Abstract: This dataset concerns credit card applications. It has a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. General goal is to predict which people in the dataset are successful in applying for a credit card.
| Feature | dtype | Description |
|---|---|---|
| Gender | Binary | 0=Female, 1=Male |
| Age | Numeric | Age in year |
| Debt | Numeric | Outstanding debt |
| Married | Binary | 0=Single/Divorced/etc, 1=Married |
| BankCustomer(BankRecord) | Binary | 0=does not have a bank account, 1=has a bank account |
| Investment score | Numeric | a number from 0 to 10 |
| Industry | Categorical | job sector of current or most recent job |
| Ethnicity | Categorical | |
| YearsEmployed | Numeric | |
| PriorDefault | Binary | 0=no prior defaults, 1=prior default |
| Employed | Binary | 0=not employed, 1=employed |
| CreditScore | Numeric | |
| DriversLicense | Binary | 0=no license, 1=has license |
| Citizenship | Categorical | either ByBirth, ByOtherMeans or Temporary |
| ZipCode: | Categorical | digit number |
| Income | Numeric | |
| Approved | Binary | 0=not approved, 1=approved |