library(readr)
data <- read_csv("C:/Users/robie/Downloads/HousePriceData.csv")## Rows: 905 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Parking, City_Category
## dbl (8): Observation, Dist_Taxi, Dist_Market, Dist_Hospital, Carpet, Builtup...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(data)## spc_tbl_ [905 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Observation : num [1:905] 1 2 3 4 5 6 7 8 9 10 ...
## $ Dist_Taxi : num [1:905] 9796 8294 11001 8301 10510 ...
## $ Dist_Market : num [1:905] 5250 8186 14399 11188 12629 ...
## $ Dist_Hospital: num [1:905] 10703 12694 16991 12289 13921 ...
## $ Carpet : num [1:905] 1659 1461 1340 1451 1770 ...
## $ Builtup : num [1:905] 1961 1752 1609 1748 2111 ...
## $ Parking : chr [1:905] "Open" "Not Provided" "Not Provided" "Covered" ...
## $ City_Category: chr [1:905] "CAT B" "CAT B" "CAT A" "CAT B" ...
## $ Rainfall : num [1:905] 530 210 720 620 450 760 1030 1020 680 1130 ...
## $ House_Price : num [1:905] 6649000 3982000 5401000 5373000 4662000 ...
## - attr(*, "spec")=
## .. cols(
## .. Observation = col_double(),
## .. Dist_Taxi = col_double(),
## .. Dist_Market = col_double(),
## .. Dist_Hospital = col_double(),
## .. Carpet = col_double(),
## .. Builtup = col_double(),
## .. Parking = col_character(),
## .. City_Category = col_character(),
## .. Rainfall = col_double(),
## .. House_Price = col_double()
## .. )
## - attr(*, "problems")=<externalptr>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
## regresion <- lm(
House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital +
Carpet + Builtup + factor(Parking) + factor(City_Category) + Rainfall,
data = data,
na.action = na.omit)
summary(regresion)##
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital +
## Carpet + Builtup + factor(Parking) + factor(City_Category) +
## Rainfall, data = data, na.action = na.omit)
##
## 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 ***
## factor(Parking)No Parking -6.170e+05 1.393e+05 -4.429 1.06e-05 ***
## factor(Parking)Not Provided -5.077e+05 1.239e+05 -4.096 4.58e-05 ***
## factor(Parking)Open -2.597e+05 1.131e+05 -2.297 0.0218 *
## factor(City_Category)CAT B -1.883e+06 9.641e+04 -19.529 < 2e-16 ***
## factor(City_Category)CAT 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-16datos_nuevos <- data.frame(
Dist_Taxi = 9000,
Dist_Market = 6000,
Dist_Hospital = 12000,
Carpet = 1600,
Builtup = 1900,
Parking = "Open",
City_Category = "CAT B",
Rainfall = 500
)
predict(regresion, newdata = datos_nuevos)## 1
## 5772055Resultados indican que el valor de una vivienda está determinado principalmente por su tamaño, la ciudad en la que se ubica y la disponibilidad de estacionamiento. En contraste, factores como la cercanía a servicios o las condiciones de lluvia no presentan un impacto significativo en el precio dentro de este análisis. El modelo presenta un buen desempeño, ya que logra explicar la mayor parte de la variación observada en los precios de las viviendas. Con base en la información analizada, se estima que una casa con dichas características tendría un valor aproximado de 5,772,055. En este sentido, el modelo resulta útil tanto para identificar los factores clave que influyen en el precio de una vivienda como para realizar estimaciones de inmuebles con características similares.