House Price Data RL
Anuar Gamez A01234986
2026-08-20
Teoria
lm() es la funcion en R para ajustar modelos
lineales.
Es el modelo estadistico mas basico que existe y mas facil de
interpretar.
Para intepretarlo se usa la medida R-cuadrada, que significa que tan
cerca estan los datos de la regresion. Va de 0 a 1, donde 1 es que el
modelo explica toda la variabilidad.
Contexto
Tenemos una base de datos que tiene la distancia a distintos puntos de interes como taxi, mercado, hospita el area dela casa y el tipo de estacionamiento y la categoria de la ciudad y la cantidad de lluvia registrada. Se desea generar un modelo predictivo.
instalar Paquetes y llamar librerias
#install.packages("corrplot")
library(corrplot)## corrplot 0.95 loadedImportar la base de datos
# file.choose()
df <- read.csv("/Users/anuar/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,10)## 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
## 7 7 13153 11869 17811 1542 1858 No Parking
## 8 8 5882 9948 13315 1261 1507 Open
## 9 9 7495 11589 13370 1090 1321 Not Provided
## 10 10 8233 7067 11400 1030 1235 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 4526000
## 7 CAT A 1030 7224000
## 8 CAT C 1020 3772000
## 9 CAT B 680 4631000
## 10 CAT C 1130 4415000tail(df,10)## Observation Dist_Taxi Dist_Market Dist_Hospital Carpet Builtup Parking
## 896 923 9538 11551 12839 1655 1986 Covered
## 897 924 11786 13969 15519 1156 1398 Open
## 898 925 9615 7904 12521 1451 1734 Open
## 899 926 7176 5779 12382 1539 1829 Open
## 900 927 10915 17486 15964 1549 1851 Not Provided
## 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
## 896 CAT B 1150 7743000
## 897 CAT A 140 9237000
## 898 CAT C 670 3488000
## 899 CAT B 650 4658000
## 900 CAT C 1220 7062000
## 901 CAT C 1080 6639000
## 902 CAT A 850 8208000
## 903 CAT A 1060 7644000
## 904 CAT B 770 9661000
## 905 CAT C 1110 5434000correlacion <- cor(df[,sapply(df, is.numeric)], use="complete.obs")
corrplot(correlacion)
Limpiar la base de datos
Generar el modelo
regresion <- lm(House_Price~., data=df)
summary(regresion)##
## Call:
## lm(formula = House_Price ~ ., data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3694478 -799483 -54242 780191 4539531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.331e+06 3.781e+05 14.097 < 2e-16 ***
## Observation 4.070e+02 1.524e+02 2.670 0.00773 **
## Dist_Taxi 2.777e+01 2.685e+01 1.034 0.30127
## Dist_Market 1.444e+01 2.083e+01 0.693 0.48839
## Dist_Hospital 4.921e+01 3.011e+01 1.634 0.10257
## Carpet 9.894e+03 1.423e+02 69.508 < 2e-16 ***
## Builtup -7.544e+03 2.407e+02 -31.344 < 2e-16 ***
## ParkingNo Parking -6.156e+05 1.388e+05 -4.435 1.04e-05 ***
## ParkingNot Provided -4.975e+05 1.236e+05 -4.027 6.15e-05 ***
## ParkingOpen -2.575e+05 1.127e+05 -2.285 0.02253 *
## City_CategoryCAT B -1.874e+06 9.613e+04 -19.494 < 2e-16 ***
## City_CategoryCAT C -2.897e+06 1.059e+05 -27.367 < 2e-16 ***
## Rainfall -9.559e+01 1.543e+02 -0.620 0.53564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1224000 on 885 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.9433, Adjusted R-squared: 0.9426
## F-statistic: 1228 on 12 and 885 DF, p-value: < 2.2e-16Ajustar el modelo
regresion2 <- lm(House_Price~Dist_Taxi+Dist_Market+Dist_Hospital+Carpet+Builtup+Parking+City_Category+Rainfall, data=df)
summary(regresion2)##
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital +
## Carpet + Builtup + Parking + City_Category + Rainfall, 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
## (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-16Generar predicciones
datos_nuevos <- data.frame(Dist_Taxi=9000, Dist_Market=8000, Dist_Hospital=12000, Carpet=1500, Builtup=1800, Parking="Covered", City_Category="CAT B", Rainfall=650)
predict(regresion2,datos_nuevos)## 1
## 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