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Credit Bankruptcy.R
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#Libraries
library(aod)
library(caret)
library(caTools)
library(dplyr)
library(ggplot2)
library(ISLR)
library(mlbench)
library(plyr)
library(readr)
library(stats)
library(MASS)
library(rpart)
library(rpart.plot)
--------------------------------------------------------------------------------
#Loading the dataset
library(readxl)
credit <- read_excel("C:/Users/kheni/OneDrive/Desktop/credit.xlsx")
View(credit)
#Checking dimensions and summary of data
dim(credit) # 1000 * 17
summary(credit)
names(credit)
#Factoring 'Default' variable
credit$default <- as.factor(credit$default)
credit$default
#Combining the columns which makes sense
columns <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17)
credit_updated <- credit[,columns]
credit_updated
dim(credit_updated) # 1000 * 17
--------------------------------------------------------------------------------
#Data Splitting (Training and Testing)
set.seed(1000)
row.number = sample(1:nrow(credit_updated), 0.8*nrow(credit_updated))
train = credit_updated[row.number,]
test = credit_updated[-row.number,]
dim(train) # 800 * 9
dim(test) # 200 * 9
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
# LOGISTIC REGRESSION
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_glm = train(default ~ .,
data = train,
method = "glm",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_glm
glm_predict <- predict(model_glm ,newdata = test)
confusionMatrix(glm_predict, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_glm_1 = train(default ~ checking_balance + months_loan_duration + credit_history + savings_balance + amount + employment_duration + percent_of_income + other_credit + years_at_residence + amount/months_loan_duration + age + + housing + purpose + existing_loans_count, data = train,
method = "glm",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_glm_1
glm_predict_1 <- predict(model_glm_1 ,newdata = test)
confusionMatrix(glm_predict_1, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_glm_2 = train(default ~. -amount -employment_duration -job,
data = train,
method = "glm",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_glm_2
glm_predict_2 <- predict(model_glm_2 ,newdata = test)
confusionMatrix(glm_predict_2, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
# LINEAR DISCRIMINANT ANALYSIS (LDA)
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_lda = train(default ~ .,
data = train,
method = "lda",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_lda
lda_predict <- predict(model_lda ,newdata = test)
confusionMatrix(lda_predict, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_lda_1 = train(default ~ checking_balance + months_loan_duration + credit_history + savings_balance + amount + employment_duration + percent_of_income + other_credit + years_at_residence + amount/months_loan_duration + age + + housing + purpose + existing_loans_count, data = train,
method = "lda",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_lda_1
lda_predict_1 <- predict(model_lda_1 ,newdata = test)
confusionMatrix(lda_predict_1, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_lda_2 = train(default ~ checking_balance + months_loan_duration + credit_history + savings_balance + amount + employment_duration + percent_of_income + other_credit + + amount/months_loan_duration + age + + housing + purpose + existing_loans_count,
data = train,
method = "lda",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_lda_2
lda_predict_2 <- predict(model_lda_2 ,newdata = test)
confusionMatrix(lda_predict_2, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_lda_3 = train(default ~ . -amount -employment_duration -job +amount/months_loan_duration -phone -age -years_at_residence,
data = train,
method = "lda",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_lda_3
lda_predict_3 <- predict(model_lda_3 ,newdata = test)
confusionMatrix(lda_predict_3, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
# Quadratic Discriminant Analysis (QDA)
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_qda_1 = train(default ~.,
data = train,
method = "qda",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_qda_1
qda_predict_1 <- predict(model_qda_1 ,newdata = test)
confusionMatrix(qda_predict_1, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_qda_2 = train(default ~ checking_balance + months_loan_duration + credit_history + savings_balance + amount + employment_duration + percent_of_income + other_credit + amount/months_loan_duration + age + + housing + purpose + existing_loans_count,
data = train,
method = "qda",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_qda_2
qda_predict_2 <- predict(model_qda_2 ,newdata = test)
confusionMatrix(qda_predict_2, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_qda_3 = train(default ~. -amount -job -credit_history -percent_of_income -years_at_residence,
data = train,
method = "qda",
family = "binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_qda_3
qda_predict_3 <- predict(model_qda_3 ,newdata = test)
confusionMatrix(qda_predict_3, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
# K Nearest Neighbor (KNN)
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_knn_1 <- train(default ~ checking_balance + months_loan_duration + credit_history + savings_balance + amount + employment_duration + percent_of_income + other_credit + amount/months_loan_duration + age + + housing + purpose + existing_loans_count,
data = train,
method = "knn",
#family="binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_knn_1
plot(model_knn_1)
knn_predict_1 <- predict(model_knn_1, newdata = test)
confusionMatrix(knn_predict_1, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_knn_2 <- train(default ~. -amount -job -years_at_residence +amount/months_loan_duration -purpose,
data = train,
method = "knn",
#family="binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_knn_2
knn_predict_2 <- predict(model_knn_2, newdata = test)
confusionMatrix(knn_predict_2, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
# Decision Tree
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_tree_1 <- train(default ~ .,
data = train,
method = "rpart",
#family="binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_tree_1
tree_predict_1 <- predict(model_tree_1, newdata = test)
confusionMatrix(tree_predict_1, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_tree_2 <- train(default ~ checking_balance + months_loan_duration + credit_history + savings_balance + amount + employment_duration + percent_of_income + other_credit + amount/months_loan_duration + age + + housing + purpose + existing_loans_count,
data = train,
method = "rpart",
#family="binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_tree_2
tree_predict_2 <- predict(model_tree_2, newdata = test)
confusionMatrix(tree_predict_2, test$default, mode = 'prec_recall', positive = 'yes')
--------------------------------------------------------------------------------
ctrl <- trainControl(method = "cv", number = 10, savePredictions = TRUE)
model_tree_2 <- train(default ~. -amount -job -years_at_residence -percent_of_income, data = train,
method = "rpart",
#family="binomial",
metric = "Accuracy",
trControl = ctrl,
preProcess = c("center","scale"),
tuneLength = 20)
model_tree_2
tree_predict_2 <- predict(model_tree_2, newdata = test)
confusionMatrix(tree_predict_2, test$default, mode = 'prec_recall', positive = 'yes')
tree<- rpart(default ~. -amount -job -years_at_residence -percent_of_income, data = train, method = 'class')
rpart.plot(tree, extra = 106)