credit_Decision_tree.Rmd

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output:
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---
```{r}
#read the dataset
file=readRDS("loan_data_1.rds")

```

```{r}
#create frame for dataset
credit_frame=data.frame(file)
```

```{r}
  str(credit_frame)   
```
```{r}
       head(credit_frame)
```
```{r}
        sapply(credit_frame,function(x) sum(is.na(x)))
```

```{r}
     library(gmodels)
     CrossTable(credit_frame$loan_status)

```


```{r}
    CrossTable(credit_frame$home_ownership)
```


```{r}
 CrossTable(credit_frame$grade)
```
```{r}
    print(summary(credit_frame$loan_amnt))
    print(summary(credit_frame$int_rate))
    print(summary(credit_frame$emp_length))
```


```{r}
   plot(credit_frame$annual_inc,ylab="Annual Income")

```
```{r}
      hist(credit_frame$annual_inc,sqrt(nrow(credit_frame)),xlab = "Annual Income",main = "histogram of annual income")
```
From scatter plot extrem value is removed 
```{r}
  index_outlier_annua_inc<-which(credit_frame$annual_inc>3000000)
   index_outlier_annua_inc
   nrow(credit_frame)
   credit_frame<-credit_frame[-index_outlier_annua_inc,]
   nrow(credit_frame)

```
```{r}
      plot(credit_frame$annual_inc,ylab="Annual Income")

```
```{r}
    hist(credit_frame$annual_inc,sqrt(nrow(credit_frame)),xlab = "Annual Income",main = "histogram of annual income")
```

still not look normanl
```{r}
  boxplot(credit_frame$annual_inc)
```

```{r}
summary(credit_frame$annual_inc)
```
```{r}
    temp_vector<-c("log_annual_income")
    credit_frame[,temp_vector]<-NA
```
```{r}
   credit_frame$log_annual_income<-log(credit_frame$annual_inc)
```

```{r}

    hist(credit_frame$log_annual_inc,sqrt(nrow(credit_frame)),xlab = "log Annual Income",main = "histogram of log annual income")
      
```
```{r}
   summary(credit_frame$log_annual_income)
```
 
 
 
Looks normal distribution acepted
```{r}
   boxplot(credit_frame$age,ylab="age")
```
```{r}
     hist(credit_frame$age,sqrt(nrow(credit_frame)),ylab = "frequency",xlab = "age",main = "Histogram of age")
```

```{r}
   summary(credit_frame$age)
```
Looks constant mean and median So distribution is quet normal
Interest rate has some missing values so fill it with mean
```{r}
summary(credit_frame$int_rate)
index_int_rate_NA<-which(is.na(credit_frame$int_rate))
credit_frame$int_rate[index_int_rate_NA]<-mean(credit_frame$int_rate,na.rm = TRUE)
summary(credit_frame$int_rate)
```

```{r}
plot(credit_frame$int_rate,xlab = "frequency",ylab = "interest rate",main = "Interest rate")
```

```{r}
  hist(credit_frame$int_rate,sqrt(nrow(credit_frame)),ylab = "frequency",xlab = "interest rate",main = "Histogram of Interest rate")
```

```{r}
  boxplot(credit_frame$int_rate,ylab="Interst rate")
```
```{r}
  summary(credit_frame$int_rate)
```

From distribution and summary it looks normal and acepted
```{r}
   
summary(credit_frame$emp_length)
index_emp_length_NA<-which(is.na(credit_frame$emp_length))
credit_frame$emp_length[index_emp_length_NA]<-mean(credit_frame$emp_length,na.rm = TRUE)
summary(credit_frame$emp_length)
     
```
```{r}
      boxplot(credit_frame$emp_length,main="Boxplot of log emp length")
```

```{r}
   hist(credit_frame$emp_length,sqrt(nrow(credit_frame)),ylab = "Frequency",xlab = "Emp length",main = "Histogram of Emp length")
```
Emp length has extrem values so preprocess it 
```{r}
   credit_frame$emp_length<-credit_frame$emp_length+1
```
```{r}
    temp_vector<-c("log_emp_length")
    credit_frame[,temp_vector]<-NA
```
```{r}
    credit_frame$log_emp_length<-log(credit_frame$emp_length)
```
```{r}
    boxplot(credit_frame$log_emp_length)
    
```
```{r}
   hist(credit_frame$log_emp_length,ylab = "frequency",xlab ="Log emp length ", main = "Histogram of Log of emp length")
    
```
```{r}
       boxplot(credit_frame$log_emp_length,ylab="Log of emp length")
```
```{r}
   summary(credit_frame$log_emp_length)
```
Looks normal because normal mean and median
```{r}
     boxplot(credit_frame$loan_amnt)
```
```{r}
   summary(credit_frame$loan_amnt)
```
```{r}
    hist(credit_frame$loan_amnt,sqrt(nrow(credit_frame)),ylab = "Frequency",xlab = "Loan Amount", main = "Histogram of Loan Amount")
```
```{r}
     temp_vector<-c("log_loan_amnt")
    credit_frame[,temp_vector]<-NA
```
  
```{r}
    credit_frame$log_loan_amnt<-log(credit_frame$loan_amnt)
```

```{r}
   boxplot(credit_frame$log_loan_amnt,main="Boxplot of log loan amnt")
```
```{r}
    hist(credit_frame$log_loan_amnt,sqrt(nrow(credit_frame)),ylab = "frequency",xlab = "Log loan amnt",main = "Histogram of Log loan amnt")
```

```{r}
   summary(credit_frame$log_loan_amnt)
```
lets create some new features 
```{r}
    CrossTable(credit_frame$grade,credit_frame$loan_status,prop.r = TRUE,prop.c = FALSE,prop.t = FALSE,prop.chisq = FALSE)   
  
```

Accordingly from above observation the loan status depends on the grade of the customer 
```{r}
     temp_vector<-c("grade_multiplier")
     credit_frame[,temp_vector]<-NA

```

```{r}
    credit_frame<-within(credit_frame,grade_multiplier[grade=='A']<-(0.941))
credit_frame<-within(credit_frame,grade_multiplier[grade=='B']<-(0.894))
credit_frame<-within(credit_frame,grade_multiplier[grade=='C']<-(0.853))
credit_frame<-within(credit_frame,grade_multiplier[grade=='D']<-(0.820))
credit_frame<-within(credit_frame,grade_multiplier[grade=='E']<-(0.797))
credit_frame<-within(credit_frame,grade_multiplier[grade=='F']<-(0.735))
credit_frame<-within(credit_frame,grade_multiplier[grade=='G']<-(0.625))

```

```{r}
  temp_vector<-c("loan_amnt_int_rate_multiplier")
     credit_frame[,temp_vector]<-NA
   
```

```{r}
   credit_frame$loan_amnt_int_rate_multiplier<-((credit_frame$int_rate)*(credit_frame$log_loan_amnt)*(credit_frame$grade_multiplier))
```

```{r}
  hist(credit_frame$loan_amnt_int_rate_multiplier,sqrt(nrow(credit_frame)),ylab = "frequency",xlab = "loan_amnt_int_rate_multiplier",main = "Histogram of loan_amnt_int_rate_multiplier ")
```
```{r}
   boxplot(credit_frame$loan_amnt_int_rate_multiplier,main="loan_amnt_int_rate_multiplier")
```

```{r}
    summary(credit_frame$loan_amnt_int_rate_multiplier)
```


```{r}
index_train <- sample(1:nrow(credit_frame), 2 / 3 * nrow(credit_frame))
   
```

```{r}
    training_set<-credit_frame[index_train,]
    test_set<-credit_frame[-index_train,]
    
```

```{r}
    x_test=test_set[,2:13]
    y_test=test_set[,1]
```


```{r}
    library(rpart)
    dt_model<-rpart(loan_status~loan_amnt_int_rate_multiplier+grade+log_loan_amnt+int_rate+log_emp_length+log_annual_income+age+annual_inc+home_ownership+emp_length+loan_amnt, method = 'class',data = training_set, parms = list(prior = c(0.7, 0.3)),control = rpart.control(cp = 0.001) )
```

```{r}
   summary(dt_model)
```



```{r}
   predicted1<-predict(dt_model,x_test,type = 'class')
```

```{r}
   mean(predicted1==y_test)
```

```{r}
 library(caTools)
   table(test_set$loan_status,predicted1)
```