GermanCredit: 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<="" code="">, ... >= 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<="" code="">, 500 <= 1000="" ...="" <="" dm<="" code="">, ... >= 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<="" code="">, 4 <= 7="" ...="" <="" years<="" code=""> 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.