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南德信贷数据集

南德信贷数据集

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Ulrike Gr?mpingBeuth University of Applied Sciences BerlinWebsite with contact information: https://prof.beuth-hochschul......

数据结构 ? 12.8K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Ulrike Gr?mping
    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.


    Attribute 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?¢a??a?¢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)


    Relevant Papers:

    Fahrmeir, L. and Hamerle, A. (1981, in German). Kategoriale Regression in der betrieblichen Planung. *Zeitschrift f?r 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.

    Gr?mping, 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]].

    H?u?ler, W.M. (1979, in German). Empirische Ergebnisse zu Diskriminationsverfahren bei Kreditscoringsystemen. *Zeitschrift f?r Operations Research* **23**, B191-B210.

    Hofmann, H.J. (1990, in German). Die Anwendung des CART-Verfahrens zur statistischen Bonit?tsanalyse von Konsumentenkrediten. *Zeitschrift f?r Betriebswirtschaft* **60**, 941-962.

    Open data LMU (2010; accessed Nov 27 2019; in German). Kreditscoring zur Klassifikation von Kreditnehmern. URL: [[Web link]].



    Citation Request:

    Gr?mping, 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.

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