Regresión lineal - House Pricing Data
Danara Esparza A00840602
2026-08-22
Teoría
lm() Es la función en R para ajustar modelos
lineales Es el modelo estadístico mas básico que existe, y más facil de
interpretar.
Para interpretarlo se usa la función R-cuadrada, que significa qué tan
cerca están los datos de la regresión. Va de 0 a 1, donde 1 es el que el
modelo explica toda la variabilidad.
Contexto
Se cuenta con un registro de propiedades residenciales con sus características físicas y de ubicación. Se desea generar un modelo predictivo para el precio de las casas.
Instalar paquetes y llamar librerías
#install.packages("corrplot")
library(corrplot)## corrplot 0.95 loadedSubir archivo
# file.choose()
df <- read.csv("/Users/danaraesparzamacias/Desktop/HousePriceData.csv") Entender base de datos
summary(df)## Observation Dist_Taxi Dist_Market Dist_Hospital
## Min. : 1.0 Min. : 146 Min. : 1666 Min. : 3227
## 1st Qu.:237.0 1st Qu.: 6477 1st Qu.: 9367 1st Qu.:11302
## Median :469.0 Median : 8228 Median :11149 Median :13189
## Mean :468.4 Mean : 8235 Mean :11022 Mean :13091
## 3rd Qu.:700.0 3rd Qu.: 9939 3rd Qu.:12675 3rd Qu.:14855
## Max. :932.0 Max. :20662 Max. :20945 Max. :23294
##
## Carpet Builtup Parking City_Category
## Min. : 775 Min. : 932 Length :905 Length :905
## 1st Qu.: 1317 1st Qu.: 1579 N.unique : 4 N.unique : 3
## Median : 1478 Median : 1774 N.blank : 0 N.blank : 0
## Mean : 1511 Mean : 1794 Min.nchar: 4 Min.nchar: 5
## 3rd Qu.: 1654 3rd Qu.: 1985 Max.nchar: 12 Max.nchar: 5
## Max. :24300 Max. :12730
## NAs :7
## Rainfall House_Price
## Min. :-110.0 Min. : 1492000
## 1st Qu.: 600.0 1st Qu.: 4623000
## Median : 780.0 Median : 5860000
## Mean : 786.9 Mean : 6083992
## 3rd Qu.: 970.0 3rd Qu.: 7200000
## Max. :1560.0 Max. :150000000
## str(df)## 'data.frame': 905 obs. of 10 variables:
## $ Observation : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Dist_Taxi : int 9796 8294 11001 8301 10510 6665 13153 5882 7495 8233 ...
## $ Dist_Market : int 5250 8186 14399 11188 12629 5142 11869 9948 11589 7067 ...
## $ Dist_Hospital: int 10703 12694 16991 12289 13921 9972 17811 13315 13370 11400 ...
## $ Carpet : int 1659 1461 1340 1451 1770 1442 1542 1261 1090 1030 ...
## $ Builtup : int 1961 1752 1609 1748 2111 1733 1858 1507 1321 1235 ...
## $ Parking : chr "Open" "Not Provided" "Not Provided" "Covered" ...
## $ City_Category: chr "CAT B" "CAT B" "CAT A" "CAT B" ...
## $ Rainfall : int 530 210 720 620 450 760 1030 1020 680 1130 ...
## $ House_Price : int 6649000 3982000 5401000 5373000 4662000 4526000 7224000 3772000 4631000 4415000 ...head(df)## Observation Dist_Taxi Dist_Market Dist_Hospital Carpet Builtup Parking
## 1 1 9796 5250 10703 1659 1961 Open
## 2 2 8294 8186 12694 1461 1752 Not Provided
## 3 3 11001 14399 16991 1340 1609 Not Provided
## 4 4 8301 11188 12289 1451 1748 Covered
## 5 5 10510 12629 13921 1770 2111 Not Provided
## 6 6 6665 5142 9972 1442 1733 Open
## City_Category Rainfall House_Price
## 1 CAT B 530 6649000
## 2 CAT B 210 3982000
## 3 CAT A 720 5401000
## 4 CAT B 620 5373000
## 5 CAT B 450 4662000
## 6 CAT B 760 4526000tail(df,5) ## Observation Dist_Taxi Dist_Market Dist_Hospital Carpet Builtup Parking
## 901 928 12176 8518 15673 1582 1910 Covered
## 902 929 7214 8717 10553 1387 1663 Open
## 903 930 7423 11708 13220 1200 1436 Open
## 904 931 15082 14700 19617 1299 1560 Open
## 905 932 9297 12537 14418 1174 1429 Covered
## City_Category Rainfall House_Price
## 901 CAT C 1080 6639000
## 902 CAT A 850 8208000
## 903 CAT A 1060 7644000
## 904 CAT B 770 9661000
## 905 CAT C 1110 5434000#Tomar en cuenta solo variables numéricas en la matriz de correlación
df_num <- df[, sapply(df, is.numeric)]
correlacion <- cor(df_num, use = "complete.obs")
corrplot(correlacion)
Generar el modelo
regresion <- lm(House_Price ~ ., data = df_num)
summary(regresion)##
## Call:
## lm(formula = House_Price ~ ., data = df_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4412613 -1228090 -100987 1275140 5529493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.283e+06 5.030e+05 6.526 1.14e-10 ***
## Observation 5.265e+02 2.118e+02 2.486 0.0131 *
## Dist_Taxi 5.509e-01 3.730e+01 0.015 0.9882
## Dist_Market 4.446e+01 2.888e+01 1.539 0.1241
## Dist_Hospital 5.905e+01 4.178e+01 1.413 0.1579
## Carpet 9.957e+03 1.979e+02 50.326 < 2e-16 ***
## Builtup -7.694e+03 3.341e+02 -23.031 < 2e-16 ***
## Rainfall 7.183e+01 2.138e+02 0.336 0.7370
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1702000 on 890 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.8898, Adjusted R-squared: 0.8889
## F-statistic: 1026 on 7 and 890 DF, p-value: < 2.2e-16Ajustar el modelo
regresion2 <- lm(House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital + Carpet + Builtup + Rainfall, data = df)
summary(regresion2) ##
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital +
## Carpet + Builtup + Rainfall, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4196767 -1205994 -77768 1277227 5655558
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.576e+06 4.904e+05 7.293 6.68e-13 ***
## Dist_Taxi 1.119e+00 3.741e+01 0.030 0.976
## Dist_Market 4.195e+01 2.895e+01 1.449 0.148
## Dist_Hospital 6.108e+01 4.189e+01 1.458 0.145
## Carpet 9.973e+03 1.983e+02 50.290 < 2e-16 ***
## Builtup -7.734e+03 3.346e+02 -23.111 < 2e-16 ***
## Rainfall 6.721e+01 2.144e+02 0.313 0.754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1707000 on 891 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.889, Adjusted R-squared: 0.8883
## F-statistic: 1189 on 6 and 891 DF, p-value: < 2.2e-16Generar predicciones
datos_nuevos <- data.frame(
Dist_Taxi = 8000,
Dist_Market = 10000,
Dist_Hospital = 12000,
Carpet = 1500,
Builtup = 1800,
Rainfall = 600
)
predict(regresion2, datos_nuevos) ## 1
## 5817366Generar predicciones por escenarios
escenarios <- data.frame(
Tipo = c("Económica", "Promedio", "Lujo"),
Dist_Taxi = c(12000, 8000, 4000),
Dist_Market = c(14000, 10000, 5000),
Dist_Hospital = c(15000, 11000, 6000),
Carpet = c(1000, 1500, 2200),
Builtup = c(1200, 1800, 2600),
Rainfall = c(700, 700, 700)
)
# Generar la nueva predicción
escenarios$Precio_Estimado <- predict(regresion2, escenarios)
escenarios## Tipo Dist_Taxi Dist_Market Dist_Hospital Carpet Builtup Rainfall
## 1 Económica 12000 14000 15000 1000 1200 700
## 2 Promedio 8000 10000 11000 1500 1800 700
## 3 Lujo 4000 5000 6000 2200 2600 700
## Precio_Estimado
## 1 5833209
## 2 5763003
## 3 6037679