South German Credit Data Set
Download: Data Folder, Data Set Description
Abstract: 700 good and 300 bad credits with 20 predictor variables. Data from 1973 to 1975. Stratified sample from actual credits with bad credits heavily oversampled. A cost matrix can be used.
Data Set Characteristics:
Number of Instances:
Number of Attributes:
Classification, Regression, Clustering
Number of Web Hits:
Beuth University of Applied Sciences Berlin
Website with contact information: https://prof.beuth-hochschule.de/groemping/
Data Set Information:
The widely used Statlog German credit data ([Web Link]), as of November 2019, suffers from severe errors in the coding information and does not come with any background information. The 'South German Credit' data provide a correction and some background information, based on the Open Data LMU (2010) representation of the same data and several other German language resources.
## This section contains a brief description for each attribute.
## Details on attribute coding can be obtained from the accompanying R code for reading the data
## or the accompanying code table,
## as well as from Groemping (2019) (listed under 'Relevant Papers').
Column name: laufkont
Variable name: status
Content: status of the debtor's checking account with the bank (categorical)
Column name: laufzeit
Variable name: duration
Content: credit duration in months (quantitative)
Column name: moral
Variable name: credit_history
Content: history of compliance with previous or concurrent credit contracts (categorical)
Column name: verw
Variable name: purpose
Content: purpose for which the credit is needed (categorical)
Column name: hoehe
Variable name: amount
Content: credit amount in DM (quantitative; result of monotonic transformation; actual data and type of
Column name: sparkont
Variable name: savings
Content: debtor's savings (categorical)
Column name: beszeit
Variable name: employment_duration
Content: duration of debtor's employment with current employer (ordinal; discretized quantitative)
Column name: rate
Variable name: installment_rate
Content: credit installments as a percentage of debtor's disposable income (ordinal; discretized quantitative)
Column name: famges
Variable name: personal_status_sex
Content: combined information on sex and marital status; categorical; sex cannot be recovered from the
variable, because male singles and female non-singles are coded with the same code (2); female widows cannot
be easily classified, because the code table does not list them in any of the female categories
Column name: buerge
Variable name: other_debtors
Content: Is there another debtor or a guarantor for the credit? (categorical)
Column name: wohnzeit
Variable name: present_residence
Content: length of time (in years) the debtor lives in the present residence (ordinal; discretized quantitative)
Column name: verm
Variable name: property
Content: the debtor's most valuable property, i.e. the highest possible code is used. Code 2 is used, if codes 3
or 4 are not applicable and there is a car or any other relevant property that does not fall under variable
Column name: alter
Variable name: age
Content: age in years (quantitative)
Column name: weitkred
Variable name: other_installment_plans
Content: installment plans from providers other than the credit-giving bank (categorical)
Column name: wohn
Variable name: housing
Content: type of housing the debtor lives in (categorical)
Column name: bishkred
Variable name: number_credits
Content: number of credits including the current one the debtor has (or had) at this bank (ordinal, discretized
quantitative); contrary to Fahrmeir and HamerleÃ¢â‚¬â„¢s (1984) statement, the original data values are not available.
Column name: beruf
Variable name: job
Content: quality of debtor's job (ordinal)
Column name: pers
Variable name: people_liable
Content: number of persons who financially depend on the debtor (i.e., are entitled to maintenance) (binary,
Column name: telef
Variable name: telephone
Content: Is there a telephone landline registered on the debtor's name? (binary; remember that the data are
from the 1970s)
Column name: gastarb
Variable name: foreign_worker
Content: Is the debtor a foreign worker? (binary)
Column name: kredit
Variable name: credit_risk
Content: Has the credit contract been complied with (good) or not (bad) ? (binary)
Fahrmeir, L. and Hamerle, A. (1981, in German). Kategoriale Regression in der betrieblichen Planung. *Zeitschrift för Operations Research* **25**, B63-B78.
Fahrmeir, L. and Hamerle, A. (1984, in German). *Multivariate Statistische Verfahren* (1st ed., Ch.8 and Appendix C). De Gruyter, Berlin.
Grömping, U. (2019). South German Credit Data: Correcting a Widely Used Data Set. Report 4/2019, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin. URL: [[Web Link]].
Häußler, W.M. (1979, in German). Empirische Ergebnisse zu Diskriminationsverfahren bei Kreditscoringsystemen. *Zeitschrift för Operations Research* **23**, B191-B210.
Hofmann, H.J. (1990, in German). Die Anwendung des CART-Verfahrens zur statistischen Bonitätsanalyse von Konsumentenkrediten. *Zeitschrift för Betriebswirtschaft* **60**, 941-962.
Open data LMU (2010; accessed Nov 27 2019; in German). Kreditscoring zur Klassifikation von Kreditnehmern. URL: [[Web Link]].
Grömping, U. (2019). South German Credit Data: Correcting a Widely Used Data Set. Report 4/2019, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.