new.R

setwd("C:/Users/USER/Desktop/Coaching/Projects/Loan Prediction")
tr <- read.csv('train.csv', header = TRUE)
head(tr)
summary(tr)
tr <- read.csv(file="train.csv", na.strings=c("", "NA"), header=TRUE)
library(plyr)
tr$Dependents <- revalue(tr$Dependents, c("3+"="3")) 
sapply(tr, function(x) sum(is.na(x)))
library(mice)
library(VIM)
mice_plot <- aggr(tr, col=c('navyblue','red'),
                  numbers=TRUE, sortVars=TRUE,
                  labels=names(tr), cex.axis=.7,
                  gap=3, ylab=c("Missing data","Pattern"))

#distribution
hist(tr$LoanAmount, 
     main="Histogram for Loan Amount", 
     xlab="Income", 
     border="blue", 
     col="maroon",
     xlim=c(0,700),
     breaks=20)

hist(tr$ApplicantIncome, 
     main="Histogram for Applicant Income", 
     xlab="Income", 
     border="blue", 
     col="maroon",
     xlim=c(0,80000),
     breaks=50)

## Histograms & Boxplots
par(mfrow=c(2,2))
hist(tr$LoanAmount, 
     main="Histogram for LoanAmount", 
     xlab="Loan Amount", 
     border="blue", 
     col="maroon",
     las=1, 
     breaks=20, prob = TRUE)
#lines(density(tr$LoanAmount), col='black', lwd=3)
boxplot(tr$LoanAmount, col='maroon',xlab = 'LoanAmount', main = 'Box Plot for Loan Amount')


hist(tr$ApplicantIncome, 
     main="Histogram for Applicant Income", 
     xlab="Income", 
     border="blue", 
     col="maroon",
     las=1, 
     breaks=50, prob = TRUE)
#lines(density(tr$ApplicantIncome), col='black', lwd=3)
boxplot(tr$ApplicantIncome, col='maroon',xlab = 'ApplicantIncome', main = 'Box Plot for Applicant Income')

dev.off()

# Dist of LoanAmount by Loan Status##
library(ggplot2)
data(tr, package="lattice")
ggplot(data=tr, aes(x=LoanAmount, fill=Education)) +
  geom_density() +
  facet_grid(Education~.) 
 
## Visualizing Categorical Variables

par(mfrow=c(2,3))
counts <- table(tr$Loan_Status, tr$Gender)
barplot(counts, main="Loan Status by Gender",
        xlab="Gender", col=c("darkgrey","maroon"),
        legend = rownames(counts))
counts2 <- table(tr$Loan_Status, tr$Education)
barplot(counts2, main="Loan Status by Education",
        xlab="Education", col=c("darkgrey","maroon"),
        legend = rownames(counts2))
counts3 <- table(tr$Loan_Status, tr$Married)
barplot(counts3, main="Loan Status by Married",
        xlab="Married", col=c("darkgrey","maroon"),
        legend = rownames(counts3))
counts4 <- table(tr$Loan_Status, tr$Self_Employed)
barplot(counts4, main="Loan Status by Self Employed",
        xlab="Self_Employed", col=c("darkgrey","maroon"),
        legend = rownames(counts4))
counts5 <- table(tr$Loan_Status, tr$Property_Area)
barplot(counts5, main="Loan Status by Property_Area",
        xlab="Property_Area", col=c("darkgrey","maroon"),
        legend = rownames(counts5))
counts6 <- table(tr$Loan_Status, tr$Credit_History)
barplot(counts6, main="Loan Status by Credit_History",
        xlab="Credit_History", col=c("darkgrey","maroon"),
        legend = rownames(counts5))

dev.off()

imputed_Data <- mice(tr, m=2, maxit = 2, method = 'cart', seed = 500)
tr <- complete(imputed_Data,2) #here I chose the second round of data imputation
sapply(tr, function(x) sum(is.na(x)))


tr$LogLoanAmount <- log(tr$LoanAmount)
par(mfrow=c(1,2))
hist(tr$LogLoanAmount, 
     main="Histogram for Loan Amount", 
     xlab="Loan Amount", 
     border="blue", 
     col="maroon",
     las=1, 
     breaks=20, prob = TRUE)
lines(density(tr$LogLoanAmount), col='black', lwd=3)
boxplot(tr$LogLoanAmount, col='maroon',xlab = 'Income', main = 'Box Plot for Applicant Income')
dev.off()

tr$Income <- tr$ApplicantIncome + tr$CoapplicantIncome
tr$ApplicantIncome <- NULL
tr$CoapplicantIncome <- NULL

tr$LogIncome <- log(tr$Income)
par(mfrow=c(1,2))
hist(tr$LogIncome, 
     main="Histogram for Applicant Income", 
     xlab="Income", 
     border="blue", 
     col="maroon",
     las=1, 
     breaks=50, prob = TRUE)
lines(density(tr$LogIncome), col='black', lwd=3)
boxplot(tr$LogIncome, col='maroon',xlab = 'Income', main = 'Box Plot for Applicant Income')
dev.off()


#Split

## 70% of the sample size

set.seed(42)
sample <- sample.int(n = nrow(tr), size = floor(.70*nrow(tr)), replace = F)
trainnew <- tr[sample, ]
testnew  <- tr[-sample, ]

logistic1 <- glm (Loan_Status ~ Credit_History,data = trainnew, family = binomial)
summary(logistic1)
my_prediction_tr1 <- predict(logistic1, newdata = trainnew, type = "response")
table(trainnew$Loan_Status, my_prediction_tr1 > 0.5)

logistic_test1 <- glm (Loan_Status ~ Credit_History,data = testnew, family = binomial)
summary(logistic_test1)
my_prediction_te1 <- predict(logistic_test1, newdata = testnew, type = "response")
table(testnew$Loan_Status, my_prediction_te1 > 0.5)

logistic2 <- glm (Loan_Status ~ Credit_History+Education+Self_Employed+Property_Area+LogLoanAmount+
                    LogIncome,data = trainnew, family = binomial)
summary(logistic2)
my_prediction_tr2 <- predict(logistic2, newdata = trainnew, type = "response")
table(trainnew$Loan_Status, my_prediction_tr2 > 0.5)


logistic_test2 <- glm (Loan_Status ~ Credit_History+Education+Self_Employed+Property_Area+LogLoanAmount+
                         LogIncome,data = testnew, family = binomial)
summary(logistic_test2)
my_prediction_te2 <- predict(logistic_test2, newdata = testnew, type = "response")
table(testnew$Loan_Status, my_prediction_te2 > 0.5)

###########decision tree############
library(rpart)
# grow tree 
dtree <- rpart(Loan_Status ~ Credit_History+Education+Self_Employed+Property_Area+LogLoanAmount+
                 LogIncome,method="class", data=trainnew,parms=list(split="information"))
dtree$cptable
plotcp(dtree)
dtree.pruned <- prune(dtree, cp=.02290076)
library(rpart.plot)
prp(dtree.pruned, type = 2, extra = 104,
    fallen.leaves = TRUE, main="Decision Tree")
dtree.pred <- predict(dtree.pruned, trainnew, type="class")
dtree.perf <- table(trainnew$Loan_Status, dtree.pred,
                    dnn=c("Actual", "Predicted"))
dtree.perf


dtree_test <- rpart(Loan_Status ~ Credit_History+Education+Self_Employed+Property_Area+LogLoanAmount+
                 LogIncome,method="class", data=testnew,parms=list(split="information"))
dtree_test$cptable
plotcp(dtree_test)
dtree_test.pruned <- prune(dtree_test, cp=.01639344)
prp(dtree_test.pruned, type = 2, extra = 104,
    fallen.leaves = TRUE, main="Decision Tree")
dtree_test.pred <- predict(dtree_test.pruned, testnew, type="class")
dtree_test.perf <- table(testnew$Loan_Status, dtree_test.pred,
                    dnn=c("Actual", "Predicted"))
dtree_test.perf

###Random Forest

library(randomForest) 
set.seed(42) 
fit.forest <- randomForest(Loan_Status ~ Credit_History+Education+Self_Employed+Property_Area+LogLoanAmount+
                             LogIncome, data=trainnew,
                           na.action=na.roughfix,
                           importance=TRUE)
fit.forest

importance(fit.forest, type=2)

forest.pred <- predict(fit.forest, testnew)
forest.perf <- table(testnew$Loan_Status, forest.pred,
                     dnn=c("Actual", "Predicted"))
forest.perf


# Use the highest 3 in importance
set.seed(42) 
fit.forest2 <- randomForest(Loan_Status ~ Credit_History+LogLoanAmount+
                             LogIncome, data=trainnew,importance=TRUE)
fit.forest2


forest.pred2 <- predict(fit.forest2, testnew)
forest.perf_test <- table(testnew$Loan_Status, forest.pred2,
                     dnn=c("Actual", "Predicted"))
forest.perf_test

my_solution <- data.frame(Loan_ID = testnew$Loan_ID, Loan_Status = forest.pred2)
# Write your solution away to a csv file with the name my_solution.csv
write.csv(my_solution, file = "my_solution.csv", row.names = FALSE)