Arbol_de_decision-Titanic
Dana Perez - A00227041
9/7/2022
Importar base de datos
#file.choose()
base_de_datos <- read.csv("/Users/dannaperez/Desktop/R/bases de datos/titanic.csv")Entender base de datos
summary(base_de_datos)## pclass survived name sex
## Min. :1.000 Min. :0.000 Length:1309 Length:1309
## 1st Qu.:2.000 1st Qu.:0.000 Class :character Class :character
## Median :3.000 Median :0.000 Mode :character Mode :character
## Mean :2.295 Mean :0.382
## 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :3.000 Max. :1.000
##
## age sibsp parch ticket
## Min. : 0.1667 Min. :0.0000 Min. :0.000 Length:1309
## 1st Qu.:21.0000 1st Qu.:0.0000 1st Qu.:0.000 Class :character
## Median :28.0000 Median :0.0000 Median :0.000 Mode :character
## Mean :29.8811 Mean :0.4989 Mean :0.385
## 3rd Qu.:39.0000 3rd Qu.:1.0000 3rd Qu.:0.000
## Max. :80.0000 Max. :8.0000 Max. :9.000
## NA's :263
## fare cabin embarked boat
## Min. : 0.000 Length:1309 Length:1309 Length:1309
## 1st Qu.: 7.896 Class :character Class :character Class :character
## Median : 14.454 Mode :character Mode :character Mode :character
## Mean : 33.295
## 3rd Qu.: 31.275
## Max. :512.329
## NA's :1
## body home.dest
## Min. : 1.0 Length:1309
## 1st Qu.: 72.0 Class :character
## Median :155.0 Mode :character
## Mean :160.8
## 3rd Qu.:256.0
## Max. :328.0
## NA's :1188library(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, unionFiltrar base de datos
titanic <- base_de_datos[,c("pclass", "age", "sex", "survived")]
titanic$survived <- as.factor (ifelse(titanic$survived==0,"murio","sobrevivio"))
titanic$i_pclass <- as.factor(titanic$pclass)
titanic$sex <- as.factor(titanic$sex)
str(titanic)## 'data.frame': 1309 obs. of 5 variables:
## $ pclass : int 1 1 1 1 1 1 1 1 1 1 ...
## $ age : num 29 0.917 2 30 25 ...
## $ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ...
## $ survived: Factor w/ 2 levels "murio","sobrevivio": 2 2 1 1 1 2 2 1 2 1 ...
## $ i_pclass: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...sum(is.na(titanic))## [1] 263titanic <- na.omit(titanic)Crear arbol de decision
library(rpart)
arbol <- rpart(formula = survived ~ ., data = titanic)
arbol## n= 1046
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1046 427 murio (0.59177820 0.40822180)
## 2) sex=male 658 135 murio (0.79483283 0.20516717)
## 4) age>=9.5 615 110 murio (0.82113821 0.17886179) *
## 5) age< 9.5 43 18 sobrevivio (0.41860465 0.58139535)
## 10) pclass>=2.5 29 11 murio (0.62068966 0.37931034) *
## 11) pclass< 2.5 14 0 sobrevivio (0.00000000 1.00000000) *
## 3) sex=female 388 96 sobrevivio (0.24742268 0.75257732)
## 6) pclass>=2.5 152 72 murio (0.52631579 0.47368421)
## 12) age>=1.5 145 66 murio (0.54482759 0.45517241) *
## 13) age< 1.5 7 1 sobrevivio (0.14285714 0.85714286) *
## 7) pclass< 2.5 236 16 sobrevivio (0.06779661 0.93220339) *#install.packages("rpart.plot")
library(rpart.plot)
rpart.plot(arbol)
prp(arbol,extra=7,prefix="fraccion/n")
Conclusion
El árbol de decisiones es un tipo de modelo de predicción que en este caso nos ayuda a observar las probabilidades que tuvo cada personaje de acuerdo en sus variaciones en sus características como lo son: edas, sexo y en la clase en la que viajaba.