Credit-Risk-Prediction-R

Machine Learning analysis on R to predict customer defaults

1. Title: Credit Risk Prediction

2. Number of Instances: 31375

3. Number of Attributes: 39

4. Attribute information:

1. ID: client ID
2. LIMIT: credit Limit of a client in dollars
3. GENDER: 1 = male, 2 = female
4. EDUCATION: (1 = graduate school, 2 = university, 3 = high school, 0 = others, 4 = others, 5 = special program, 6 = unknown)
5. MARRIAGE: marital status (1 = married, 2 = single, 3 = divorced, 0 = others)
6. AGE: age in years
7. AGE_CTG: age category (1 = from 20 to 34; 2 = from 35 to 49; 3 = from 50 to 64; 4 = 65 and over) 8 - 13) Variables PY1, PY2, PY3, PY4, PY5, PY6 : History of past payments. PY1 = the repayment status in period X; PY2 = the repayment status in period (X-1) PY3 = the repayment status in period (X-2); PY4 = the repayment status in period (X-3) PY5 = the repayment status in period (X-4); PY6 = the repayment status in period (X-5)

Categories in these variables are:
-2: No consumption/transaction;  -1: Paid in full;  0: small payment;  
1 = payment delay for one period; 2 = payment delay for two periods; ...; 8 = payment delay for eight periods; 9 = payment delays for nine periods and above

14 - 19) Variables = BILL1, BILL2, BILL3, BILL4, BILL5, BILL6: Amount of bill statement in dollars BILL1: bill statement in period X; BILL2: bill statement in period (X-1); BILL3: bill statement in period (X-2); BILL4: bill statement in period (X-3); BILL5: bill statement in period (X-4); BILL6: bill statement in period (X-5)

20 - 25) Variables: PYAMT1, PYAMT2, PYAMT3, PYAMT4, PYAMT5, PYAMT6: Amount of previous payment in dollars PYAMT1: amount paid in period X; PYAMT2: amount paid in period (X-1); PYAMT3: amount paid in period (X-2); PYAMT4: amount paid in period (X-3); PYAMT5: amount paid in period (X-4); PYAMT6: amount paid in period (X-5)

26. SATISFACTION: service satisfaction (0 = not satisfactory; 1 = normal; 2 = satisfactory)
27. FREQTRANSACTION: how frequently client visits Universal Plus (0 = rarely, 1 = regularly)
28. PHONE: whether the client has a landline or not (0 = no phone; 1 = yes)
29. DEPENDENT: whether the client has children or not (0 = no child; 1 = yes)
30. CREDITCRD: number of credit cards
31. RSTATUS: current accommodation status (0 = shared lease, 1 = homeowner, 2 = rent)
32. OTH_ACCOUNT: whether the client has several bank accounts (0 = no, 1= yes)
33. CAR: whether the client has a car (0 = no, 1 = yes)
34. YEARSINADD: years in the current address (3 = three years or below, 4 = four years, ..., 7 = seven years or above)
35. SECONDHOME: whether the client has another address (0 = no, 1 = yes)
36. EMPLOYMENT: whether the client has a permenant job (0 = no, 1 = yes)
37. NEW_CSTM: whether the client joined Universal Plus in the last two years or s/he is an existing customer (0 = joined in the last two years, 1 = existing customer)
38. CM_HIST: criminal history, e.g. insurance fraud (0 = no, 1 = yes)
39. CLASS: 0 = the client paid the credit back; 1 = the client did not pay the credit and went into default