Statlog (German Credit Data)

Donated on 11/16/1994

This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix

Dataset Characteristics

Multivariate

Subject Area

Social Science

Associated Tasks

Classification

Feature Type

Categorical, Integer

# Instances

1000

# Features

20

Dataset Information

Additional Information

Two datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical/symbolic attributes and is in the file "german.data". For algorithms that need numerical attributes, Strathclyde University produced the file "german.data-numeric". This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables. Several attributes that are ordered categorical (such as attribute 17) have been coded as integer. This was the form used by StatLog. This dataset requires use of a cost matrix (see below) ..... 1 2 ---------------------------- 1 0 1 ----------------------- 2 5 0 (1 = Good, 2 = Bad) The rows represent the actual classification and the columns the predicted classification. It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1).

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
Attribute1FeatureCategoricalStatus of existing checking accountno
Attribute2FeatureIntegerDurationmonthsno
Attribute3FeatureCategoricalCredit historyno
Attribute4FeatureCategoricalPurposeno
Attribute5FeatureIntegerCredit amountno
Attribute6FeatureCategoricalSavings account/bondsno
Attribute7FeatureCategoricalOtherPresent employment sinceno
Attribute8FeatureIntegerInstallment rate in percentage of disposable incomeno
Attribute9FeatureCategoricalMarital StatusPersonal status and sexno
Attribute10FeatureCategoricalOther debtors / guarantorsno

0 to 10 of 21

Additional Variable Information

Attribute 1: (qualitative) Status of existing checking account A11 : ... < 0 DM A12 : 0 <= ... < 200 DM A13 : ... >= 200 DM / salary assignments for at least 1 year A14 : no checking account Attribute 2: (numerical) Duration in month Attribute 3: (qualitative) Credit history A30 : no credits taken/ all credits paid back duly A31 : all credits at this bank paid back duly A32 : existing credits paid back duly till now A33 : delay in paying off in the past A34 : critical account/ other credits existing (not at this bank) Attribute 4: (qualitative) Purpose A40 : car (new) A41 : car (used) A42 : furniture/equipment A43 : radio/television A44 : domestic appliances A45 : repairs A46 : education A47 : (vacation - does not exist?) A48 : retraining A49 : business A410 : others Attribute 5: (numerical) Credit amount Attibute 6: (qualitative) Savings account/bonds A61 : ... < 100 DM A62 : 100 <= ... < 500 DM A63 : 500 <= ... < 1000 DM A64 : .. >= 1000 DM A65 : unknown/ no savings account Attribute 7: (qualitative) Present employment since A71 : unemployed A72 : ... < 1 year A73 : 1 <= ... < 4 years A74 : 4 <= ... < 7 years A75 : .. >= 7 years Attribute 8: (numerical) Installment rate in percentage of disposable income Attribute 9: (qualitative) Personal status and sex A91 : male : divorced/separated A92 : female : divorced/separated/married A93 : male : single A94 : male : married/widowed A95 : female : single Attribute 10: (qualitative) Other debtors / guarantors A101 : none A102 : co-applicant A103 : guarantor Attribute 11: (numerical) Present residence since Attribute 12: (qualitative) Property A121 : real estate A122 : if not A121 : building society savings agreement/ life insurance A123 : if not A121/A122 : car or other, not in attribute 6 A124 : unknown / no property Attribute 13: (numerical) Age in years Attribute 14: (qualitative) Other installment plans A141 : bank A142 : stores A143 : none Attribute 15: (qualitative) Housing A151 : rent A152 : own A153 : for free Attribute 16: (numerical) Number of existing credits at this bank Attribute 17: (qualitative) Job A171 : unemployed/ unskilled - non-resident A172 : unskilled - resident A173 : skilled employee / official A174 : management/ self-employed/ highly qualified employee/ officer Attribute 18: (numerical) Number of people being liable to provide maintenance for Attribute 19: (qualitative) Telephone A191 : none A192 : yes, registered under the customers name Attribute 20: (qualitative) foreign worker A201 : yes A202 : no

Baseline Model Performance

Papers Citing this Dataset

Markov chain Monte Carlo algorithms with sequential proposals

By Joonha Park, Yves Atchad'e. 2019

Published in

SLA-based adaptation schemes in distributed stream processing engines

By Muhammad Hanif, Eunsam Kim, Sumi Helal, Choonhwa Lee. 2019

Published in Applied Sciences.

Trust-Region Variational Inference with Gaussian Mixture Models

By Oleg Arenz, Mingjun Zhong, Gerhard Neumann. 2019

Published in ArXiv.

Model Agnostic Contrastive Explanations for Structured Data

By Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, Ruchir Puri. 2019

Published in ArXiv.

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Keywords

finance

Creators

Hans Hofmann

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