install.packages("devtools", dependencies = TRUE)
library(devtools)
devtools::install_github("ayhandis/creditR")
library(creditR)
ls("package:creditR")
#Model data and data structure
data("germancredit")
str(germancredit)
#Preparing a sample data set

sample_data <- germancredit[,c("duration.in.month","credit.amount","installment.rate.in.percentage.of.disposable.income", "age.in.years","creditability")]

#Converting the ‘Creditability’ (default flag) variable into numeric type
sample_data$creditability <- ifelse(sample_data$creditability == "bad",1,0)

#Calculating the missing ratios
missing_ratio(sample_data)
#Splitting the data into train and test sets
traintest <- train_test_split(sample_data,123,0.70)
train <- traintest$train
test <- traintest$test

#Applying WOE transformation on the variables
woerules <- woe.binning(df = train,target.var = "creditability",pred.var = train,event.class = 1)
train_woe <- woe.binning.deploy(train, woerules, add.woe.or.dum.var='woe')

#Creating a dataset with the transformed variables and default flag
train_woe <- woe.get.clear.data(train_woe,default_flag = "creditability",prefix = "woe")

#Applying the WOE rules used on the train data to the test data
test_woe <- woe.binning.deploy(test, woerules, add.woe.or.dum.var='woe')
test_woe <- woe.get.clear.data(test_woe,default_flag = "creditability",prefix = "woe")

#Performing the IV and Gini calculations for the whole data set
IV.calc.data(train_woe,"creditability")
Gini.univariate.data(train_woe,"creditability")

#Creating a new dataset by Gini elimination. IV elimination is also possible
eliminated_data <- Gini_elimination(train_woe,"creditability",0.10)
str(eliminated_data)

#A demonstration of the functions useful in performing Clustering
clustering_data <- variable.clustering(eliminated_data,"creditability", 2)
clustering_data

# Returns the data for variables that have the maximum gini value in the dataset
selected_data <- variable.clustering.gini(eliminated_data,"creditability", 2)

correlation.cluster(eliminated_data,clustering_data,variables = "variable",clusters = "Group")


#Creating a logistic regression model of the data
model= glm(formula = creditability ~ ., family = binomial(link = "logit"),  data = eliminated_data)
summary(model)


#Calculating variable weights
woe.glm.feature.importance(eliminated_data,model,"creditability")




#Generating the PD values for the train and test data
ms_train_data <- cbind(eliminated_data,model$fitted.values)
ms_test_data <- cbind(test_woe[,colnames(eliminated_data)], predict(model,type = "response", newdata = test_woe))
colnames(ms_train_data) <- c("woe.duration.in.month.binned","woe.age.in.years.binned","woe.installment.rate.in.percentage.of.disposable.income.binned","creditability","PD")
colnames(ms_test_data) <- c("woe.duration.in.month.binned","woe.age.in.years.binned","woe.installment.rate.in.percentage.of.disposable.income.binned","creditability","PD")