Variant V Analysis.Rmd

---
title: "Big Data Project - Variant V"
output: html_document
date: "2023-05-11"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(ISLR2)
library(MASS)
library(tidyverse)
library(dplyr)
library(glmnet)
library(leaps)
library(FNN)
library(caret)
library(ROSE)
```

## Load in data and clean

```{r, warning=FALSE}
rm(list=ls())
var5=read_csv("Variant V.csv")

# Fill -1 with NAs where appropriate
var5clean=var5 %>%
  mutate(prev_address_months_count = na_if(prev_address_months_count, -1),
  current_address_months_count = na_if(current_address_months_count, -1),
  bank_months_count = na_if(bank_months_count, -1)) # Replacing -1 with NA

# Mutate new variables for missing info on prev_address_months_count and bank_months_na
var5clean=mutate_each(var5clean, funs(replace(., is.na(.), 0)), prev_address_months_count)
var5clean=mutate_each(var5clean, funs(replace(., is.na(.), 0)), bank_months_count)
var5clean=var5clean%>%
  mutate(prev_address_na=case_when(prev_address_months_count==0 ~ 1),
         bank_months_na=case_when(bank_months_count==0 ~ 1))
var5clean=mutate_each(var5clean, funs(replace(., is.na(.), 0)), prev_address_na)
var5clean=mutate_each(var5clean, funs(replace(., is.na(.), 0)), bank_months_na)

var5clean=na.omit(var5clean) # Drop last few cases with missing values
```

## Create training and test sets

```{r}
set.seed(125)

index = sample(1:nrow(var5clean), 0.8*nrow(var5clean)) # Doing an 80-20 train-test split

train = var5clean[index,] # Create the training data 
test = var5clean[-index,] # Create the test data
```

## Resampling due to imbalanced nature of data

```{r}
over = ovun.sample(fraud_bool~., data = train, method = "over")$data
table(over$fraud_bool)

# Now have oversampled dataset with more than half of the data being fraud_bool==1
# Will still use original test set but will now use over dataset to estimate models
```

## LASSO

```{r Model Selection}
xtrain=model.matrix(fraud_bool~., over)[,-1]
ytrain=over$fraud_bool
xtest=model.matrix(fraud_bool~.,test)[,-1]
ytest=test$fraud_bool

cv.out=cv.glmnet(xtrain, ytrain, alpha =1)

bestlam=cv.out$lambda.min
#Creating training model using LASSO regression
lassomodel=glmnet(xtrain, ytrain, alpha=1, lambda=bestlam)
coef(lassomodel)
#Printing out the logistic model
lassomodel$beta
```

> Lots of small coefficients. Only drop device_fraud_count.

```{r}
#Fitting training model on test set
xtestold=xtest=model.matrix(fraud_bool~.,test)[,-1]
pred=predict(lassomodel, s=bestlam, newx=xtestold)

# Make prediction binary
pred_LASSO_bin=ifelse(pred > 0.5, 1, 0)

# Performance Metrics
## Percent accuracy
print(mean(pred_LASSO_bin==ytest))
## Confusion matrix
print(confusionMatrix(as.factor(pred_LASSO_bin), as.factor(ytest)))
## Test MSE
MSE=mean((pred-ytest)^2)
print(MSE)
```

### Adjusting datasets by dropping device_fraud_count

```{r}
var5clean=var5clean%>%select(!device_fraud_count)
train=as.data.frame(train)%>%select(!device_fraud_count)
xtrain=as.data.frame(xtrain)%>%select(!device_fraud_count) # Also formats categorical variables as binary variables and chooses a base group for each
xtest=as.data.frame(xtestold)%>%select(!device_fraud_count)
```

## The Analysis

### LASSO - done above

### Logistic

```{r}
logistic = glm(fraud_bool~., data=train, family=binomial)
summary(logistic)

pred_Logit = predict(logistic, newdata=test, type="response")
pred_Logit = ifelse(pred_Logit > 0.5, 1, 0)

# Performance metrics
print(confusionMatrix(as.factor(pred_Logit), as.factor(ytest)))
## Test MSE
MSE=mean((pred_Logit-ytest)^2)
mean(pred_Logit==as.matrix(ytest))
print(MSE)
```

### LDA

```{r}
lda.model = lda(fraud_bool~., data=train)
lda.model

predmodel.train.lda = predict(lda.model, newdata=test)

# Performance metrics
confusionMatrix(as.factor(predmodel.train.lda$class), as.factor(ytest))
# Test MSE
predmodeltrainlda=as.data.frame(predmodel.train.lda)
predldaclass=as.numeric(predmodeltrainlda$class)
MSE=mean((predldaclass-ytest)^2)
mean(predmodel.train.lda$class==as.matrix(ytest))
print(MSE)
```

### QDA

```{r}
qda.model=qda(fraud_bool~., data=train)
qda.model

predmodel.train.qda = predict(qda.model, newdata=test)

# Performance Metrics
confusionMatrix(as.factor(predmodel.train.qda$class), as.factor(ytest))
# Test MSE
predmodeltrainqda=as.data.frame(predmodel.train.qda)
predqdaclass=as.numeric(predmodeltrainqda$class)
MSE=mean((predqdaclass-ytest)^2)
mean(predmodel.train.qda$class==as.matrix(ytest))
print(MSE)