KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

KEEL-dataset - data set description

German Credit data set

Description
Files and additional references

Description

This section describes main characteristics of the german data set and its attributes:

General information

German Credit data set
Type	Classification	Origin	Real world
Features	20	(Real / Integer / Nominal)	(0 / 7 / 13)
Instances	1000	Classes	2
Missing values?			No

Attribute description

Attribute	Domain	Attribute	Domain
StatusAccount	{A11, A12, A14, A13}	ResidenceSince	[1, 4]
DurationMonth	[4, 72]	Property	{A121, A122, A124, A123}
CreditHistory	{A34, A32, A33, A30, A31}	Age	[19, 75]
Purpose	{A43, A46, A42, A40, A41, A49, A44, A45, A410, A48}	InstallmentPlans	{A143, A141, A142}
CreditAmount	[250, 18424]	Housing	{A152, A153, A151}
SavingsAccount	{A65, A61, A63, A64, A62}	NCredits	[1, 4]
EmploymentSince	{A75, A73, A74, A71, A72}	Job	{A173, A172, A174, A171}
InstallmentRate	[1, 4]	NPeopleMain	[1, 2]
StatusAndSex	{A93, A92, A91, A94}	Telephone	{A192, A191}
Guarantors	{A101, A103, A102}	ForeignWorker	{A201, A202}
Customer	{1,2}

Additional information

A numerical version of the Statlog German Credit Data data set. Here, the task is to clasify customers as good (1) or bad (2), depending on 20 features about them and their bancary accounts.

In this problem, the use of an additional cost matrix is suggested, because it is worse to class a customer as good when they are bad (cost 5), than it is to class a customer as bad when they are good (cost 1).

Files and additional references

In this section you can download some files related to the german data set:

The complete data set already formatted in KEEL format can be downloaded from here .
A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from here .
A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from here .
The header file associated to this data set can be downloaded from here .
This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29.