South German Credit
Donated on 6/19/2020
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
Multivariate
Subject Area
Business
Associated Tasks
Classification, Regression, Clustering
Feature Type
Integer, Real
# Instances
1000
# Features
21
Dataset Information
Additional Information
The widely used Statlog German credit data ([https://archive.ics.uci.edu/ml/datasets/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?
No
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)
Dataset Files
File | Size |
---|---|
SouthGermanCredit.asc | 46.8 KB |
read_SouthGermanCredit.R | 7.4 KB |
codetable.txt | 3.1 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset south_german_credit = fetch_ucirepo(id=573) # data (as pandas dataframes) X = south_german_credit.data.features y = south_german_credit.data.targets # metadata print(south_german_credit.metadata) # variable information print(south_german_credit.variables)
South German Credit [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C5QG88.
Keywords
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.