% 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 % % 3. 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. % % % 6. Number of Attributes german: 20 (7 numerical, 13 categorical) % Number of Attributes german.numer: 24 (24 numerical) % % % 7. 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 % % % % 8. 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 % @relation german_credit @attribute checking_status { '<0', '0<=X<200', '>=200', 'no checking'} @attribute duration real @attribute credit_history { 'no credits/all paid', 'all paid', 'existing paid', 'delayed previously', 'critical/other existing credit'} @attribute purpose { 'new car', 'used car', furniture/equipment, radio/tv, 'domestic appliance', repairs, education, vacation, retraining, business, other} @attribute credit_amount real @attribute savings_status { '<100', '100<=X<500', '500<=X<1000', '>=1000', 'no known savings'} @attribute employment { unemployed, '<1', '1<=X<4', '4<=X<7', '>=7'} @attribute installment_commitment real @attribute personal_status { 'male div/sep', 'female div/dep/mar', 'male single', 'male mar/wid', 'female single'} @attribute other_parties { none, 'co applicant', guarantor} @attribute residence_since real @attribute property_magnitude { 'real estate', 'life insurance', car, 'no known property'} @attribute age real @attribute other_payment_plans { bank, stores, none} @attribute housing { rent, own, 'for free'} @attribute existing_credits real @attribute job { 'unemp/unskilled non res', 'unskilled resident', skilled, 'high qualif/self emp/mgmt'} @attribute num_dependents real @attribute own_telephone { none, yes} @attribute foreign_worker { yes, no} @attribute class { good, bad} @data '>=200',24,'existing paid',furniture/equipment,2892,'<100','>=7',3,'male div/sep',none,4,'no known property',51,none,'for free',1,skilled,1,none,yes,good 'no checking',24,'existing paid',furniture/equipment,3062,'500<=X<1000','>=7',4,'male single',none,3,'no known property',32,none,rent,1,skilled,1,yes,yes,good 'no checking',9,'existing paid',furniture/equipment,2301,'100<=X<500','<1',2,'female div/dep/mar',none,4,'life insurance',22,none,rent,1,skilled,1,none,yes,good '<0',18,'existing paid','used car',7511,'no known savings','>=7',1,'male single',none,4,'life insurance',51,none,'for free',1,skilled,2,yes,yes,bad 'no checking',12,'critical/other existing credit',furniture/equipment,1258,'<100','<1',2,'female div/dep/mar',none,4,'life insurance',22,none,rent,2,'unskilled resident',1,none,yes,good 'no checking',24,'delayed previously','new car',717,'no known savings','>=7',4,'male mar/wid',none,4,car,54,none,own,2,skilled,1,yes,yes,good '0<=X<200',9,'existing paid','new car',1549,'no known savings','<1',4,'male single',none,2,'real estate',35,none,own,1,'unemp/unskilled non res',1,none,yes,good 'no checking',24,'critical/other existing credit',education,1597,'<100','>=7',4,'male single',none,4,'no known property',54,none,'for free',2,skilled,2,none,yes,good '0<=X<200',18,'critical/other existing credit',radio/tv,1795,'<100','>=7',3,'female div/dep/mar',guarantor,4,'real estate',48,bank,rent,2,'unskilled resident',1,yes,yes,good '<0',20,'critical/other existing credit',furniture/equipment,4272,'<100','>=7',1,'female div/dep/mar',none,4,'life insurance',24,none,own,2,skilled,1,none,yes,good 'no checking',12,'critical/other existing credit',radio/tv,976,'no known savings','>=7',4,'male single',none,4,car,35,none,own,2,skilled,1,none,yes,good '0<=X<200',12,'existing paid','new car',7472,'no known savings',unemployed,1,'female div/dep/mar',none,2,'real estate',24,none,rent,1,'unemp/unskilled non res',1,none,yes,good '<0',36,'existing paid','new car',9271,'<100','4<=X<7',2,'male single',none,1,car,24,none,own,1,skilled,1,yes,yes,bad '0<=X<200',6,'existing paid',radio/tv,590,'<100','<1',3,'male mar/wid',none,3,'real estate',26,none,own,1,'unskilled resident',1,none,no,good 'no checking',12,'critical/other existing credit',radio/tv,930,'no known savings','>=7',4,'male single',none,4,'real estate',65,none,own,4,skilled,1,none,yes,good '0<=X<200',42,'all paid','used car',9283,'<100',unemployed,1,'male single',none,2,'no known property',55,bank,'for free',1,'high qualif/self emp/mgmt',1,yes,yes,good '0<=X<200',15,'no credits/all paid','new car',1778,'<100','<1',2,'female div/dep/mar',none,1,'real estate',26,none,rent,2,'unemp/unskilled non res',1,none,yes,bad '0<=X<200',8,'existing paid',business,907,'<100','<1',3,'male mar/wid',none,2,'real estate',26,none,own,1,skilled,1,yes,yes,good '0<=X<200',6,'existing paid',radio/tv,484,'<100','4<=X<7',3,'male mar/wid',guarantor,3,'real estate',28,bank,own,1,'unskilled resident',1,none,yes,good '<0',36,'critical/other existing credit','used car',9629,'<100','4<=X<7',4,'male single',none,4,car,24,none,own,2,skilled,1,yes,yes,bad '<0',48,'existing paid','domestic appliance',3051,'<100','1<=X<4',3,'male single',none,4,car,54,none,own,1,skilled,1,none,yes,bad '<0',48,'existing paid','new car',3931,'<100','4<=X<7',4,'male single',none,4,'no known property',46,none,'for free',1,skilled,2,none,yes,bad '0<=X<200',36,'delayed previously','new car',7432,'<100','1<=X<4',2,'female div/dep/mar',none,2,'life insurance',54,none,rent,1,skilled,1,none,yes,good 'no checking',6,'existing paid','domestic appliance',1338,'500<=X<1000','1<=X<4',1,'male div/sep',none,4,'real estate',62,none,own,1,skilled,1,none,yes,good 'no checking',6,'critical/other existing credit',radio/tv,1554,'<100','4<=X<7',1,'female div/dep/mar',none,2,car,24,none,rent,2,skilled,1,yes,yes,good '<0',36,'existing paid',other,15857,'<100',unemployed,2,'male div/sep','co applicant',3,car,43,none,own,1,'high qualif/self emp/mgmt',1,none,yes,good '<0',18,'existing paid',radio/tv,1345,'<100','1<=X<4',4,'male mar/wid',none,3,'real estate',26,bank,own,1,skilled,1,none,yes,bad 'no checking',12,'existing paid','new car',1101,'<100','1<=X<4',3,'male mar/wid',none,2,'real estate',27,none,own,2,skilled,1,yes,yes,good '>=200',12,'existing paid',radio/tv,3016,'<100','1<=X<4',3,'male mar/wid',none,1,car,24,none,own,1,skilled,1,none,yes,good '<0',36,'existing paid',furniture/equipment,2712,'<100','>=7',2,'male single',none,2,'life insurance',41,bank,own,1,skilled,2,none,yes,bad '<0',8,'critical/other existing credit','new car',731,'<100','>=7',4,'male single',none,4,'real estate',47,none,own,2,'unskilled resident',1,none,yes,good 'no checking',18,'critical/other existing credit',furniture/equipment,3780,'<100','<1',3,'male div/sep',none,2,car,35,none,own,2,'high qualif/self emp/mgmt',1,yes,yes,good '<0',21,'critical/other existing credit','new car',1602,'<100','>=7',4,'male mar/wid',none,3,car,30,none,own,2,skilled,1,yes,yes,good '<0',18,'critical/other existing credit','new car',3966,'<100','>=7',1,'female div/dep/mar',none,4,'real estate',33,bank,rent,3,skilled,1,yes,yes,bad 'no checking',18,'no credits/all paid',business,4165,'<100','1<=X<4',2,'male single',none,2,car,36,stores,own,2,skilled,2,none,yes,bad '<0',36,'existing paid','used car',8335,'no known savings','>=7',3,'male single',none,4,'no known property',47,none,'for free',1,skilled,1,none,yes,bad '0<=X<200',48,'delayed previously',business,6681,'no known savings','1<=X<4',4,'male single',none,4,'no known property',38,none,'for free',1,skilled,2,yes,yes,good 'no checking',24,'delayed previously',business,2375,'500<=X<1000','1<=X<4',4,'male single',none,2,car,44,none,own,2,skilled,2,yes,yes,good '<0',18,'existing paid','new car',1216,'<100','<1',4,'female div/dep/mar',none,3,car,23,none,rent,1,skilled,1,yes,yes,bad '<0',45,'no credits/all paid',business,11816,'<100','>=7',2,'male single',none,4,car,29,none,rent,2,skilled,1,none,yes,bad '0<=X<200',24,'existing paid',radio/tv,5084,'no known savings','>=7',2,'female div/dep/mar',none,4,car,42,none,own,1,skilled,1,yes,yes,good '>=200',15,'existing paid',radio/tv,2327,'<100','<1',2,'female div/dep/mar',none,3,'real estate',25,none,own,1,'unskilled resident',1,none,yes,bad '<0',12,'no credits/all paid','new car',1082,'<100','1<=X<4',4,'male single',none,4,car,48,bank,own,2,skilled,1,none,yes,bad 'no checking',12,'existing paid',radio/tv,886,'no known savings','1<=X<4',4,'female div/dep/mar',none,2,car,21,none,own,1,skilled,1,none,yes,good 'no checking',4,'existing paid',furniture/equipment,601,'<100','<1',1,'female div/dep/mar',none,3,'real estate',23,none,rent,1,'unskilled resident',2,none,yes,good '<0',24,'critical/other existing credit','used car',2957,'<100','>=7',4,'male single',none,4,'life insurance',63,none,own,2,skilled,1,yes,yes,good 'no checking',24,'critical/other existing credit',radio/tv,2611,'<100','>=7',4,'male mar/wid','co applicant',3,'real estate',46,none,own,2,skilled,1,none,yes,good '<0',36,'existing paid',furniture/equipment,5179,'<100','4<=X<7',4,'male single',none,2,'life insurance',29,none,own,1,skilled,1,none,yes,bad 'no checking',21,'delayed previously','used car',2993,'<100','1<=X<4',3,'male single',none,2,'real estate',28,stores,own,2,'unskilled resident',1,none,yes,good 'no checking',18,'existing paid',repairs,1943,'<100','<1',4,'female div/dep/mar',none,4,'real estate',23,none,own,1,skilled,1,none,yes,bad 'no checking',24,'all paid',business,1559,'<100','4<=X<7',4,'male single',none,4,car,50,bank,own,1,skilled,1,yes,yes,good 'no checking',18,'existing paid',furniture/equipment,3422,'<100','>=7',4,'male single',none,4,'life insurance',47,bank,own,3,skilled,2,yes,yes,good '0<=X<200',21,'existing paid',furniture/equipment,3976,'no known savings','4<=X<7',2,'male single',none,3,car,35,none,own,1,skilled,1,yes,yes,good 'no checking',18,'existing paid','new car',6761,'no known savings','1<=X<4',2,'male single',none,4,car,68,none,rent,2,skilled,1,none,yes,bad 'no checking',24,'existing paid','new car',1249,'<100','<1',4,'male mar/wid',none,2,'real estate',28,none,own,1,skilled,1,none,yes,good '<0',9,'existing paid',radio/tv,1364,'<100','4<=X<7',3,'male single',none,4,'real estate',59,none,own,1,skilled,1,none,yes,good '<0',12,'existing paid',radio/tv,709,'<100','>=7',4,'male single',none,4,'real estate',57,stores,own,1,'unskilled resident',1,none,yes,bad '<0',20,'critical/other existing credit','new car',2235,'<100','1<=X<4',4,'male mar/wid',guarantor,2,'life insurance',33,bank,rent,2,skilled,1,none,no,bad 'no checking',24,'critical/other existing credit','used car',4042,'no known savings','4<=X<7',3,'male single',none,4,'life insurance',43,none,own,2,skilled,1,yes,yes,good 'no checking',15,'critical/other existing credit',radio/tv,1471,'<100','1<=X<4',4,'male single',none,4,'no known property',35,none,'for free',2,skilled,1,yes,yes,good '<0',18,'all paid','new car',1442,'<100','4<=X<7',4,'male single',none,4,'no known property',32,none,'for free',2,'unskilled resident',2,none,yes,bad 'no checking',36,'delayed previously','new car',10875,'<100','>=7',2,'male single',none,2,car,45,none,own,2,skilled,2,yes,yes,good 'no checking',24,'existing paid','new car',1474,'100<=X<500','<1',4,'male mar/wid',none,3,'real estate',33,none,own,1,skilled,1,yes,yes,good 'no checking',10,'existing paid',retraining,894,'no known savings','4<=X<7',4,'female div/dep/mar',none,3,'life insurance',40,none,own,1,skilled,1,yes,yes,good 'no checking',15,'critical/other existing credit',furniture/equipment,3343,'<100','1<=X<4',4,'male single',none,2,'no known property',28,none,'for free',1,skilled,1,yes,yes,good '<0',15,'existing paid','new car',3959,'<100','1<=X<4',3,'female div/dep/mar',none,2,'life insurance',29,none,own,1,skilled,1,yes,yes,bad 'no checking',9,'existing paid','new car',3577,'100<=X<500','1<=X<4',1,'male single',guarantor,2,'real estate',26,none,rent,1,skilled,2,none,no,good 'no checking',24,'critical/other existing credit','used car',5804,'>=1000','1<=X<4',4,'male single',none,2,'real estate',27,none,own,2,skilled,1,none,yes,good 'no checking',18,'delayed previously',business,2169,'<100','1<=X<4',4,'male mar/wid',none,2,car,28,none,own,1,skilled,1,yes,yes,bad '<0',24,'existing paid',radio/tv,2439,'<100','<1',4,'female div/dep/mar',none,4,'real estate',35,none,own,1,skilled,1,yes,yes,bad 'no checking',27,'critical/other existing credit',furniture/equipment,4526,'>=1000','<1',4,'male single',none,2,'real estate',32,stores,own,2,'unskilled resident',2,yes,yes,good 'no checking',10,'existing paid',furniture/equipment,2210,'<100','1<=X<4',2,'male single',none,2,'real estate',25,bank,rent,1,'unskilled resident',1,none,yes,bad 'no checking',15,'existing paid',furniture/equipment,2221,'500<=X<1000','1<=X<4',2,'female div/dep/mar',none,4,car,20,none,rent,1,skilled,1,none,yes,good '<0',18,'existing paid',radio/tv,2389,'<100','<1',4,'female div/dep/mar',none,1,car,27,stores,own,1,skilled,1,none,yes,good 'no checking',12,'critical/other existing credit',furniture/equipment,3331,'<100','>=7',2,'male single',none,4,'life insurance',42,stores,own,1,skilled,1,none,yes,good 'no checking',36,'existing paid',business,7409,'no known savings','>=7',3,'male single',none,2,'life insurance',37,none,own,2,skilled,1,none,yes,good '<0',12,'existing paid',furniture/equipment,652,'<100','>=7',4,'female div/dep/mar',none,4,'life insurance',24,none,rent,1,skilled,1,none,yes,good 'no checking',36,'delayed previously',furniture/equipment,7678,'500<=X<1000','4<=X<7',2,'female div/dep/mar',none,4,car,40,none,own,2,skilled,1,yes,yes,good '>=200',6,'critical/other existing credit','new car',1343,'<100','>=7',1,'male single',none,4,'real estate',46,none,own,2,skilled,2,none,no,good '<0',24,'critical/other existing credit',business,1382,'100<=X<500','4<=X<7',4,'male single',none,1,'real estate',26,none,own,2,skilled,1,yes,yes,good 'no checking',15,'existing paid','domestic appliance',874,'no known savings','<1',4,'female div/dep/mar',none,1,'real estate',24,none,own,1,skilled,1,none,yes,good '<0',12,'existing paid',furniture/equipment,3590,'<100','1<=X<4',2,'male single','co applicant',2,'life insurance',29,none,own,1,'unskilled resident',2,none,yes,good '0<=X<200',11,'critical/other existing credit','new car',1322,'>=1000','1<=X<4',4,'female div/dep/mar',none,4,car,40,none,own,2,skilled,1,none,yes,good '<0',18,'all paid',radio/tv,1940,'<100','<1',3,'male single','co applicant',4,'no known property',36,bank,'for free',1,'high qualif/self emp/mgmt',1,yes,yes,good 'no checking',36,'existing paid',radio/tv,3595,'<100','>=7',4,'male single',none,2,car,28,none,own,1,skilled,1,none,yes,good '<0',9,'existing paid','new 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