南德信贷数据集

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.

Ulrike Grömping 柏林贝uth应用科技大学（Beuth University of Applied Sciences Berlin） 联系方式网站：https://prof.beuth-hochschule.de/groemping/

数据集说明：广泛使用的Statlog德国信用数据集（Statlog German credit data）截至2019年11月存在编码信息严重错误，且未附带任何背景说明。本「南德信用」（South German Credit）数据集基于2010年慕尼黑大学开放数据（Open Data LMU）对该数据集的呈现形式，以及其他多份德语资源，对原数据集进行了修正并补充了背景信息。

## 属性信息
## 本节包含各属性的简要说明。## 属性编码的详细信息可参阅配套的R数据读取代码、配套的编码表，或Groemping（2019）的相关文献（列于「相关论文」板块下）。

列名：laufkont 变量名：status 含义：债务人在本行的支票账户状态（分类变量（categorical））
列名：laufzeit 变量名：duration 含义：信用期限（月，定量变量（quantitative））
列名：moral 变量名：credit_history 含义：过往或当前信用合约的履约历史（分类变量（categorical））
列名：verw 变量名：purpose 含义：信用申请用途（分类变量（categorical））
列名：hoehe 变量名：amount 含义：信用金额（德国马克，定量变量（quantitative）；经单调变换得到；实际数据与变换类型均未知）
列名：sparkont 变量名：savings 含义：债务人的储蓄情况（分类变量（categorical））
列名：beszeit 变量名：employment_duration 含义：债务人在当前雇主处的就职时长（有序变量（ordinal）；离散化定量变量）
列名：rate 变量名：installment_rate 含义：信用分期还款额占债务人可支配收入的比例（有序变量（ordinal）；离散化定量变量）
列名：famges 变量名：personal_status_sex 含义：合并的性别与婚姻状况信息（分类变量（categorical）；无法从该变量还原原始性别信息，因为单身男性与非单身女性被编码为同一代码（2）；由于编码表未将丧偶女性归入任何女性类别，因此难以对丧偶女性进行分类）
列名：buerge 变量名：other_debtors 含义：是否存在其他债务人或信用担保人（分类变量（categorical））
列名：wohnzeit 变量名：present_residence 含义：债务人在当前居住地的居住时长（年，有序变量（ordinal）；离散化定量变量）
列名：verm 变量名：property 含义：债务人最具价值的资产，即使用最高可用代码；若代码3或4不适用，且债务人拥有汽车或其他不属于`savings`变量覆盖范围的相关资产，则使用代码2（有序变量（ordinal））
列名：alter 变量名：age 含义：年龄（岁，定量变量（quantitative））
列名：weitkred 变量名：other_installment_plans 含义：除放款银行外的其他机构提供的分期还款计划（分类变量（categorical））
列名：wohn 变量名：housing 含义：债务人的住房类型（分类变量（categorical））
列名：bishkred 变量名：number_credits 含义：债务人在本行已有的（含当前申请的）信用账户总数（有序变量（ordinal）；离散化定量变量）；与Fahrmeir与Hamerle（1984）的表述相悖，该数据集的原始数据值已不可得
列名：beruf 变量名：job 含义：债务人的职业水平（有序变量（ordinal））
列名：pers 变量名：people_liable 含义：经济上依赖债务人的人数（即有权获得赡养费的人数，二分类变量（binary）；离散化定量变量）
列名：telef 变量名：telephone 含义：债务人名下是否登记有固定电话（二分类变量（binary）；请注意该数据集采集自20世纪70年代）
列名：gastarb 变量名：foreign_worker 含义：债务人是否为外籍劳工（二分类变量（binary））
列名：kredit 变量名：credit_risk 含义：信用合约履约情况（履约为良好，违约为不良，二分类变量（binary））

## 相关论文
1. Fahrmeir, L. 与 Hamerle, A. (1981，德语)：《Kategoriale Regression in der betrieblichen Planung》，载于*Zeitschrift für Operations Research* **25**，B63-B78。
2. Fahrmeir, L. 与 Hamerle, A. (1984，德语)：《Multivariate Statistische Verfahren》（第1版，第8章与附录C），De Gruyter出版社，柏林。
3. Grömping, U. (2019)：《South German Credit data: Correcting a Widely Used Data Set》，柏林贝uth应用科技大学数学、物理与化学系II部，2019年第4号报告，《数学、物理与化学报告》。链接：[[Web link]]
4. Häußler, W.M. (1979，德语)：《Empirische Ergebnisse zu Diskriminationsverfahren bei Kreditscoringsystemen》，载于*Zeitschrift für Operations Research* **23**，B191-B210。
5. Hofmann, H.J. (1990，德语)：《Die Anwendung des CART-Verfahrens zur statistischen Bonitätsanalyse von Konsumentenkrediten》，载于*Zeitschrift für Betriebswirtschaft* **60**，941-962。
6. 慕尼黑大学开放数据（Open Data LMU, 2010；2019年11月27日访问，德语）：《Kreditscoring zur Klassifikation von Kreditnehmern》，链接：[[Web link]]

## 引用要求
Grömping, U. (2019)：《South German Credit data: Correcting a Widely Used Data Set》，柏林贝uth应用科技大学数学、物理与化学系II部，2019年第4号报告，《数学、物理与化学报告》。

搜集汇总

背景与挑战

背景概述

南德信贷数据集是对Statlog German credit数据的修正版本，提供了详细的背景信息和属性描述，包括债务人的账户状态、信用历史、贷款目的等多个信贷相关属性，用于信用风险评估。