german

# This code will install required packages if they are not already installed
# ALWAYS INSTALL YOUR PACKAGES LIKE THIS!
# install packages
if (!require("tidyverse")) {
  install.packages("tidyverse")
  library(tidyverse)
}

if (!require("knitr")) {
  install.packages("knitr")
  library(knitr)
}

if(!require("digest")){
  install.packages("digest")
  library("digest")
}

if (!require("ggplot2")) {
   install.packages("ggplot2")
   library(ggplot2)
}

# use for lda
if (!require(MASS)){
  install.packages("MASS")
  library(MASS)
}

# use for plot roc curve
if (!require(pROC)){
  install.packages("pROC")
  library(pROC)
}

# use for xgboost
if (!require("xgboost")) {
   install.packages("xgboost", dependencies = TRUE)
   library(xgboost)
}

# use for svm
if (!require("e1071")) {
   install.packages("e1071")
   library(e1071)
}

# use for Naive Bayes
if(!require("naivebayes")){
  install.packages("naivebayes")
  library(naivebayes)
}

# use for Random Forest
if(!require("randomForest")){
  install.packages("randomForest")
  library("randomForest")
}


#dataset
set.seed(100)

# Load the Data 
creditdata <- read.csv("~/LLM_Fairness_Fall_2023/StudentNotebooks/FinalReport/statlog_german_credit_data.csv", header = TRUE)

# Print the first few rows
head(creditdata)


# Split the dataset into training and test sets
train_indices <- sample(1:nrow(creditdata), 0.9 * nrow(creditdata))
train <- creditdata[train_indices, ]
test <- creditdata[-train_indices, ]

# Display the dimensions of the training and test sets
cat("Training set size:", nrow(train), "\n")
cat("Test set size:", nrow(test), "\n")