##1. Importar la base de datos
df <- read.csv("/Users/danrwar/Desktop/Rstudio works/etapa 1/rentadebicis.csv")
##2. Entender la base de datos
summary(df)
## hora dia mes año
## Min. : 0.00 Min. : 1.000 Min. : 1.000 Min. :2011
## 1st Qu.: 6.00 1st Qu.: 5.000 1st Qu.: 4.000 1st Qu.:2011
## Median :12.00 Median :10.000 Median : 7.000 Median :2012
## Mean :11.54 Mean : 9.993 Mean : 6.521 Mean :2012
## 3rd Qu.:18.00 3rd Qu.:15.000 3rd Qu.:10.000 3rd Qu.:2012
## Max. :23.00 Max. :19.000 Max. :12.000 Max. :2012
## estacion dia_de_la_semana asueto temperatura
## Min. :1.000 Min. :1.000 Min. :0.00000 Min. : 0.82
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:0.00000 1st Qu.:13.94
## Median :3.000 Median :4.000 Median :0.00000 Median :20.50
## Mean :2.507 Mean :4.014 Mean :0.02857 Mean :20.23
## 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:0.00000 3rd Qu.:26.24
## Max. :4.000 Max. :7.000 Max. :1.00000 Max. :41.00
## sensacion_termica humedad velocidad_del_viento
## Min. : 0.76 Min. : 0.00 Min. : 0.000
## 1st Qu.:16.66 1st Qu.: 47.00 1st Qu.: 7.002
## Median :24.24 Median : 62.00 Median :12.998
## Mean :23.66 Mean : 61.89 Mean :12.799
## 3rd Qu.:31.06 3rd Qu.: 77.00 3rd Qu.:16.998
## Max. :45.45 Max. :100.00 Max. :56.997
## rentas_de_no_registrados rentas_de_registrados rentas_totales
## Min. : 0.00 Min. : 0.0 Min. : 1.0
## 1st Qu.: 4.00 1st Qu.: 36.0 1st Qu.: 42.0
## Median : 17.00 Median :118.0 Median :145.0
## Mean : 36.02 Mean :155.6 Mean :191.6
## 3rd Qu.: 49.00 3rd Qu.:222.0 3rd Qu.:284.0
## Max. :367.00 Max. :886.0 Max. :977.0
##3. Generar regresión lineal
regresion <- lm(rentas_totales ~ hora +dia + mes + año + estacion + dia_de_la_semana + sensacion_termica + humedad + velocidad_del_viento + asueto, data= df)
##4. Ajustar el modelo
regresion <- lm(rentas_totales ~ hora + mes + año + sensacion_termica + humedad + velocidad_del_viento, data= df)
##5. Construir el modelo predictivo
datos <- data.frame(hora= 11.54, mes=1:12, año=2013, sensacion_termica=22.66, humedad=61.89, velocidad_del_viento=12.799)
predict(regresion,datos)
## 1 2 3 4 5 6 7 8
## 267.4283 275.0020 282.5757 290.1494 297.7231 305.2968 312.8705 320.4443
## 9 10 11 12
## 328.0180 335.5917 343.1654 350.7391
1. Abrir base de datos
bd <- read.csv("/Users/danrwar/Desktop/Rstudio works/etapa 1/HousePriceData.csv")
summary(bd)
## 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
##
Observaciones 1. El precio de la casa está con datos atípicos. 2. Rainfall tiene valores negativos. 3. Carpet tiene 7 NA.
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
count(bd,Parking, sort=TRUE)
## Parking n
## 1 Open 355
## 2 Not Provided 225
## 3 Covered 184
## 4 No Parking 141
count(bd,City_Category, sort=TRUE)
## City_Category n
## 1 CAT B 351
## 2 CAT A 320
## 3 CAT C 234
# Cuantos NA tengo en la base de datos
sum(is.na(bd))
## [1] 7
# Cuantos NA tengo por variable
sapply(bd, function(x)sum(is.na(bd)))
## Observation Dist_Taxi Dist_Market Dist_Hospital Carpet
## 7 7 7 7 7
## Builtup Parking City_Category Rainfall House_Price
## 7 7 7 7 7
# Eliminar NA
bd <- na.omit(bd)
# Eliminar registro del precio atípico
bd <- bd[bd$House_Price<12000000,]
# Eliminar registro de rainfall negativo
bd <- bd[bd$Rainfall>=0,]
# Gráficas
boxplot(bd$House_Price, horizontal = TRUE)
regresion <- lm(House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital + Carpet + Builtup + Parking + City_Category + Rainfall, data = bd)
summary(regresion)
##
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital +
## Carpet + Builtup + Parking + City_Category + Rainfall, data = bd)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3572009 -800792 -65720 761534 4401585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.599e+06 3.672e+05 15.246 < 2e-16 ***
## Dist_Taxi 3.009e+01 2.682e+01 1.122 0.2622
## Dist_Market 1.285e+01 2.081e+01 0.618 0.5370
## Dist_Hospital 4.864e+01 3.008e+01 1.617 0.1062
## Carpet -7.997e+02 3.476e+03 -0.230 0.8181
## Builtup 1.339e+03 2.901e+03 0.462 0.6444
## ParkingNo Parking -6.040e+05 1.389e+05 -4.348 1.53e-05 ***
## ParkingNot Provided -4.924e+05 1.235e+05 -3.988 7.22e-05 ***
## ParkingOpen -2.632e+05 1.126e+05 -2.338 0.0196 *
## City_CategoryCAT B -1.877e+06 9.598e+04 -19.554 < 2e-16 ***
## City_CategoryCAT C -2.890e+06 1.059e+05 -27.300 < 2e-16 ***
## Rainfall -1.175e+02 1.550e+02 -0.758 0.4484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1222000 on 884 degrees of freedom
## Multiple R-squared: 0.5007, Adjusted R-squared: 0.4945
## F-statistic: 80.58 on 11 and 884 DF, p-value: < 2.2e-16
datos <- data.frame(Dist_Taxi=8278, Dist_Market=16251, Dist_Hospital=13857,Carpet=1455, Builtup=1764,Parking="Covered",City_Category="CAT A",Rainfall=390)
predict(regresion,datos)
## 1
## 7883860