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:
2. 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,
6=unknown)

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Predictive Analytics Assignment (301117) 2019 SPRING Submission 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: 2. 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, 6=unknown)6. 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…