Regression - House Price Data
Gabriel Tejeda A01178459
2026-08-21
Importar la base de datos
# file.choose()
df <- read.csv("/Users/gabotejeda/Desktop/HousePriceData.csv")Entender la 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 4526000Crear un mapa de correlación
library(corrplot)## corrplot 0.95 loadeddf_cor <- df[sapply(df, is.numeric)]
df_cor$Observation <- NULL
correlacion <- cor(df_cor, use = "complete.obs")
corrplot(correlacion)
Preparar las variables como factores y quitar NAs
df$Parking <- as.factor(df$Parking)
df$City_Category <- as.factor(df$City_Category)
df <- na.omit(df)Crear Modelo
regresion <- lm(House_Price ~ . - Observation, data = df)
summary(regresion)##
## Call:
## lm(formula = House_Price ~ . - Observation, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3586934 -837542 -65314 784513 4577689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.568e+06 3.688e+05 15.097 < 2e-16 ***
## Dist_Taxi 2.834e+01 2.694e+01 1.052 0.2931
## Dist_Market 1.237e+01 2.089e+01 0.592 0.5538
## Dist_Hospital 5.071e+01 3.021e+01 1.679 0.0936 .
## Carpet 9.907e+03 1.428e+02 69.398 < 2e-16 ***
## Builtup -7.575e+03 2.412e+02 -31.403 < 2e-16 ***
## ParkingNo Parking -6.170e+05 1.393e+05 -4.429 1.06e-05 ***
## ParkingNot Provided -5.077e+05 1.239e+05 -4.096 4.58e-05 ***
## ParkingOpen -2.597e+05 1.131e+05 -2.297 0.0218 *
## City_CategoryCAT B -1.883e+06 9.641e+04 -19.529 < 2e-16 ***
## City_CategoryCAT C -2.902e+06 1.062e+05 -27.321 < 2e-16 ***
## Rainfall -9.984e+01 1.548e+02 -0.645 0.5191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1228000 on 886 degrees of freedom
## Multiple R-squared: 0.9429, Adjusted R-squared: 0.9422
## F-statistic: 1329 on 11 and 886 DF, p-value: < 2.2e-16Modificar modelo de regresión
regresion2 <- lm(House_Price ~ Carpet + Builtup + Parking + City_Category,
data = df)
summary(regresion2)##
## Call:
## lm(formula = House_Price ~ Carpet + Builtup + Parking + City_Category,
## data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3430967 -802513 -46915 775070 4263090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6477702.2 272077.2 23.808 < 2e-16 ***
## Carpet 9968.8 143.3 69.584 < 2e-16 ***
## Builtup -7611.6 243.3 -31.279 < 2e-16 ***
## ParkingNo Parking -536301.8 139943.6 -3.832 0.000136 ***
## ParkingNot Provided -457380.4 125020.1 -3.658 0.000269 ***
## ParkingOpen -236576.3 114356.8 -2.069 0.038857 *
## City_CategoryCAT B -1918518.1 97007.4 -19.777 < 2e-16 ***
## City_CategoryCAT C -2908756.6 107359.6 -27.094 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1243000 on 890 degrees of freedom
## Multiple R-squared: 0.9412, Adjusted R-squared: 0.9407
## F-statistic: 2034 on 7 and 890 DF, p-value: < 2.2e-16```
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