Regresión Lineal casa

1 REGRESIÓN LINEAL

# Importar base de datos
  data <- read.csv("/Users/nataliamartinez/Desktop/HousePriceData.csv")

# Explorar datos
str(data)

## '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 ...

summary(data)

##   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   Class :character   Class :character  
##  Median : 1478   Median : 1774   Mode  :character   Mode  :character  
##  Mean   : 1511   Mean   : 1794                                        
##  3rd Qu.: 1654   3rd Qu.: 1985                                        
##  Max.   :24300   Max.   :12730                                        
##  NA's   :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  
## 

#Asegura que las categorías sean factores (antes de predecir)
data$Parking <- as.factor(data$Parking)
data$City_Category <- as.factor(data$City_Category)

#Modelo de regresión
modelo <- lm(
  House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital +
    Carpet + Builtup + Parking + City_Category + Rainfall,
  data = data
)

summary(modelo)

## 
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital + 
##     Carpet + Builtup + Parking + City_Category + Rainfall, data = data)
## 
## 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
##   (7 observations deleted due to missingness)
## Multiple R-squared:  0.9429, Adjusted R-squared:  0.9422 
## F-statistic:  1329 on 11 and 886 DF,  p-value: < 2.2e-16

2 Pronóstico 1

#Casa 1
datos_nuevos <- data.frame(
  Dist_Taxi = 9796,
  Dist_Market = 5250,
  Dist_Hospital = 10703,
  Carpet = 1659,
  Builtup = 1961,
  Parking = factor("Open", levels = levels(data$Parking)),
  City_Category = factor("CAT B", levels = levels(data$City_Category)),
  Rainfall = 530
)

predict(modelo, datos_nuevos)

##       1 
## 5838997

2.0.0.0.0.1 El modelo está diciendo que la casa #1 tendría un precio estimado de 5,838,997

2.0.0.0.0.2 Comparado con el precio real de 6,649,000

2.0.0.0.0.3 El modelo la estimó un poco más baja

3 Pronóstico 2

#Casa nueva
datos_nuevos <- data.frame(
  Dist_Taxi = 7000,
  Dist_Market = 8000,
  Dist_Hospital = 10000,
  Carpet = 1800,
  Builtup = 2100,
  Parking = factor("Covered", levels=levels(data$Parking)),
  City_Category = factor("CAT A", levels=levels(data$City_Category)),
  Rainfall = 700
)
predict(modelo, datos_nuevos)

##       1 
## 8227645

3.0.0.0.0.1 El modelo estima que una casa con esas características tendría un precio aproximado de 8,227,645