testds.Rmd

---
title: "R Notebook"
output: html_notebook
---


```{r}
set.seed(12345)

inputdata <- read.csv("ds.csv")
#apply(inputdata,2,function(x) sum(is.na(x)))

#str(inputdata)

inputdf <- inputdata[c(3:8,11,13,16:17)]
head(inputdf)

```

```{r}
library(caret)
index <- sample(1:nrow(inputdf), round(0.2*nrow(inputdf)))
train <- inputdf[index,]
test <- inputdf[-index,]

lm.fit <- glm(loan_status~.,data=train,family=binomial)
summary(lm.fit)

real.results <- subset(test,select = c(9))
pr.lm <- predict(lm.fit,test,type = 'response')
results <- ifelse(pr.lm >0.5,1,0)
head(results)
head(pr.lm)

misclassificationerror <- mean(real.results != results)
print(paste('Accuracy',1-misclassificationerror))

confusionMatrix(data = results,reference = real.results$loan_status)
MSE.lm <- sum((pr.lm - test$loan_status) ^2/nrow(test))
```
```{r}
set.seed(456)
library(neuralnet)
maxs <- apply(inputdf,2,max)
mins <- apply(inputdf,2,min)

scaled <- as.data.frame(scale(inputdf, center = mins, scale = maxs-mins))

train2 <- scaled[index,]
test2 <- scaled[-index,]

n<-names(train2)
f <- as.formula(paste("loan_status~",paste(n[!n %in% "loan_status"],collapse = "+")))
nn <- neuralnet(f, data=train2,hidden = 7,linear.output = F,stepmax = 1000000)

plot(nn)
```
```{r}
nn.bp <- neuralnet(f,data = test2,hidden = 7,err.fct = "ce",linear.output = FALSE,algorithm = "backprop",learningrate = 0.01)

plot(nn.bp)
nn.bp$result.matrix
```

```{r}
testSet_Features = subset(test2,select=c("loan_amnt", "funded_amnt", "funded_amnt_inv", "term", "int_rate", "installment", "emp_length", "annual_inc", "application_type"));

head(testSet_Features)
```

```{r}
NNresults = compute(nn,testSet_Features );
#print(nrow(NNresults$net.result),nrow(test2$loan_status))
finalOutput = data.frame(Actual = test2$loan_status,
                          Prediction = NNresults$net.result,
                          Matches = doesPredictionMatch(test2$loan_status, NNresults$net.result, 0.3));

row.names(finalOutput) = NULL;

print(cat("Prediction Success Rate : ",countSuccessPercent(finalOutput$Matches)));

print(finalOutput)


```

```{r}
doesPredictionMatch = function(expected = Null, predicted = Null, threshold = 0.3){
   if(is.null(expected) || is.null(predicted)){
    print("Necessary arguments missing or null");
    stop();
  }
  
  results = rep(FALSE,length(expected));
  
  for(i in 1:length(expected)){
    if((!is.na(expected[i]))&&(!is.na(predicted[i]))){  
      if(abs(expected[i]-predicted[i])<threshold ){
        results[i] = TRUE;
      }
    }
      
  }
  return (results);
}
countSuccessPercent = function(input = NULL){
  count= 0;
  for(i in 1:length(input)){
    tmp =as.logical(input[i]); 
    if(is.logical(tmp) && tmp ==TRUE ){
      count=count+1;
    }
  }
  return ((count/length(input))*100);  
}

```

```{r}

pr.nn_ <- NNresults$net.result*(max(inputdf$loan_status)-min(inputdf$loan_status))+min(inputdf$loan_status)
test.r <- (test2$loan_status)*(max(inputdf$loan_status)-min(inputdf$loan_status))+min(inputdf$loan_status)

MSE.nn <- sum((test.r - pr.nn_)^2)/nrow(test2)

print(paste(MSE.lm,MSE.nn))

```

```{r}

par(mfrow=c(1,2))

plot(test2$loan_status,pr.nn_,col='red',main='Real vs predicted NN',pch=18,cex=0.7)
abline(0,1,lwd=2)
legend('bottomright',legend='NN',pch=18,col='red', bty='n')

plot(test$loan_status,pr.lm,col='blue',main='Real vs predicted lm',pch=18, cex=0.7)
abline(0,1,lwd=2)
legend('bottomright',legend='LM',pch=18,col='blue', bty='n', cex=.95)



plot(test$loan_status,pr.nn_,col='red',main='Real vs predicted NN',pch=18,cex=0.7)
points(test2$loan_status,pr.lm,col='blue',pch=18,cex=0.7)
abline(0,1,lwd=2)
legend('bottomright',legend=c('NN','LM'),pch=18,col=c('red','blue'))
```
```{r}
library(ggplot2)
par(mfrow=c(2,2))
gwplot(nn,selected.covariate="loan_amnt",min=-2.5, max=5,col="blue")
gwplot(nn,selected.covariate="funded_amnt",min=-2.5, max=5)
gwplot(nn,selected.covariate="funded_amnt_inv",min=-2.5, max=5)
gwplot(nn,selected.covariate="term",min=-2.5, max=5)
gwplot(nn,selected.covariate="int_rate", min=-2.5, max=5)
gwplot(nn,selected.covariate="installment", min=-2.5, max=5)
gwplot(nn,selected.covariate="emp_length", min=-2.5, max=5)
gwplot(nn,selected.covariate="annual_inc", min=-2.5, max=5)
gwplot(nn,selected.covariate="application_type", min=-2.5, max=5)

```