#------------------
# Data Preparation
#------------------

#Read datasets
#Download the data from http://www.saedsayad.com/datasets/CreditData.zip
train <- read.csv("Credit_train.csv")
test <- read.csv("Credit_test.csv")

#Rows and Cols
dim(train)
dim(test)

#Columns name
colnames(train)
colnames(test)

#Show  
head(train)
head(test)




#------------------------------------------
# Support Vector Machines - Classification
#------------------------------------------
library(caret)
library(e1071)
library(AUC)

#remove all records with missing values
train <- na.omit(train)
test <- na.omit(test)

#train
model.SVM <- svm(DEFAULT~., train, kernel ='linear')
summary(model.SVM)

#confusion matrix
pc <- NULL
pc <- predict(model.SVM, test, type = "class")
xtab <- table(pc, test$DEFAULT)
caret::confusionMatrix(xtab, positive="Y")

#lift chart
pb <- NULL
pb <- predict(model.SVM, test, type="raw")
pb <- as.data.frame(pb)
pred.SVM <- data.frame(test$DEFAULT, pb)
colnames(pred.SVM) <- c("target","score")
lift.SVM <- lift(target ~ score, data = pred.SVM, cuts=10, class="Y")
xyplot(lift.SVM, main="SVM - Lift Chart", type=c("l","g"), lwd=2
       ,scales=list(x=list(alternating=FALSE,tick.number = 10)
                     ,y=list(alternating=FALSE,tick.number = 10)))


#roc chart
labels <- as.factor(ifelse(pred.SVM$target=="Y", 1, 0))
predictions <- pred.SVM$score
auc(roc(predictions, labels), min = 0, max = 1)
plot(roc(predictions, labels), min=0, max=1, type="l", main="SVM - ROC Chart")