#titanic sink

#get train data

train_data<-read.csv('C:\\NotBackedUp\\fu\\temp\\Titanic Sink\\data\\train.csv')

#pick vip attributes

train_data.pick <- train_data

train_data.pick

train_data.pick$PassengerId = NULL

train_data.pick$Name = NULL

train_data.pick$Embarked = NULL

train_data.pick$Ticket = NULL

train_data.pick$Fare = NULL

names(train_data.pick)

##clean data

summary(train_data.pick$Survived) #1=yes 0=no

summary(train_data.pick$Pclass)

#female -> 1, male -> 2

summary(train_data.pick$Sex)

v = NULL

for ( i in 1:nrow(train_data.pick)) {

#print(train_data.pick$Sex[i])

if(train_data.pick$Sex[i]=='male'){

v <- c(v,1)

}

else{

v <- c(v,2)

}

}

train_data.pick['nSex'] = v

train_data.pick$Sex =NULL

names(train_data.pick)

summary(train_data.pick$nSex)

summary(train_data.pick$Age)

#na

t_mean<-mean(na.omit(train_data.pick$Age) )

for (i in 1:nrow(train_data.pick)){

#print(is.na(train_data.pick$Age[i]))

#print(i)

if(is.na(train_data.pick$Age[i])==TRUE){

train_data.pick$Age[i]<-t_mean

}

}

summary(train_data.pick$Age)

summary(train_data.pick$SibSp)

summary(train_data.pick$Parch)

#Cabin to number

summary(train_data.pick$Cabin)

nCabin <- NULL

for (i in 1:nrow(train_data.pick)){

t<-as.character(train_data.pick$Cabin[i])

contain_alpha <- grepl("[[:alpha:]]",t)

if ( contain_alpha ){

alpha_t <- gsub("\\d+|NA", "", t)

code_t <- utf8ToInt(substr(gsub("\\d+|NA", "", t),1,1))-64

}else {

code_t <- 0

}

nCabin <- c(nCabin,code_t)

}

train_data.pick["nCabin"] <-nCabin

train_data.pick$Cabin<-NULL

#SUPPORT VECTOR MACHINE#

library(e1071)#svm

#learning from training

svmmodel<-svm(as.factor(Survived)~., data=train_data.pick, method="C-classification", kernel="radial",cross=5, probability=TRUE)

#get test data

test_data<-read.csv('C:\\NotBackedUp\\fu\\temp\\Titanic Sink\\data\\test.csv')

#pick vip attributes

test_data.pick <- test_data

test_data.pick$PassengerId = NULL

test_data.pick$Name = NULL

test_data.pick$Embarked = NULL

test_data.pick$Ticket = NULL

test_data.pick$Fare = NULL

names(test_data.pick)

##clean data

summary(test_data.pick$Survived)

summary(test_data.pick$Pclass)

#female -> 1, male -> 2

summary(test_data.pick$Sex)

v = NULL

for ( i in 1:nrow(test_data.pick)) {

#print(train_data.pick$Sex[i])

if(test_data.pick$Sex[i]=='male'){

v <- c(v,1)

}

else{

v <- c(v,2)

}

}

test_data.pick['nSex'] = v

test_data.pick$Sex =NULL

names(test_data.pick)

summary(test_data.pick$nSex)

summary(test_data.pick$Age)

#na

t_mean<-mean(na.omit(test_data.pick$Age) )

for (i in 1:nrow(test_data.pick)){

if(is.na(test_data.pick$Age[i])==TRUE){

test_data.pick$Age[i]<-t_mean

}

}

summary(test_data.pick$Age)

summary(test_data.pick$SibSp)

summary(test_data.pick$Parch)

#Cabin to number

summary(test_data.pick$Cabin)

nCabin <- NULL

for (i in 1:nrow(test_data.pick)){

t<-as.character(test_data.pick$Cabin[i])

contain_alpha <- grepl("[[:alpha:]]",t)

if ( contain_alpha ){

alpha_t <- gsub("\\d+|NA", "", t)

code_t <- utf8ToInt(substr(gsub("\\d+|NA", "", t),1,1))-64

}else {

code_t <- 0

}

nCabin <- c(nCabin,code_t)

}

test_data.pick["nCabin"] <-nCabin

test_data.pick$Cabin<-NULL

test_data.pick$nCabin

#predicting the test data

svmmodel.predict<-predict(svmmodel,test_data.pick,decision.values=TRUE)

svmmodel.probs<-attr(svmmodel.predict,"decision.values")

svmmodel.class<-svmmodel.predict[1:nrow(test_data.pick)]

 

svmmodel.truth

svmmodel.class

#analyzing result

truth<-read.csv('C:\\NotBackedUp\\fu\\temp\\Titanic Sink\\data\\gender_submission.csv')

svmmodel.truth<-truth$Survived

library(SDMTools)#confusion

svmmodel.confusion<-confusion.matrix(svmmodel.truth,svmmodel.class)

svmmodel.accuracy<-prop.correct(svmmodel.confusion) #/t

svmmodel.recall <- #tf/t

svmmodel.confusion

#roc analysis for test data

svmmodel.prediction<-prediction(svmmodel.probs,svmmodel.labels)

svmmodel.performance<-performance(svmmodel.prediction,"tpr","fpr")

svmmodel.auc<-performance(svmmodel.prediction,"auc")@y.values[[1]]

 

final_result<-test_data[1:2]

final_result['Survived']<-svmmodel.class

final_result$Pclass<-NULL

 

write.csv(final_result,quote = FALSE,

"C:\\NotBackedUp\\fu\\temp\\Titanic Sink\\data\\my_submission.csv",

row.names=FALSE

)