R: Statlog German Credit

GermanCredit {klaR} | R Documentation

## Statlog German Credit

### 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

    data("GermanCredit")

### 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.

[Package klaR version 0.6-12 Index]