Skip to main content

Table 4 Taiwan dataset features

From: A machine learning-based credit risk prediction engine system using a stacked classifier and a filter-based feature selection method

Feature Name

Type

ID – ID of each client

Continuous

LIMIT_BAL – Amount of given credit

Continuous

SEX

Continuous

EDUCATION

Continuous

AGE in years

Continuous

PAY_0: Repayment status in September, 2005

Continuous

PAY_2: Repayment status in August, 2005

Continuous

PAY_3: Repayment status in July, 2005

Continuous

PAY_4: Repayment status in June, 2005

Continuous

PAY_5: Repayment status in May, 2005

Continuous

PAY_6: Repayment status in April, 2005

Continuous

BILL_AMT1: Amount of bill statement in September 2005

Continuous

BILL_AMT2: Amount of bill statement in August 2005

Continuous

BILL_AMT3: Amount of bill statement in July 2005

Continuous

BILL_AMT4: Amount of bill statement in June 2005

Continuous

BILL_AMT5: Amount of bill statement in May 2005

Continuous

BILL_AMT5: Amount of bill statement in April 2005

Continuous

PAY_AMT1: Amount of bill statement in September 2005

Continuous

PAY_AMT2: Amount of bill statement in August 2005

Continuous

PAY_AMT3: Amount of bill statement in July 2005

Continuous

PAY_AMT4: Amount of bill statement in June 2005

Continuous

PAY_AMT5: Amount of bill statement in May 2005

Continuous

PAY_AMT6: Amount of bill statement in April 2005

Continuous

Class (Default – Yes/No)

Nominal