casas <- read.csv("C:\\Users\\Torres\\Downloads\\R\\R App\\HousePriceData.csv")
casas <- casas[-348, ]
str(casas)## 'data.frame': 904 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 ...summary(casas)## Observation Dist_Taxi Dist_Market Dist_Hospital Carpet
## Min. : 1.0 Min. : 146 Min. : 1666 Min. : 3227 Min. : 775
## 1st Qu.:236.8 1st Qu.: 6476 1st Qu.: 9366 1st Qu.:11302 1st Qu.:1317
## Median :469.5 Median : 8224 Median :11143 Median :13188 Median :1477
## Mean :468.5 Mean : 8222 Mean :11011 Mean :13079 Mean :1486
## 3rd Qu.:700.2 3rd Qu.: 9936 3rd Qu.:12668 3rd Qu.:14851 3rd Qu.:1653
## Max. :932.0 Max. :16850 Max. :18281 Max. :22407 Max. :2229
## NA's :7
## Builtup Parking City_Category Rainfall
## Min. : 932 Length:904 Length:904 Min. :-110.0
## 1st Qu.:1578 Class :character Class :character 1st Qu.: 600.0
## Median :1774 Mode :character Mode :character Median : 780.0
## Mean :1782 Mean : 786.5
## 3rd Qu.:1983 3rd Qu.: 970.0
## Max. :2667 Max. :1560.0
##
## House_Price
## Min. : 1492000
## 1st Qu.: 4622750
## Median : 5857000
## Mean : 5924793
## 3rd Qu.: 7187250
## Max. :11632000
## regresion <- lm(House_Price~Dist_Taxi+Dist_Market+Dist_Hospital+Carpet+factor(Parking)+factor(City_Category)+Rainfall, data=casas)
summary(regresion)##
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital +
## Carpet + factor(Parking) + factor(City_Category) + Rainfall,
## data = casas)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3573873 -805741 -63223 762582 4419284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.599e+06 3.669e+05 15.261 < 2e-16 ***
## Dist_Taxi 2.958e+01 2.680e+01 1.103 0.2701
## Dist_Market 1.197e+01 2.079e+01 0.576 0.5650
## Dist_Hospital 4.955e+01 3.006e+01 1.648 0.0996 .
## Carpet 8.006e+02 1.641e+02 4.879 1.27e-06 ***
## factor(Parking)No Parking -6.134e+05 1.386e+05 -4.426 1.08e-05 ***
## factor(Parking)Not Provided -4.947e+05 1.233e+05 -4.013 6.51e-05 ***
## factor(Parking)Open -2.632e+05 1.125e+05 -2.339 0.0196 *
## factor(City_Category)CAT B -1.878e+06 9.593e+04 -19.573 < 2e-16 ***
## factor(City_Category)CAT C -2.896e+06 1.057e+05 -27.404 < 2e-16 ***
## Rainfall -9.982e+01 1.540e+02 -0.648 0.5171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1222000 on 886 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.5013, Adjusted R-squared: 0.4956
## F-statistic: 89.05 on 10 and 886 DF, p-value: < 2.2e-16datos_nuevos <- data.frame(Dist_Taxi=9796, Dist_Market=5250, Dist_Hospital=10703, Carpet=1659, Builtup=1961, Parking="Open", City_Category="CAT B", Rainfall=530)
predict(regresion, datos_nuevos)## 1
## 5616423```
Se puede apreciar que el modelo predictivo no se equivoca tanto con base en los datos analíticos en cuanto a House_Price, ya que el resultado cae perfectamente en la media de esta categoría.
El modelo dio una R cuadrada alta con un 87%, esto después de eliminar variables irrelevantes y factorizar aquellas categóricas.
Se puede apreciar que tanto carpet como builtup son las dos categorías que más impacto tienen en el precio de la casa.
Para el modelo final fue necesario deshacerse de la variable builtup, ya que esta en esencia era lo mismo que la variable carpet, por tanto se terminaban cancelando.