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Statistics & Analysis

Write an R script creditpred R to predict the default payments of credit card clients based on the clients demographic information and payment history


Predictive Analytics Assignment (301117) 

2019 SPRING 


The assignment solution should be submitted online via vUWS in “Assignment” section through the link provided there. All R source code and supported documents (completed coversheet) should be packed in one zip file and upload. Multiple files are not acceptable. No other forms of submission is allowed. All data files are included in “Assignment” page. The submission without completed cover sheet will NOT be marked and counted as non- submission! 

Question 1 (total 100 points) Write an R script creditpred.R to predict the default payments of credit card clients based on the clients' demographic information and payment history. 

The data contain information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005. 

File descriptions 

  1. Credit_Card_train.csv - the training set. You can download it from vuws -> Data -> 

Assignment -> Credit_Card_train.csv 

Data fields 

There are 25 variables: 

  1. ID - ID of each client 3. LIMIT_BAL - Amount of given credit in NT dollars (includes individual and 

family/supplementary credit 4. SEX - Gender (1=male, 2=female) 5. EDUCATION - (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 


  1. MARRIAGE - Marital status (1=married, 2=single, 3=others) 7. AGE - Age in years 8. PAY_0 - Repayment status in September, 2005 (negative integer or 0=pay duly, 1=payment 

delay for one month, 2=payment delay for two months, ... 8=payment delay for eight months, 9=payment delay for nine months and above) 9. PAY_2 - Repayment status in August, 2005 (scale same as above) 10. PAY_3 - Repayment status in July, 2005 (scale same as above) 11. PAY_4 - Repayment status in June, 2005 (scale same as above) 12. PAY_5 - Repayment status in May, 2005 (scale same as above) 13. PAY_6 - Repayment status in April, 2005 (scale same as above) 14. BILL_AMT1 - Amount of bill statement in September, 2005 (NT dollar) 15. BILL_AMT2 - Amount of bill statement in August, 2005 (NT dollar) 16. BILL_AMT3 - Amount of bill statement in July, 2005 (NT dollar) 17. BILL_AMT4 - Amount of bill statement in June, 2005 (NT dollar) 18. BILL_AMT5 - Amount of bill statement in May, 2005 (NT dollar) 19. BILL_AMT6 - Amount of bill statement in April, 2005 (NT dollar) 20. PAY_AMT1 - Amount of previous payment in September, 2005 (NT dollar) 21. PAY_AMT2 - Amount of previous payment in August, 2005 (NT dollar) 22. PAY_AMT3 - Amount of previous payment in July, 2005 (NT dollar) 23. PAY_AMT4 - Amount of previous payment in June, 2005 (NT dollar) 24. PAY_AMT5 - Amount of previous payment in May, 2005 (NT dollar) 25. PAY_AMT6 - Amount of previous payment in April, 2005 (NT dollar) 26. default.payment.next.month - Default payment (1=yes, 0=no). This is the response 

variable your model is trying to predict. 

The required subtasks and their marks 

  1. Data reading and formatting (5 points): read the data into R and format it into 

data.frame. 2. Missing value handling (20 points): There are missing values in variable AGE in some observations. Instead of simply remove the observations with NA value, figure out another way to handle missing values, e.g. using predictive model. 3. Building models with proper model selection. You have to consider all of the following. 

1) Determine what variables you want to use in the model (10 points). 2) Use at least three models that you have learnt in this unit or some other models 

you know (but must be appropriate for this task). If you decide to use linear model, it counts as one model, i.e. linear models with different predictors are treated as one model. (30 points) 3) For each model you pick, apply cross validation to select the best model 

hyperparameters if there is any and report the performance. (30 points) 4) Determine which model to report as the final one. (5 points) 4. Model complexity and efficacy trade-off discussion. This is extra subtask for 10 bonus 

points. If you want to get these extra points, you have to address this problem somehow and reach to some conclusion, e.g. what model you choose eventually showing the 

complexity and performance trade-off. Hint: you can consider feature selection, the hyperparameter in SVM giving less support vectors and so on. 

Marking scheme: 

  1. All files must be present, creditpre.R and completed coversheet. No coversheet is 

treated as non-submission. 2. The R files must use the names as specified in this document. 3. (20%) Readable R code with comments indicating what code is doing what and put 

the answers as comment in the code. 4. (80%) Functioning R code that does all the things that are required for this 

assignment. Code fraction that fails to achieve what is required has no partial points. If the subtask is complete, you get the points. Partial points are only given to the cases where there are only minor mistakes. 

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