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arff slurped Weka

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# Instances: 1000 / # Attributes: 21
HDF5 (657.1 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

german_credit

Version mldata

0

Comment

Description of the German credit dataset.

1. Title: German Credit data

2. Source Information

Professor Dr. Hans Hofmann
Institut f"ur Statistik und "Okonometrie
Universit"at Hamburg
FB Wirtschaftswissenschaften
Von-Melle-Park 5
2000 Hamburg 13

1. Number of Instances: 1000

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.

1. Number of Attributes german: 20 (7 numerical, 13 categorical) Number of Attributes german.numer: 24 (24 numerical)

2. Attribute description for german

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

1. Cost Matrix

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).

Relabeled values in attribute checking_status From: A11 To: '<0'
From: A12 To: '0<=X<200'
From: A13 To: '>=200'
From: A14 To: 'no checking'

Relabeled values in attribute credit_history From: A30 To: 'no credits/all paid' From: A31 To: 'all paid'
From: A32 To: 'existing paid'
From: A33 To: 'delayed previously' From: A34 To: 'critical/other existing credit'

Relabeled values in attribute purpose From: A40 To: 'new car'
From: A41 To: 'used car'
From: A42 To: furniture/equipment From: A43 To: radio/tv
From: A44 To: 'domestic appliance' From: A45 To: repairs
From: A46 To: education
From: A47 To: vacation
From: A48 To: retraining
From: A49 To: business
From: A410 To: other

Relabeled values in attribute savings_status From: A61 To: '<100'
From: A62 To: '100<=X<500'
From: A63 To: '500<=X<1000'
From: A64 To: '>=1000'
From: A65 To: 'no known savings'

Relabeled values in attribute employment From: A71 To: unemployed
From: A72 To: '<1'
From: A73 To: '1<=X<4'
From: A74 To: '4<=X<7'
From: A75 To: '>=7'

Relabeled values in attribute personal_status From: A91 To: 'male div/sep'
From: A92 To: 'female div/dep/mar' From: A93 To: 'male single'
From: A94 To: 'male mar/wid'
From: A95 To: 'female single'

Relabeled values in attribute other_parties From: A101 To: none
From: A102 To: 'co applicant'
From: A103 To: guarantor

Relabeled values in attribute property_magnitude From: A121 To: 'real estate'
From: A122 To: 'life insurance'
From: A123 To: car
From: A124 To: 'no known property'

Relabeled values in attribute other_payment_plans From: A141 To: bank
From: A142 To: stores
From: A143 To: none

Relabeled values in attribute housing From: A151 To: rent
From: A152 To: own
From: A153 To: 'for free'

Relabeled values in attribute job From: A171 To: 'unemp/unskilled non res' From: A172 To: 'unskilled resident' From: A173 To: skilled
From: A174 To: 'high qualif/self emp/mgmt'

Relabeled values in attribute own_telephone From: A191 To: none
From: A192 To: yes

Relabeled values in attribute foreign_worker From: A201 To: yes
From: A202 To: no

Relabeled values in attribute class From: 1 To: good
From: 2 To: bad

Names

checking_status,duration,credit_history,purpose,credit_amount,savings_status,employment,installment_commitment,personal_status,other_parties,

Types

1. nominal:'<0','0<=X<200','>=200','no checking'
2. numeric
3. nominal:'no credits/all paid','all paid','existing paid','delayed previously','critical/other existing credit'
4. nominal:'new car','used car',furniture/equipment,radio/tv,'domestic appliance',repairs,education,vacation,retraining,business,other
5. numeric
6. nominal:'<100','100<=X<500','500<=X<1000','>=1000','no known savings'
7. nominal:unemployed,'<1','1<=X<4','4<=X<7','>=7'
8. numeric
9. nominal:'male div/sep','female div/dep/mar','male single','male mar/wid','female single'
10. nominal:none,'co applicant',guarantor

Data (first 10 data points)

chec...	dura...	cred...	purp...	cred...	savi...	empl...	inst...	pers...	othe...	...
'<0'	6	'cri...	radi...	1169	'no ...	'>=7'	4	'mal...	none	...
'0<=...	48	'exi...	radi...	5951	'<100'	'1<=...	2	'fem...	none	...
'no ...	12	'cri...	educ...	2096	'<100'	'4<=...	2	'mal...	none	...
'<0'	42	'exi...	furn...	7882	'<100'	'4<=...	2	'mal...	guar...	...
'<0'	24	'del...	'new...	4870	'<100'	'1<=...	3	'mal...	none	...
'no ...	36	'exi...	educ...	9055	'no ...	'1<=...	2	'mal...	none	...
'no ...	24	'exi...	furn...	2835	'500...	'>=7'	3	'mal...	none	...
'0<=...	36	'exi...	'use...	6948	'<100'	'1<=...	2	'mal...	none	...
'no ...	12	'exi...	radi...	3059	'>=1...	'4<=...	2	'mal...	none	...
'0<=...	30	'cri...	'new...	5234	'<100'	unem...	4	'mal...	none	...
...	...	...	...	...	...	...	...	...	...	...

Description

A jarfile containing 37 classification problems, originally obtained from the UCI repository (datasets-UCI.jar, 1,190,961 Bytes).

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http://www.ics.uci.edu/~mlearn/MLRepository.html

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revision 1

by mldata on 2010-11-06 09:57

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