Classification Model -Tree

Dataset <- read.csv(file.choose(),header = TRUE)
Data<-Dataset
dataset = dataset[3:5]

# Encoding the target feature as factor
Data$Outcome = factor(Data$Outcome, levels = c(0, 1))
Data$Outcome = factor(Data$Outcome,
                         levels = c('Paid', 'Defaulted'),
                         labels = c(0, 1))
library(caTools)
set.seed(123)
split = sample.split(Data$Outcome, SplitRatio = 0.80)
training_set = subset(Data, split == TRUE)
test_set = subset(Data, split == FALSE)
training_set[-3] = scale(training_set[-3])
test_set[-3] = scale(test_set[-3])

# Fitting Decision Tree Classification to the Training set
library(rpart)
classifier = rpart(formula = Outcome ~ .,
                   data = training_set)
summary (classifier)

Predictor = predict(classifier, newdata = test_set[-11], type = 'class')
Predictor

cm = table(test_set[, 11], Predictor)
cm

Summary(Predictor)
printcp(classifier)
printcp(Predictor)
plotcp(classifier)
summary(classifier)
summary(cm)
plot(classifier)

plot(classifier, uniform=TRUE, 
     main="Classification Tree Customers")
text(classifier, use.n=TRUE, all=TRUE, cex=.8)

# Plotting the tree
plot(classifier)
plot(classifier, uniform=TRUE, 
     main="Classification Tree Customers")
text(classifier)
plot(Predictor, uniform=TRUE, 
     main="Classification Tree Customers")
text(Predictor, use.n=TRUE, all=TRUE, cex=.8)
plot(classifier, uniform=TRUE, 
     main="Classification Tree Customers")
text(classifier, use.n=TRUE, all=TRUE, cex=.8)