ls()
## character(0)
rm(list = ls())
library(dplyr)
## Warning: 패키지 'dplyr'는 R 버전 4.1.3에서 작성되었습니다
##
## 다음의 패키지를 부착합니다: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
read.delim("titanic3.txt",header=TRUE,sep=',')->full
full %>% glimpse
## Rows: 1,309
## Columns: 14
## $ pclass <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
## $ survived <int> 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, ~
## $ name <chr> "Allen, Miss. Elisabeth Walton", "Allison, Master. Hudson Tr~
## $ sex <chr> "female", "male", "female", "male", "female", "male", "femal~
## $ age <dbl> 29.00, 0.92, 2.00, 30.00, 25.00, 48.00, 63.00, 39.00, 53.00,~
## $ sibsp <int> 0, 1, 1, 1, 1, 0, 1, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ parch <int> 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, ~
## $ ticket <chr> "24160", "113781", "113781", "113781", "113781", "19952", "1~
## $ fare <dbl> 211.3375, 151.5500, 151.5500, 151.5500, 151.5500, 26.5500, 7~
## $ cabin <chr> "B5", "C22 C26", "C22 C26", "C22 C26", "C22 C26", "E12", "D7~
## $ embarked <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "C", "C", "C", ~
## $ boat <chr> "2", "11", "", "", "", "3", "10", "", "D", "", "", "4", "9",~
## $ body <int> NA, NA, NA, 135, NA, NA, NA, NA, NA, 22, 124, NA, NA, NA, NA~
## $ home.dest <chr> "St Louis, MO", "Montreal, PQ / Chesterville, ON", "Montreal~
library(caret)
## 필요한 패키지를 로딩중입니다: ggplot2
## 필요한 패키지를 로딩중입니다: lattice
set.seed(123)
train_list<-createDataPartition(y=full$survived,p=0.7,list=FALSE)
full_train<-full[train_list,]
full_test<-full[-train_list,]
NROW(full_train)
## [1] 917
train<-full_train
test<-full_test
train %>% mutate(index='train')->train
test %>% mutate(index='test')->test
bind_rows(train,test)->full
full %>% select(-boat,-body,-home.dest)->full
full %>% glimpse
## Rows: 1,309
## Columns: 12
## $ pclass <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
## $ survived <int> 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1~
## $ name <chr> "Allison, Master. Hudson Trevor", "Allison, Miss. Helen Lorai~
## $ sex <chr> "male", "female", "female", "male", "male", "female", "male",~
## $ age <dbl> 0.92, 2.00, 25.00, 48.00, 39.00, 53.00, 71.00, 47.00, 24.00, ~
## $ sibsp <int> 1, 1, 1, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0~
## $ parch <int> 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ ticket <chr> "113781", "113781", "113781", "19952", "112050", "11769", "PC~
## $ fare <dbl> 151.5500, 151.5500, 151.5500, 26.5500, 0.0000, 51.4792, 49.50~
## $ cabin <chr> "C22 C26", "C22 C26", "C22 C26", "E12", "A36", "C101", "", "C~
## $ embarked <chr> "S", "S", "S", "S", "S", "S", "C", "C", "C", "S", "C", "C", "~
## $ index <chr> "train", "train", "train", "train", "train", "train", "train"~
full$survived<-ifelse(full$survived==0,"생존","사망")
full$survived<-as.factor(full$survived)
table(full$embarked)
##
## C Q S
## 2 270 123 914
summary(is.na(full))
## pclass survived name sex
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:1309 FALSE:1309 FALSE:1309 FALSE:1309
##
## age sibsp parch ticket
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:1046 FALSE:1309 FALSE:1309 FALSE:1309
## TRUE :263
## fare cabin embarked index
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:1308 FALSE:1309 FALSE:1309 FALSE:1309
## TRUE :1
full$pclass<-as.factor(full$pclass)
full$sex<-as.factor(full$sex)
full$embarked<-as.factor(full$embarked)
#결치값 확인가능
summary(full)
## pclass survived name sex age
## 1:323 사망:500 Length:1309 female:466 Min. : 0.17
## 2:277 생존:809 Class :character male :843 1st Qu.:21.00
## 3:709 Mode :character Median :28.00
## Mean :29.88
## 3rd Qu.:39.00
## Max. :80.00
## NA's :263
## sibsp parch ticket fare
## Min. :0.0000 Min. :0.000 Length:1309 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.000 Class :character 1st Qu.: 7.896
## Median :0.0000 Median :0.000 Mode :character Median : 14.454
## Mean :0.4989 Mean :0.385 Mean : 33.295
## 3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.: 31.275
## Max. :8.0000 Max. :9.000 Max. :512.329
## NA's :1
## cabin embarked index
## Length:1309 : 2 Length:1309
## Class :character C:270 Class :character
## Mode :character Q:123 Mode :character
## S:914
##
##
##
full %>% glimpse
## Rows: 1,309
## Columns: 12
## $ pclass <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
## $ survived <fct> 사망, 생존, 생존, 사망, 생존, 사망, 생존, 생존, 사망, 생존, ~
## $ name <chr> "Allison, Master. Hudson Trevor", "Allison, Miss. Helen Lorai~
## $ sex <fct> male, female, female, male, male, female, male, male, female,~
## $ age <dbl> 0.92, 2.00, 25.00, 48.00, 39.00, 53.00, 71.00, 47.00, 24.00, ~
## $ sibsp <int> 1, 1, 1, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0~
## $ parch <int> 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ ticket <chr> "113781", "113781", "113781", "19952", "112050", "11769", "PC~
## $ fare <dbl> 151.5500, 151.5500, 151.5500, 26.5500, 0.0000, 51.4792, 49.50~
## $ cabin <chr> "C22 C26", "C22 C26", "C22 C26", "E12", "A36", "C101", "", "C~
## $ embarked <fct> S, S, S, S, S, S, C, C, C, S, C, C, C, C, C, C, S, C, C, C, S~
## $ index <chr> "train", "train", "train", "train", "train", "train", "train"~
levels(full$embarked)[1]<-NA
table(full$embarked,useNA="always")
##
## C Q S <NA>
## 270 123 914 2
full %>% filter(!is.na(age)&!is.na(fare)&!is.na(embarked))->full
colSums(is.na(full))
## pclass survived name sex age sibsp parch ticket
## 0 0 0 0 0 0 0 0
## fare cabin embarked index
## 0 0 0 0
levels(full$embarked)
## [1] "C" "Q" "S"
library(recipes)
## Warning: 패키지 'recipes'는 R 버전 4.1.3에서 작성되었습니다
##
## 다음의 패키지를 부착합니다: 'recipes'
## The following object is masked from 'package:stats':
##
## step
recipe(survived~.,data=full) %>% step_YeoJohnson(age,sibsp,parch,fare) %>%
step_center(age,sibsp,parch,fare) %>%
step_scale(age,sibsp,parch,fare) %>%
prep() %>% juice()->data
data %>% glimpse
## Rows: 1,043
## Columns: 12
## $ pclass <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
## $ name <fct> "Allison, Master. Hudson Trevor", "Allison, Miss. Helen Lorai~
## $ sex <fct> male, female, female, male, male, female, male, male, female,~
## $ age <dbl> -2.373513978, -2.236510138, -0.271313496, 1.227445945, 0.6675~
## $ sibsp <dbl> 1.2828274, 1.2828274, 1.2828274, -0.7239369, -0.7239369, 1.59~
## $ parch <dbl> 1.7467662, 1.7467662, 1.7467662, -0.6019626, -0.6019626, -0.6~
## $ ticket <fct> 113781, 113781, 113781, 19952, 112050, 11769, PC 17609, PC 17~
## $ fare <dbl> 1.8357480, 1.8357480, 1.8357480, 0.3451848, -4.3263865, 0.962~
## $ cabin <fct> C22 C26, C22 C26, C22 C26, E12, A36, C101, , C62 C64, B35, B5~
## $ embarked <fct> S, S, S, S, S, S, C, C, C, C, C, C, C, C, C, S, C, C, C, S, S~
## $ index <fct> train, train, train, train, train, train, train, train, train~
## $ survived <fct> 사망, 생존, 생존, 사망, 생존, 사망, 생존, 생존, 사망, 생존, ~
full %>% filter(index=="train") %>% select(-index,-name,-ticket,-cabin)->train
full %>% filter(index=="test") %>% select(-index,-name,-ticket,-cabin)->test
ctrl<-trainControl(method="cv",summaryFunction = twoClassSummary,
classProbs = TRUE)
train(survived~.,data=train,
method="rpart",metric='ROC',
trControl=ctrl)->rffit
rffit
## CART
##
## 722 samples
## 7 predictor
## 2 classes: '사망', '생존'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 650, 650, 649, 649, 650, 650, ...
## Resampling results across tuning parameters:
##
## cp ROC Sens Spec
## 0.02678571 0.7978806 0.5392857 0.9637879
## 0.06071429 0.7698368 0.5678571 0.8956061
## 0.41428571 0.6037734 0.3071429 0.9004040
##
## ROC was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.02678571.
predict(rffit,test,type="prob")->rffit1
predict(rffit,test,type="raw")->rffit2
head(rffit2)
## [1] 사망 생존 사망 사망 사망 생존
## Levels: 사망 생존
head(rffit1)
## 사망 생존
## 1 0.9079755 0.09202454
## 2 0.1951754 0.80482456
## 3 0.9079755 0.09202454
## 4 0.9079755 0.09202454
## 5 0.9079755 0.09202454
## 6 0.1951754 0.80482456
levels(test$survived)
## [1] "사망" "생존"
test$survived<-as.factor(test$survived)
confusionMatrix(rffit2,test$survived)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 사망 생존
## 사망 70 1
## 생존 75 175
##
## Accuracy : 0.7632
## 95% CI : (0.7129, 0.8087)
## No Information Rate : 0.5483
## P-Value [Acc > NIR] : 1.115e-15
##
## Kappa : 0.4995
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.4828
## Specificity : 0.9943
## Pos Pred Value : 0.9859
## Neg Pred Value : 0.7000
## Prevalence : 0.4517
## Detection Rate : 0.2181
## Detection Prevalence : 0.2212
## Balanced Accuracy : 0.7385
##
## 'Positive' Class : 사망
##