Practica8.- Predicciones con nuevos datos de casas de Melbourne
David Haro
30/3/2020
Objetivo.
Crear predicciones de casas con técnicas regresión múltiple y árboles de regresión. Descripción.
Crear predicciones con el modelo de regresión lineal y con el modelo de árboles de regresión utilizando para ello, el conjunto de datos de Melborne únicamente con variables del tipo numérica y recibiendo un uevo conjunto de 5 datos para alguans variables del conjunto de datos. Predecir:
Para cuando las variables siguientes tengan estos valores ¿cuál es la predicción del precio de venta de la propiedad utilizando ambos modelos?.
library(rpart) # Arboles
library(rpart.plot) # Visualizar y representar árboles
library(caret)
library(dplyr)
library(readr)
library(ggplot2)
library(reshape)datos <- read.csv("C:/Users/JD/Documents/Analisis inteligente de datos/Datos/melb_data.csv")
head(datos)## Suburb Address Rooms Type Price Method SellerG Date
## 1 Abbotsford 85 Turner St 2 h 1480000 S Biggin 3/12/2016
## 2 Abbotsford 25 Bloomburg St 2 h 1035000 S Biggin 4/02/2016
## 3 Abbotsford 5 Charles St 3 h 1465000 SP Biggin 4/03/2017
## 4 Abbotsford 40 Federation La 3 h 850000 PI Biggin 4/03/2017
## 5 Abbotsford 55a Park St 4 h 1600000 VB Nelson 4/06/2016
## 6 Abbotsford 129 Charles St 2 h 941000 S Jellis 7/05/2016
## Distance Postcode Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 1 2.5 3067 2 1 1 202 NA NA
## 2 2.5 3067 2 1 0 156 79 1900
## 3 2.5 3067 3 2 0 134 150 1900
## 4 2.5 3067 3 2 1 94 NA NA
## 5 2.5 3067 3 1 2 120 142 2014
## 6 2.5 3067 2 1 0 181 NA NA
## CouncilArea Lattitude Longtitude Regionname Propertycount
## 1 Yarra -37.7996 144.9984 Northern Metropolitan 4019
## 2 Yarra -37.8079 144.9934 Northern Metropolitan 4019
## 3 Yarra -37.8093 144.9944 Northern Metropolitan 4019
## 4 Yarra -37.7969 144.9969 Northern Metropolitan 4019
## 5 Yarra -37.8072 144.9941 Northern Metropolitan 4019
## 6 Yarra -37.8041 144.9953 Northern Metropolitan 4019tail(datos)## Suburb Address Rooms Type Price Method SellerG Date
## 13575 Westmeadows 9 Black St 3 h 582000 S Red 26/08/2017
## 13576 Wheelers Hill 12 Strada Cr 4 h 1245000 S Barry 26/08/2017
## 13577 Williamstown 77 Merrett Dr 3 h 1031000 SP Williams 26/08/2017
## 13578 Williamstown 83 Power St 3 h 1170000 S Raine 26/08/2017
## 13579 Williamstown 96 Verdon St 4 h 2500000 PI Sweeney 26/08/2017
## 13580 Yarraville 6 Agnes St 4 h 1285000 SP Village 26/08/2017
## Distance Postcode Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 13575 16.5 3049 3 2 2 256 NA NA
## 13576 16.7 3150 4 2 2 652 NA 1981
## 13577 6.8 3016 3 2 2 333 133 1995
## 13578 6.8 3016 3 2 4 436 NA 1997
## 13579 6.8 3016 4 1 5 866 157 1920
## 13580 6.3 3013 4 1 1 362 112 1920
## CouncilArea Lattitude Longtitude Regionname Propertycount
## 13575 -37.67917 144.8939 Northern Metropolitan 2474
## 13576 -37.90562 145.1676 South-Eastern Metropolitan 7392
## 13577 -37.85927 144.8790 Western Metropolitan 6380
## 13578 -37.85274 144.8874 Western Metropolitan 6380
## 13579 -37.85908 144.8930 Western Metropolitan 6380
## 13580 -37.81188 144.8845 Western Metropolitan 6543summary(datos)## Suburb Address Rooms Type
## Reservoir : 359 1/1 Clarendon St: 3 Min. : 1.000 h:9449
## Richmond : 260 13 Robinson St : 3 1st Qu.: 2.000 t:1114
## Bentleigh East: 249 14 Arthur St : 3 Median : 3.000 u:3017
## Preston : 239 2 Bruce St : 3 Mean : 2.938
## Brunswick : 222 28 Blair St : 3 3rd Qu.: 3.000
## Essendon : 220 36 Aberfeldie St: 3 Max. :10.000
## (Other) :12031 (Other) :13562
## Price Method SellerG Date
## Min. : 85000 PI:1564 Nelson :1565 27/05/2017: 473
## 1st Qu.: 650000 S :9022 Jellis :1316 3/06/2017 : 395
## Median : 903000 SA: 92 hockingstuart:1167 12/08/2017: 387
## Mean :1075684 SP:1703 Barry :1011 17/06/2017: 374
## 3rd Qu.:1330000 VB:1199 Ray : 701 27/11/2016: 362
## Max. :9000000 Marshall : 659 29/07/2017: 341
## (Other) :7161 (Other) :11248
## Distance Postcode Bedroom2 Bathroom
## Min. : 0.00 Min. :3000 Min. : 0.000 Min. :0.000
## 1st Qu.: 6.10 1st Qu.:3044 1st Qu.: 2.000 1st Qu.:1.000
## Median : 9.20 Median :3084 Median : 3.000 Median :1.000
## Mean :10.14 Mean :3105 Mean : 2.915 Mean :1.534
## 3rd Qu.:13.00 3rd Qu.:3148 3rd Qu.: 3.000 3rd Qu.:2.000
## Max. :48.10 Max. :3977 Max. :20.000 Max. :8.000
##
## Car Landsize BuildingArea YearBuilt
## Min. : 0.00 Min. : 0.0 Min. : 0 Min. :1196
## 1st Qu.: 1.00 1st Qu.: 177.0 1st Qu.: 93 1st Qu.:1940
## Median : 2.00 Median : 440.0 Median : 126 Median :1970
## Mean : 1.61 Mean : 558.4 Mean : 152 Mean :1965
## 3rd Qu.: 2.00 3rd Qu.: 651.0 3rd Qu.: 174 3rd Qu.:1999
## Max. :10.00 Max. :433014.0 Max. :44515 Max. :2018
## NA's :62 NA's :6450 NA's :5375
## CouncilArea Lattitude Longtitude
## :1369 Min. :-38.18 Min. :144.4
## Moreland :1163 1st Qu.:-37.86 1st Qu.:144.9
## Boroondara :1160 Median :-37.80 Median :145.0
## Moonee Valley: 997 Mean :-37.81 Mean :145.0
## Darebin : 934 3rd Qu.:-37.76 3rd Qu.:145.1
## Glen Eira : 848 Max. :-37.41 Max. :145.5
## (Other) :7109
## Regionname Propertycount
## Southern Metropolitan :4695 Min. : 249
## Northern Metropolitan :3890 1st Qu.: 4380
## Western Metropolitan :2948 Median : 6555
## Eastern Metropolitan :1471 Mean : 7454
## South-Eastern Metropolitan: 450 3rd Qu.:10331
## Eastern Victoria : 53 Max. :21650
## (Other) : 73datos.Num <- select(datos, Price, Rooms, Distance, Bedroom2, Bathroom, Car, Landsize, BuildingArea, YearBuilt, Propertycount)
head(datos.Num)## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 1 1480000 2 2.5 2 1 1 202 NA NA
## 2 1035000 2 2.5 2 1 0 156 79 1900
## 3 1465000 3 2.5 3 2 0 134 150 1900
## 4 850000 3 2.5 3 2 1 94 NA NA
## 5 1600000 4 2.5 3 1 2 120 142 2014
## 6 941000 2 2.5 2 1 0 181 NA NA
## Propertycount
## 1 4019
## 2 4019
## 3 4019
## 4 4019
## 5 4019
## 6 4019str(datos.Num)## 'data.frame': 13580 obs. of 10 variables:
## $ Price : num 1480000 1035000 1465000 850000 1600000 ...
## $ Rooms : int 2 2 3 3 4 2 3 2 1 2 ...
## $ Distance : num 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 ...
## $ Bedroom2 : num 2 2 3 3 3 2 4 2 1 3 ...
## $ Bathroom : num 1 1 2 2 1 1 2 1 1 1 ...
## $ Car : num 1 0 0 1 2 0 0 2 1 2 ...
## $ Landsize : num 202 156 134 94 120 181 245 256 0 220 ...
## $ BuildingArea : num NA 79 150 NA 142 NA 210 107 NA 75 ...
## $ YearBuilt : num NA 1900 1900 NA 2014 ...
## $ Propertycount: num 4019 4019 4019 4019 4019 ...mediana.BA <- median(datos.Num$BuildingArea, na.rm = TRUE)
mediana.YB <- median(datos.Num$YearBuilt, na.rm = TRUE)
mediana.C <- median(datos.Num$Car, na.rm = TRUE)
head(datos.Num, 10)## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 1 1480000 2 2.5 2 1 1 202 NA NA
## 2 1035000 2 2.5 2 1 0 156 79 1900
## 3 1465000 3 2.5 3 2 0 134 150 1900
## 4 850000 3 2.5 3 2 1 94 NA NA
## 5 1600000 4 2.5 3 1 2 120 142 2014
## 6 941000 2 2.5 2 1 0 181 NA NA
## 7 1876000 3 2.5 4 2 0 245 210 1910
## 8 1636000 2 2.5 2 1 2 256 107 1890
## 9 300000 1 2.5 1 1 1 0 NA NA
## 10 1097000 2 2.5 3 1 2 220 75 1900
## Propertycount
## 1 4019
## 2 4019
## 3 4019
## 4 4019
## 5 4019
## 6 4019
## 7 4019
## 8 4019
## 9 4019
## 10 4019datos.Num<- datos.Num %>%
mutate (BuildingArea = ifelse(is.na(BuildingArea), mediana.BA, BuildingArea))
datos.Num <- datos.Num %>%
mutate (YearBuilt = ifelse(is.na(YearBuilt), mediana.YB, YearBuilt))
datos.Num <- datos.Num %>%
mutate (Car = ifelse(is.na(Car), mediana.C, Car))
head(datos.Num, 10)## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 1 1480000 2 2.5 2 1 1 202 126 1970
## 2 1035000 2 2.5 2 1 0 156 79 1900
## 3 1465000 3 2.5 3 2 0 134 150 1900
## 4 850000 3 2.5 3 2 1 94 126 1970
## 5 1600000 4 2.5 3 1 2 120 142 2014
## 6 941000 2 2.5 2 1 0 181 126 1970
## 7 1876000 3 2.5 4 2 0 245 210 1910
## 8 1636000 2 2.5 2 1 2 256 107 1890
## 9 300000 1 2.5 1 1 1 0 126 1970
## 10 1097000 2 2.5 3 1 2 220 75 1900
## Propertycount
## 1 4019
## 2 4019
## 3 4019
## 4 4019
## 5 4019
## 6 4019
## 7 4019
## 8 4019
## 9 4019
## 10 4019#para la semilla
set.seed(2020) # Semilla
entrena <- createDataPartition(datos.Num$Price, p=0.7, list = FALSE)
head(entrena)## Resample1
## [1,] 1
## [2,] 3
## [3,] 4
## [4,] 5
## [5,] 7
## [6,] 9nrow(entrena)## [1] 9508head(datos.Num[-entrena,])## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 2 1035000 2 2.5 2 1 0 156 79 1900
## 6 941000 2 2.5 2 1 0 181 126 1970
## 8 1636000 2 2.5 2 1 2 256 107 1890
## 12 1350000 3 2.5 3 2 2 214 190 2005
## 13 750000 2 2.5 2 2 1 0 94 2009
## 20 890000 2 2.5 2 1 1 150 73 1985
## Propertycount
## 2 4019
## 6 4019
## 8 4019
## 12 4019
## 13 4019
## 20 4019nrow(datos.Num[-entrena,])## [1] 4072head(datos.Num)## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 1 1480000 2 2.5 2 1 1 202 126 1970
## 2 1035000 2 2.5 2 1 0 156 79 1900
## 3 1465000 3 2.5 3 2 0 134 150 1900
## 4 850000 3 2.5 3 2 1 94 126 1970
## 5 1600000 4 2.5 3 1 2 120 142 2014
## 6 941000 2 2.5 2 1 0 181 126 1970
## Propertycount
## 1 4019
## 2 4019
## 3 4019
## 4 4019
## 5 4019
## 6 4019datos.Entrena <- datos.Num[entrena,]
head(datos.Entrena)## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 1 1480000 2 2.5 2 1 1 202 126 1970
## 3 1465000 3 2.5 3 2 0 134 150 1900
## 4 850000 3 2.5 3 2 1 94 126 1970
## 5 1600000 4 2.5 3 1 2 120 142 2014
## 7 1876000 3 2.5 4 2 0 245 210 1910
## 9 300000 1 2.5 1 1 1 0 126 1970
## Propertycount
## 1 4019
## 3 4019
## 4 4019
## 5 4019
## 7 4019
## 9 4019summary(datos.Entrena)## Price Rooms Distance Bedroom2
## Min. : 85000 Min. : 1.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 650000 1st Qu.: 2.000 1st Qu.: 6.10 1st Qu.: 2.000
## Median : 903000 Median : 3.000 Median : 9.20 Median : 3.000
## Mean :1078063 Mean : 2.937 Mean :10.13 Mean : 2.911
## 3rd Qu.:1330000 3rd Qu.: 3.000 3rd Qu.:13.00 3rd Qu.: 3.000
## Max. :9000000 Max. :10.000 Max. :47.40 Max. :10.000
## Bathroom Car Landsize BuildingArea
## Min. :0.000 Min. : 0.000 Min. : 0.0 Min. : 0.0
## 1st Qu.:1.000 1st Qu.: 1.000 1st Qu.: 178.0 1st Qu.: 123.0
## Median :1.000 Median : 2.000 Median : 443.5 Median : 126.0
## Mean :1.529 Mean : 1.613 Mean : 579.6 Mean : 136.8
## 3rd Qu.:2.000 3rd Qu.: 2.000 3rd Qu.: 650.0 3rd Qu.: 129.0
## Max. :8.000 Max. :10.000 Max. :433014.0 Max. :6791.0
## YearBuilt Propertycount
## Min. :1830 Min. : 389
## 1st Qu.:1960 1st Qu.: 4386
## Median :1970 Median : 6567
## Mean :1967 Mean : 7453
## 3rd Qu.:1975 3rd Qu.:10331
## Max. :2018 Max. :21650datos.Valida <- datos.Num[-entrena,]
head(datos.Valida)## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 2 1035000 2 2.5 2 1 0 156 79 1900
## 6 941000 2 2.5 2 1 0 181 126 1970
## 8 1636000 2 2.5 2 1 2 256 107 1890
## 12 1350000 3 2.5 3 2 2 214 190 2005
## 13 750000 2 2.5 2 2 1 0 94 2009
## 20 890000 2 2.5 2 1 1 150 73 1985
## Propertycount
## 2 4019
## 6 4019
## 8 4019
## 12 4019
## 13 4019
## 20 4019summary(datos.Valida)## Price Rooms Distance Bedroom2
## Min. : 170000 Min. :1.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 649500 1st Qu.:2.000 1st Qu.: 6.20 1st Qu.: 2.000
## Median : 902500 Median :3.000 Median : 9.20 Median : 3.000
## Mean :1070130 Mean :2.941 Mean :10.15 Mean : 2.923
## 3rd Qu.:1330000 3rd Qu.:4.000 3rd Qu.:13.00 3rd Qu.: 3.000
## Max. :8000000 Max. :8.000 Max. :48.10 Max. :20.000
## Bathroom Car Landsize BuildingArea
## Min. :0.000 Min. : 0.00 Min. : 0.0 Min. : 0.0
## 1st Qu.:1.000 1st Qu.: 1.00 1st Qu.: 173.0 1st Qu.: 120.0
## Median :1.000 Median : 2.00 Median : 435.0 Median : 126.0
## Mean :1.547 Mean : 1.61 Mean : 508.9 Mean : 146.2
## 3rd Qu.:2.000 3rd Qu.: 2.00 3rd Qu.: 653.0 3rd Qu.: 130.0
## Max. :8.000 Max. :10.00 Max. :44500.0 Max. :44515.0
## YearBuilt Propertycount
## Min. :1196 Min. : 249
## 1st Qu.:1960 1st Qu.: 4217
## Median :1970 Median : 6543
## Mean :1967 Mean : 7457
## 3rd Qu.:1972 3rd Qu.:10331
## Max. :2017 Max. :21650set.seed(2020) # Semilla
arbol <- rpart(formula = Price ~ ., data = datos.Entrena)
arbol## n= 9508
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 9508 3.905176e+15 1078063.0
## 2) Rooms< 3.5 7161 1.741856e+15 931007.8
## 4) Rooms< 2.5 3023 3.911722e+14 724213.1
## 8) Landsize< 85.5 1214 6.222679e+13 559018.9 *
## 9) Landsize>=85.5 1809 2.735839e+14 835073.1 *
## 5) Rooms>=2.5 4138 1.126966e+15 1082081.0
## 10) Distance>=11.9 1576 2.382967e+14 832810.4 *
## 11) Distance< 11.9 2562 7.305050e+14 1235418.0
## 22) BuildingArea< 156.5 2230 5.283540e+14 1181265.0 *
## 23) BuildingArea>=156.5 332 1.516845e+14 1599162.0 *
## 3) Rooms>=3.5 2347 1.535971e+15 1526746.0
## 6) Distance>=11.45 1075 2.619142e+14 1136763.0 *
## 7) Distance< 11.45 1272 9.723911e+14 1856331.0
## 14) BuildingArea< 246.5 1025 5.968351e+14 1695057.0
## 28) Landsize< 708.5 803 3.644950e+14 1566809.0 *
## 29) Landsize>=708.5 222 1.713594e+14 2158948.0 *
## 15) BuildingArea>=246.5 247 2.382655e+14 2525584.0 *prp(arbol, type = 2, nn = TRUE,
fallen.leaves = TRUE, faclen = 4,
varlen = 8, shadow.col = "gray")
arbol$cptable## CP nsplit rel error xerror xstd
## 1 0.16064529 0 1.0000000 1.0001908 0.03335304
## 2 0.07724771 1 0.8393547 0.8397732 0.02940088
## 3 0.05728747 2 0.7621070 0.7627294 0.02731604
## 4 0.04050130 3 0.7048195 0.7055469 0.02633946
## 5 0.03515605 4 0.6643182 0.6669834 0.02651936
## 6 0.01561538 5 0.6291622 0.6395277 0.02549443
## 7 0.01417645 6 0.6135468 0.6211866 0.02488627
## 8 0.01292296 7 0.5993704 0.6079174 0.02482012
## 9 0.01000000 8 0.5864474 0.5990287 0.02442405plotcp(arbol)
arbol.Recortado <- prune(arbol, cp = 0.01417645)
prp(arbol.Recortado, type = 2, nn = TRUE,
fallen.leaves = TRUE, faclen = 4,
varlen = 8, shadow.col = "gray")
summary(datos.Valida)## Price Rooms Distance Bedroom2
## Min. : 170000 Min. :1.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 649500 1st Qu.:2.000 1st Qu.: 6.20 1st Qu.: 2.000
## Median : 902500 Median :3.000 Median : 9.20 Median : 3.000
## Mean :1070130 Mean :2.941 Mean :10.15 Mean : 2.923
## 3rd Qu.:1330000 3rd Qu.:4.000 3rd Qu.:13.00 3rd Qu.: 3.000
## Max. :8000000 Max. :8.000 Max. :48.10 Max. :20.000
## Bathroom Car Landsize BuildingArea
## Min. :0.000 Min. : 0.00 Min. : 0.0 Min. : 0.0
## 1st Qu.:1.000 1st Qu.: 1.00 1st Qu.: 173.0 1st Qu.: 120.0
## Median :1.000 Median : 2.00 Median : 435.0 Median : 126.0
## Mean :1.547 Mean : 1.61 Mean : 508.9 Mean : 146.2
## 3rd Qu.:2.000 3rd Qu.: 2.00 3rd Qu.: 653.0 3rd Qu.: 130.0
## Max. :8.000 Max. :10.00 Max. :44500.0 Max. :44515.0
## YearBuilt Propertycount
## Min. :1196 Min. : 249
## 1st Qu.:1960 1st Qu.: 4217
## Median :1970 Median : 6543
## Mean :1967 Mean : 7457
## 3rd Qu.:1972 3rd Qu.:10331
## Max. :2017 Max. :21650prediccion.price <- predict(arbol, newdata = datos.Valida)
# La predicción para la casa 1
datos.Valida[1,]## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 2 1035000 2 2.5 2 1 0 156 79 1900
## Propertycount
## 2 4019Rooms=c(1,1,3,5,4)
Distance=c(8,5,7,5,4)
Bedroom2=c(2,2,2,3,2)
Bathroom=c(2,1,2,2,1)
Car=c(2,2,3,1,1)
Landsize=c(700,350,420,560,550)
BuildingArea=c(90,210,200,110,132)
YearBuilt=c(1970,1984,1997,2015,1987)
Propertycount=c(7000,2220,7800,3500,4700)
nuevo.Dato <- data.frame(Rooms, Distance, Bedroom2, Bathroom, Car, Landsize, BuildingArea, YearBuilt,
Propertycount)
colnames(nuevo.Dato) <- c( "Rooms", "Distance", "Bedroom2", "Bathroom", "Car", "Landsize", "BuildingArea", "YearBuilt", "Propertycount")
nuevo.Dato## Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 1 1 8 2 2 2 700 90 1970
## 2 1 5 2 1 2 350 210 1984
## 3 3 7 2 2 3 420 200 1997
## 4 5 5 3 2 1 560 110 2015
## 5 4 4 2 1 1 550 132 1987
## Propertycount
## 1 7000
## 2 2220
## 3 7800
## 4 3500
## 5 4700prediccion.price <- predict(arbol, newdata = nuevo.Dato)
# La predicción para la casa 1
datos.Valida[1,]## Price Rooms Distance Bedroom2 Bathroom Car Landsize BuildingArea YearBuilt
## 2 1035000 2 2.5 2 1 0 156 79 1900
## Propertycount
## 2 4019prediccion.price[1]## 1
## 835073.1