
## $ car_ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ symboling : int 3 3 1 2 2 2 1 1 1 0 ...
## $ CarName : Factor w/ 147 levels "alfa-romero giulia",..: 1 3 2 4 5 9 5 7 6 8 ...
## $ fueltype : Factor w/ 2 levels "diesel","gas": 2 2 2 2 2 2 2 2 2 2 ...
## $ aspiration : Factor w/ 2 levels "std","turbo": 1 1 1 1 1 1 1 1 2 2 ...
## $ doornumber : Factor w/ 2 levels "four","two": 2 2 2 1 1 2 1 1 1 2 ...
## $ carbody : Factor w/ 5 levels "convertible",..: 1 1 3 4 4 4 4 5 4 3 ...
## $ drivewheel : Factor w/ 3 levels "4wd","fwd","rwd": 3 3 3 2 1 2 2 2 2 1 ...
## $ enginelocation : Factor w/ 2 levels "front","rear": 1 1 1 1 1 1 1 1 1 1 ...
## $ wheelbase : num 88.6 88.6 94.5 99.8 99.4 ...
## $ carlength : num 169 169 171 177 177 ...
## $ carwidth : num 64.1 64.1 65.5 66.2 66.4 66.3 71.4 71.4 71.4 67.9 ...
## $ carheight : num 48.8 48.8 52.4 54.3 54.3 53.1 55.7 55.7 55.9 52 ...
## $ curbweight : int 2548 2548 2823 2337 2824 2507 2844 2954 3086 3053 ...
## $ enginetype : Factor w/ 7 levels "dohc","dohcv",..: 1 1 6 4 4 4 4 4 4 4 ...
## $ cylindernumber : Factor w/ 7 levels "eight","five",..: 3 3 4 3 2 2 2 2 2 2 ...
## $ enginesize : int 130 130 152 109 136 136 136 136 131 131 ...
## $ fuelsystem : Factor w/ 8 levels "1bbl","2bbl",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ boreratio : num 3.47 3.47 2.68 3.19 3.19 3.19 3.19 3.19 3.13 3.13 ...
## $ stroke : num 2.68 2.68 3.47 3.4 3.4 3.4 3.4 3.4 3.4 3.4 ...
## $ compressionratio: num 9 9 9 10 8 8.5 8.5 8.5 8.3 7 ...
## $ horsepower : int 111 111 154 102 115 110 110 110 140 160 ...
## $ peakrpm : int 5000 5000 5000 5500 5500 5500 5500 5500 5500 5500 ...
## $ citympg : int 21 21 19 24 18 19 19 19 17 16 ...
## $ highwaympg : int 27 27 26 30 22 25 25 25 20 22 ...
## $ price : num 13495 16500 16500 13950 17450 ...kable(head(datos, 10), caption = "Datos de precios de carros") %>%
kable_styling(full_width = F, bootstrap_options = c("striped", "bordered", "condensed")) %>%
kable_paper("hover")| car_ID | symboling | CarName | fueltype | aspiration | doornumber | carbody | drivewheel | enginelocation | wheelbase | carlength | carwidth | carheight | curbweight | enginetype | cylindernumber | enginesize | fuelsystem | boreratio | stroke | compressionratio | horsepower | peakrpm | citympg | highwaympg | price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3 | alfa-romero giulia | gas | std | two | convertible | rwd | front | 88.6 | 168.8 | 64.1 | 48.8 | 2548 | dohc | four | 130 | mpfi | 3.47 | 2.68 | 9.0 | 111 | 5000 | 21 | 27 | 13495.00 |
| 2 | 3 | alfa-romero stelvio | gas | std | two | convertible | rwd | front | 88.6 | 168.8 | 64.1 | 48.8 | 2548 | dohc | four | 130 | mpfi | 3.47 | 2.68 | 9.0 | 111 | 5000 | 21 | 27 | 16500.00 |
| 3 | 1 | alfa-romero Quadrifoglio | gas | std | two | hatchback | rwd | front | 94.5 | 171.2 | 65.5 | 52.4 | 2823 | ohcv | six | 152 | mpfi | 2.68 | 3.47 | 9.0 | 154 | 5000 | 19 | 26 | 16500.00 |
| 4 | 2 | audi 100 ls | gas | std | four | sedan | fwd | front | 99.8 | 176.6 | 66.2 | 54.3 | 2337 | ohc | four | 109 | mpfi | 3.19 | 3.40 | 10.0 | 102 | 5500 | 24 | 30 | 13950.00 |
| 5 | 2 | audi 100ls | gas | std | four | sedan | 4wd | front | 99.4 | 176.6 | 66.4 | 54.3 | 2824 | ohc | five | 136 | mpfi | 3.19 | 3.40 | 8.0 | 115 | 5500 | 18 | 22 | 17450.00 |
| 6 | 2 | audi fox | gas | std | two | sedan | fwd | front | 99.8 | 177.3 | 66.3 | 53.1 | 2507 | ohc | five | 136 | mpfi | 3.19 | 3.40 | 8.5 | 110 | 5500 | 19 | 25 | 15250.00 |
| 7 | 1 | audi 100ls | gas | std | four | sedan | fwd | front | 105.8 | 192.7 | 71.4 | 55.7 | 2844 | ohc | five | 136 | mpfi | 3.19 | 3.40 | 8.5 | 110 | 5500 | 19 | 25 | 17710.00 |
| 8 | 1 | audi 5000 | gas | std | four | wagon | fwd | front | 105.8 | 192.7 | 71.4 | 55.7 | 2954 | ohc | five | 136 | mpfi | 3.19 | 3.40 | 8.5 | 110 | 5500 | 19 | 25 | 18920.00 |
| 9 | 1 | audi 4000 | gas | turbo | four | sedan | fwd | front | 105.8 | 192.7 | 71.4 | 55.9 | 3086 | ohc | five | 131 | mpfi | 3.13 | 3.40 | 8.3 | 140 | 5500 | 17 | 20 | 23875.00 |
| 10 | 0 | audi 5000s (diesel) | gas | turbo | two | hatchback | 4wd | front | 99.5 | 178.2 | 67.9 | 52.0 | 3053 | ohc | five | 131 | mpfi | 3.13 | 3.40 | 7.0 | 160 | 5500 | 16 | 22 | 17859.17 |
3.3.1 Diccionario de datos
| Col | Nombre | Descripción |
|---|---|---|
| 1 | Car_ID | Unique id of each observation (Interger) |
| 2 | Symboling | Its assigned insurance risk rating, A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.(Categorical) |
| 3 | carCompany | Name of car company (Categorical) |
| 4 | fueltype | Car fuel type i.e gas or diesel (Categorical) |
| 5 | aspiration | Aspiration used in a car (Categorical) (Std o Turbo) |
| 6 | doornumber | Number of doors in a car (Categorical). Puertas |
| 7 | carbody | body of car (Categorical). (convertible, sedan, wagon …) |
| 8 | drivewheel | type of drive wheel (Categorical). (hidráulica, manual, ) |
| 9 | enginelocation | Location of car engine (Categorical). Lugar del motor |
| 10 | wheelbase | Weelbase of car (Numeric). Distancia de ejes en pulgadas. |
| 11 | carlength | Length of car (Numeric). Longitud |
| 12 | carwidth | Width of car (Numeric). Amplitud |
| 13 | carheight | height of car (Numeric). Altura |
| 14 | curbweight | The weight of a car without occupants or baggage. (Numeric). Peso del auto |
| 15 | enginetype | Type of engine. (Categorical). Tipo de motor |
| 16 | cylindernumber | cylinder placed in the car (Categorical). Cilindraje |
| 17 | enginesize | Size of car (Numeric). Tamaño del carro en … |
| 18 | fuelsystem | Fuel system of car (Categorical) |
| 19 | boreratio | Boreratio of car (Numeric). Eficiencia de motor |
| 20 | stroke | Stroke or volume inside the engine (Numeric). Pistones, tiempos, combustión |
| 21 | compressionratio | compression ratio of car (Numeric). Comprensión o medición de presión en motor |
| 22 | horsepower | Horsepower (Numeric). Poder del carro |
| 23 | peakrpm | car peak rpm (Numeric). Picos de revoluciones por minuto |
| 24 | citympg | Mileage in city (Numeric). Consumo de gasolina |
| 25 | highwaympg | Mileage on highway (Numeric). Consumo de gasolina |
| 26 | price (Dependent variable) |
Price of car (Numeric). Precio del carro en dólares |
3.4 Preparación de datos
3.4.1 Selección de variables
Se seleccionan solo las variables numéricas utilizando la función f_variables_numéricas() previamente construida.
Se manda cargar la función para este propósito.
source("https://raw.githubusercontent.com/rpizarrog/Analisis-Inteligente-de-datos/main/funciones/funciones.r")vars_numericas <- f_variables_numericas(datos)
cat(vars_numericas, sep = ',')## car_ID,symboling,wheelbase,carlength,carwidth,carheight,curbweight,enginesize,boreratio,stroke,compressionratio,horsepower,peakrpm,citympg,highwaympg,priceDe estas variables se omite por su naturaleza de identificar el número de carro car_ID, 1,2,3…n y de no ser de interés estadístico:
datos <- datos %>%
select (c(symboling, wheelbase, carlength, carwidth, carheight, curbweight, enginesize, boreratio, stroke, compressionratio, horsepower, peakrpm, citympg, highwaympg, price))
kable(head(datos, 10), caption = "Datos de precios de carros") %>%
kable_styling(full_width = F, bootstrap_options = c("striped", "bordered", "condensed")) %>%
kable_paper("hover")| symboling | wheelbase | carlength | carwidth | carheight | curbweight | enginesize | boreratio | stroke | compressionratio | horsepower | peakrpm | citympg | highwaympg | price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 88.6 | 168.8 | 64.1 | 48.8 | 2548 | 130 | 3.47 | 2.68 | 9.0 | 111 | 5000 | 21 | 27 | 13495.00 |
| 3 | 88.6 | 168.8 | 64.1 | 48.8 | 2548 | 130 | 3.47 | 2.68 | 9.0 | 111 | 5000 | 21 | 27 | 16500.00 |
| 1 | 94.5 | 171.2 | 65.5 | 52.4 | 2823 | 152 | 2.68 | 3.47 | 9.0 | 154 | 5000 | 19 | 26 | 16500.00 |
| 2 | 99.8 | 176.6 | 66.2 | 54.3 | 2337 | 109 | 3.19 | 3.40 | 10.0 | 102 | 5500 | 24 | 30 | 13950.00 |
| 2 | 99.4 | 176.6 | 66.4 | 54.3 | 2824 | 136 | 3.19 | 3.40 | 8.0 | 115 | 5500 | 18 | 22 | 17450.00 |
| 2 | 99.8 | 177.3 | 66.3 | 53.1 | 2507 | 136 | 3.19 | 3.40 | 8.5 | 110 | 5500 | 19 | 25 | 15250.00 |
| 1 | 105.8 | 192.7 | 71.4 | 55.7 | 2844 | 136 | 3.19 | 3.40 | 8.5 | 110 | 5500 | 19 | 25 | 17710.00 |
| 1 | 105.8 | 192.7 | 71.4 | 55.7 | 2954 | 136 | 3.19 | 3.40 | 8.5 | 110 | 5500 | 19 | 25 | 18920.00 |
| 1 | 105.8 | 192.7 | 71.4 | 55.9 | 3086 | 131 | 3.13 | 3.40 | 8.3 | 140 | 5500 | 17 | 20 | 23875.00 |
| 0 | 99.5 | 178.2 | 67.9 | 52.0 | 3053 | 131 | 3.13 | 3.40 | 7.0 | 160 | 5500 | 16 | 22 | 17859.17 |
3.5 Correlación de Pearson
Se hace una correlación de Pearson entre todas las variables numéricas en función del precio
correla <- data.frame(cor_price = cor(x = datos, y = datos[,'price']))kbl(correla, caption = "Correlación de Pearson en funcion del precio", col.names = c("Corr. Precio")) %>%
kable_styling(full_width = F, bootstrap_options = c("striped", "bordered", "condensed")) %>%
kable_paper("hover")| Corr. Precio | |
|---|---|
| symboling | -0.0799782 |
| wheelbase | 0.5778156 |
| carlength | 0.6829200 |
| carwidth | 0.7593253 |
| carheight | 0.1193362 |
| curbweight | 0.8353049 |
| enginesize | 0.8741448 |
| boreratio | 0.5531732 |
| stroke | 0.0794431 |
| compressionratio | 0.0679835 |
| horsepower | 0.8081388 |
| peakrpm | -0.0852672 |
| citympg | -0.6857513 |
| highwaympg | -0.6975991 |
| price | 1.0000000 |
3.5.1 Ordenación de correlación en función del precio
correla <- correla %>%
arrange(desc(cor_price))
kbl(correla, caption = "Correlación de Pearson en funcion del precio. Ordenados", col.names = "Corr. Precio") %>%
kable_styling(full_width = F, bootstrap_options = c("striped", "bordered", "condensed")) %>%
row_spec(c(2,3,4,5,6,7,14,15), bold = T, color = "white", background = "blue") %>%
kable_paper("hover")| Corr. Precio | |
|---|---|
| price | 1.0000000 |
| enginesize | 0.8741448 |
| curbweight | 0.8353049 |
| horsepower | 0.8081388 |
| carwidth | 0.7593253 |
| carlength | 0.6829200 |
| wheelbase | 0.5778156 |
| boreratio | 0.5531732 |
| carheight | 0.1193362 |
| stroke | 0.0794431 |
| compressionratio | 0.0679835 |
| symboling | -0.0799782 |
| peakrpm | -0.0852672 |
| citympg | -0.6857513 |
| highwaympg | -0.6975991 |
De estas variables numéricas se destacan aquellas que tienen una correlación de Pearson por encima del \(0.5\) y por debajo del \(-0.5\):
Por encima de \(0.5\):
enginesize 0.8741448
curbweight 0.8353049
horsepower 0.8081388
carwidth 0.7593253
carlength 0.6829200
wheelbase 0.5778156
boreratio 0.5531732
Por debajo de \(-0.5\):
citympg -0.6857513
highwaympg -0.6975991
3.5.2 Correlación visual
chart.Correlation(datos)
3.6 Generar el archivo preparado
write.csv(datos, "CarPrice_Assignment_Numericas_Preparado.csv")4 Interpretación
Pendiente …