Description

The dataset contains data of past credit applicants. The applicants are rated as good or bad. Models of this data can be used to determine if new applicants present a good or bad credit risk.

Usage

Format

A data frame containing 1,000 observations on 21 variables.

status

factor variable indicating the status of the existing checking account, with levels ... < 100 DM, 0 <= ... < 200 DM, ... >= 200 DM/salary for at least 1 year and no checking account.

duration

duration in months.

credit_history

factor variable indicating credit history, with levels no credits taken/all credits paid back duly, all credits at this bank paid back duly, existing credits paid back duly till now, delay in paying off in the past and critical account/other credits existing.

purpose

factor variable indicating the credit's purpose, with levels car (new), car (used), furniture/equipment, radio/television, domestic appliances, repairs, education, retraining, business and others.

amount

credit amount.

savings

factor. savings account/bonds, with levels ... < 100 DM, 100 <= ... < 500 DM, 500 <= ... < 1000 DM, ... >= 1000 DM and unknown/no savings account.

employment_duration

ordered factor indicating the duration of the current employment, with levels unemployed, ... < 1 year, 1 <= ... < 4 years, 4 <= ... < 7 years and ... >= 7 years.

installment_rate

installment rate in percentage of disposable income.

personal_status_sex

factor variable indicating personal status and sex, with levels male:divorced/separated, female:divorced/separated/married, male:single, male:married/widowed and female:single.

other_debtors

factor. Other debtors, with levels none, co-applicant and guarantor.

present_residence

present residence since?

property

factor variable indicating the client's highest valued property, with levels real estate, building society savings agreement/life insurance, car or other and unknown/no property.

age

client's age.

other_installment_plans

factor variable indicating other installment plans, with levels bank, stores and none.

housing

factor variable indicating housing, with levels rent, own and for free.

number_credits

number of existing credits at this bank.

job

factor indicating employment status, with levels unemployed/unskilled - non-resident, unskilled - resident, skilled employee/official and management/self-employed/highly qualified employee/officer.

people_liable

Number of people being liable to provide maintenance.

telephone

binary variable indicating if the customer has a registered telephone number.

foreign_worker

binary variable indicating if the customer is a foreign worker.

credit_risk

binary variable indicating credit risk, with levels good and bad.

Source

The original data was provided by:

Professor Dr. Hans Hofmann, Institut fuer Statistik und Oekonometrie, Universitaet Hamburg, FB Wirtschaftswissenschaften, Von-Melle-Park 5, 2000 Hamburg 13

The dataset has been taken from the UCI Repository Of Machine Learning Databases at http://archive.ics.uci.edu/ml/.

It was published this way in CRAN package evtree (maintainer: Thomas Grubinger) that has been archived from CRAN on May 31, 2014. Afterwards the exactly same data object has been copied from the evtree package to klaR.