Statlog (Australian Credit Approval)

This file concerns credit card applications. This database exists elsewhere in the repository (Credit Screening Database) in a slightly different form

Dataset Characteristics

Multivariate

Subject Area

Business

Associated Tasks

Classification

Feature Type

Categorical, Integer, Real

# Instances

690

# Features

14

Dataset Information

Additional Information

This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data. This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values.

Has Missing Values?

Yes

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
A1FeatureCategoricalno
A2FeatureContinuousno
A3FeatureContinuousno
A4FeatureCategoricalno
A5FeatureCategoricalno
A6FeatureCategoricalno
A7FeatureContinuousno
A8FeatureCategoricalno
A9FeatureCategoricalno
A10FeatureContinuousno

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Additional Variable Information

There are 6 numerical and 8 categorical attributes. The labels have been changed for the convenience of the statistical algorithms. For example, attribute 4 originally had 3 labels p,g,gg and these have been changed to labels 1,2,3. A1: 0,1 CATEGORICAL (formerly: a,b) A2: continuous. A3: continuous. A4: 1,2,3 CATEGORICAL (formerly: p,g,gg) A5: 1, 2,3,4,5, 6,7,8,9,10,11,12,13,14 CATEGORICAL (formerly: ff,d,i,k,j,aa,m,c,w, e, q, r,cc, x) A6: 1, 2,3, 4,5,6,7,8,9 CATEGORICAL (formerly: ff,dd,j,bb,v,n,o,h,z) A7: continuous. A8: 1, 0 CATEGORICAL (formerly: t, f) A9: 1, 0 CATEGORICAL (formerly: t, f) A10: continuous. A11: 1, 0 CATEGORICAL (formerly t, f) A12: 1, 2, 3 CATEGORICAL (formerly: s, g, p) A13: continuous. A14: continuous. A15: 1,2 class attribute (formerly: +,-)

Baseline Model Performance

Papers Citing this Dataset

Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification

By Kathrin Grosse, Michael Smith, Michael Backes. 2018

Published in ArXiv.

A Pruning Neural Network Model in Credit Classification Analysis

By Yajiao Tang, Junkai Ji, Shangce Gao, Hongwei Dai, Yang Yu, Yuki Todo. 2018

Published in Computational intelligence and neuroscience.

Constrained Learning Vector Quantization or Relaxed k-Separability

By Marek Grochowski, Wlodzislaw Duch. 2009

Published in ICANN.

Operations strategy and flexibility: modeling with Bayesian classifiers

By María Abad-Grau, Daniel Arias-Aranda. 2006

Published in Industrial Management and Data Systems.

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Creators

Ross Quinlan

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