🔍Credit Scoring 🔎

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.

🐕Features🐕

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