credit: South German Credit Data

In liver: Toolkit and Datasets for Data Science

South German Credit Data

Description

A dataset containing information on credit applicants, including account status, credit history, loan purpose, credit amount, savings, employment duration, personal characteristics, property, housing, and other financial attributes. The outcome variable indicates whether the applicant represents a good or bad credit risk.

Usage

data(credit)

Format

A data frame with 1000 observations and 21 variables:

status

Status of the debtor's checking account with the bank.

duration

Credit duration in months.

credit_history

History of compliance with previous or concurrent credit contracts.

purpose

Purpose for which the credit is needed.

amount

Credit amount in Deutsche Mark (DM).

savings

Debtor's savings.

employment_duration

Duration of the debtor's employment with the current employer.

installment_rate

Credit installments as a percentage of the debtor's disposable income.

personal_status_sex

Combined information on personal status and sex.

other_debtors

Whether there is another debtor or a guarantor for the credit.

present_residence

Length of time the debtor has lived in the present residence.

property

The debtor's most valuable property.

age

Age in years.

other_installment_plans

Installment plans from providers other than the credit-giving bank.

housing

Type of housing the debtor lives in.

number_credits

Number of credits the debtor has or had at this bank, including the current one.

job

Quality of the debtor's job.

people_liable

Number of persons financially dependent on the debtor.

telephone

Whether a telephone landline is registered in the debtor's name.

foreign_worker

Whether the debtor is a foreign worker.

credit_risk

Credit risk outcome: "good risk" or "bad risk".

Details

The South German Credit data are a corrected and documented version of the widely used German credit data. The dataset contains 700 good and 300 bad credits and covers actual credit data from 1973 to 1975, with bad credits heavily oversampled. It can be used to illustrate methods for classification, exploratory data analysis, and predictive modeling in R.

Source

UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/573/south+german+credit+update

South German Credit [Dataset]. (2020). UCI Machine Learning Repository. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.24432/C5QG88")}

References

Gr\"omping, U. (2019). South German credit data: Correcting a widely used data set.

See Also

loan, mortgage, bank, churn_mlc, churn, churn_tel, adult, cereal, advertising, marketing, drug, house, house_price, red_wines, white_wines, insurance, caravan, fertilizer, corona

Examples

data(credit)

str(credit)
summary(credit)

liver documentation built on April 7, 2026, 9:07 a.m.