South German Credit

Donated on 11/28/2019

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.

Dataset Characteristics


Subject Area


Associated Tasks

Classification, Regression, Clustering

Feature Type

Integer, Real

# Instances


# Features


Dataset Information

Additional Information

The widely used Statlog German credit data (, 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.

Has Missing Values?


Variable Information

## 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 transformation unknown) 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 sparkont. (ordinal) 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 Hamerle’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, discretized quantitative) 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)


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