% 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 '<0',6,'critical/other existing credit',radio/tv,1169,'no known savings','>=7',4,'male single',none,4,'real estate',67,none,own,2,skilled,1,yes,yes,good '0<=X<200',48,'existing paid',radio/tv,5951,'<100','1<=X<4',2,'female div/dep/mar',none,2,'real estate',22,none,own,1,skilled,1,none,yes,bad 'no checking',12,'critical/other existing credit',education,2096,'<100','4<=X<7',2,'male single',none,3,'real estate',49,none,own,1,'unskilled resident',2,none,yes,good '<0',42,'existing paid',furniture/equipment,7882,'<100','4<=X<7',2,'male single',guarantor,4,'life insurance',45,none,'for free',1,skilled,2,none,yes,good '<0',24,'delayed previously','new car',4870,'<100','1<=X<4',3,'male single',none,4,'no known property',53,none,'for free',2,skilled,2,none,yes,bad 'no checking',36,'existing paid',education,9055,'no known savings','1<=X<4',2,'male single',none,4,'no known property',35,none,'for free',1,'unskilled resident',2,yes,yes,good 'no checking',24,'existing paid',furniture/equipment,2835,'500<=X<1000','>=7',3,'male single',none,4,'life insurance',53,none,own,1,skilled,1,none,yes,good '0<=X<200',36,'existing paid','used car',6948,'<100','1<=X<4',2,'male single',none,2,car,35,none,rent,1,'high qualif/self emp/mgmt',1,yes,yes,good 'no checking',12,'existing paid',radio/tv,3059,'>=1000','4<=X<7',2,'male div/sep',none,4,'real estate',61,none,own,1,'unskilled resident',1,none,yes,good '0<=X<200',30,'critical/other existing credit','new car',5234,'<100',unemployed,4,'male mar/wid',none,2,car,28,none,own,2,'high qualif/self emp/mgmt',1,none,yes,bad '0<=X<200',12,'existing paid','new car',1295,'<100','<1',3,'female div/dep/mar',none,1,car,25,none,rent,1,skilled,1,none,yes,bad '<0',48,'existing paid',business,4308,'<100','<1',3,'female div/dep/mar',none,4,'life insurance',24,none,rent,1,skilled,1,none,yes,bad '0<=X<200',12,'existing paid',radio/tv,1567,'<100','1<=X<4',1,'female div/dep/mar',none,1,car,22,none,own,1,skilled,1,yes,yes,good '<0',24,'critical/other existing credit','new car',1199,'<100','>=7',4,'male single',none,4,car,60,none,own,2,'unskilled resident',1,none,yes,bad '<0',15,'existing paid','new car',1403,'<100','1<=X<4',2,'female div/dep/mar',none,4,car,28,none,rent,1,skilled,1,none,yes,good '<0',24,'existing paid',radio/tv,1282,'100<=X<500','1<=X<4',4,'female div/dep/mar',none,2,car,32,none,own,1,'unskilled resident',1,none,yes,bad 'no checking',24,'critical/other existing credit',radio/tv,2424,'no known savings','>=7',4,'male single',none,4,'life insurance',53,none,own,2,skilled,1,none,yes,good '<0',30,'no credits/all paid',business,8072,'no known savings','<1',2,'male single',none,3,car,25,bank,own,3,skilled,1,none,yes,good '0<=X<200',24,'existing paid','used car',12579,'<100','>=7',4,'female div/dep/mar',none,2,'no known property',44,none,'for free',1,'high qualif/self emp/mgmt',1,yes,yes,bad 'no checking',24,'existing paid',radio/tv,3430,'500<=X<1000','>=7',3,'male single',none,2,car,31,none,own,1,skilled,2,yes,yes,good 'no checking',9,'critical/other existing credit','new car',2134,'<100','1<=X<4',4,'male single',none,4,car,48,none,own,3,skilled,1,yes,yes,good '<0',6,'existing paid',radio/tv,2647,'500<=X<1000','1<=X<4',2,'male single',none,3,'real estate',44,none,rent,1,skilled,2,none,yes,good '<0',10,'critical/other existing credit','new car',2241,'<100','<1',1,'male single',none,3,'real estate',48,none,rent,2,'unskilled resident',2,none,no,good '0<=X<200',12,'critical/other existing credit','used car',1804,'100<=X<500','<1',3,'male single',none,4,'life insurance',44,none,own,1,skilled,1,none,yes,good 'no checking',10,'critical/other existing credit',furniture/equipment,2069,'no known savings','1<=X<4',2,'male mar/wid',none,1,car,26,none,own,2,skilled,1,none,no,good '<0',6,'existing paid',furniture/equipment,1374,'<100','1<=X<4',1,'male single',none,2,'real estate',36,bank,own,1,'unskilled resident',1,yes,yes,good 'no checking',6,'no credits/all paid',radio/tv,426,'<100','>=7',4,'male mar/wid',none,4,car,39,none,own,1,'unskilled resident',1,none,yes,good '>=200',12,'all paid',radio/tv,409,'>=1000','1<=X<4',3,'female div/dep/mar',none,3,'real estate',42,none,rent,2,skilled,1,none,yes,good '0<=X<200',7,'existing paid',radio/tv,2415,'<100','1<=X<4',3,'male single',guarantor,2,'real estate',34,none,own,1,skilled,1,none,yes,good '<0',60,'delayed previously',business,6836,'<100','>=7',3,'male single',none,4,'no known property',63,none,own,2,skilled,1,yes,yes,bad '0<=X<200',18,'existing paid',business,1913,'>=1000','<1',3,'male mar/wid',none,3,'real estate',36,bank,own,1,skilled,1,yes,yes,good '<0',24,'existing paid',furniture/equipment,4020,'<100','1<=X<4',2,'male single',none,2,car,27,stores,own,1,skilled,1,none,yes,good '0<=X<200',18,'existing paid','new car',5866,'100<=X<500','1<=X<4',2,'male single',none,2,car,30,none,own,2,skilled,1,yes,yes,good 'no checking',12,'critical/other existing credit',business,1264,'no known savings','>=7',4,'male single',none,4,'no known property',57,none,rent,1,'unskilled resident',1,none,yes,good '>=200',12,'existing paid',furniture/equipment,1474,'<100','<1',4,'female div/dep/mar',none,1,'life insurance',33,bank,own,1,'high qualif/self emp/mgmt',1,yes,yes,good '0<=X<200',45,'critical/other existing credit',radio/tv,4746,'<100','<1',4,'male single',none,2,'life insurance',25,none,own,2,'unskilled resident',1,none,yes,bad 'no checking',48,'critical/other existing credit',education,6110,'<100','1<=X<4',1,'male single',none,3,'no known property',31,bank,'for free',1,skilled,1,yes,yes,good '>=200',18,'existing paid',radio/tv,2100,'<100','1<=X<4',4,'male single','co applicant',2,'real estate',37,stores,own,1,skilled,1,none,yes,bad '>=200',10,'existing paid','domestic appliance',1225,'<100','1<=X<4',2,'male single',none,2,car,37,none,own,1,skilled,1,yes,yes,good '0<=X<200',9,'existing paid',radio/tv,458,'<100','1<=X<4',4,'male single',none,3,'real estate',24,none,own,1,skilled,1,none,yes,good 'no checking',30,'existing paid',radio/tv,2333,'500<=X<1000','>=7',4,'male single',none,2,car,30,bank,own,1,'high qualif/self emp/mgmt',1,none,yes,good '0<=X<200',12,'existing paid',radio/tv,1158,'500<=X<1000','1<=X<4',3,'male div/sep',none,1,car,26,none,own,1,skilled,1,yes,yes,good '0<=X<200',18,'delayed previously',repairs,6204,'<100','1<=X<4',2,'male single',none,4,'real estate',44,none,own,1,'unskilled resident',2,yes,yes,good '<0',30,'critical/other existing credit','used car',6187,'100<=X<500','4<=X<7',1,'male mar/wid',none,4,car,24,none,rent,2,skilled,1,none,yes,good '<0',48,'critical/other existing credit','used car',6143,'<100','>=7',4,'female div/dep/mar',none,4,'no known property',58,stores,'for free',2,'unskilled resident',1,none,yes,bad 'no checking',11,'critical/other existing credit','new car',1393,'<100','<1',4,'female div/dep/mar',none,4,car,35,none,own,2,'high qualif/self emp/mgmt',1,none,yes,good 'no checking',36,'existing paid',radio/tv,2299,'500<=X<1000','>=7',4,'male single',none,4,car,39,none,own,1,skilled,1,none,yes,good '<0',6,'existing paid','used car',1352,'500<=X<1000',unemployed,1,'female div/dep/mar',none,2,'life insurance',23,none,rent,1,'unemp/unskilled non res',1,yes,yes,good 'no checking',11,'critical/other existing credit','new car',7228,'<100','1<=X<4',1,'male single',none,4,'life insurance',39,none,own,2,'unskilled resident',1,none,yes,good 'no checking',12,'existing paid',radio/tv,2073,'100<=X<500','1<=X<4',4,'female div/dep/mar','co applicant',2,'real estate',28,none,own,1,skilled,1,none,yes,good '0<=X<200',24,'delayed previously',furniture/equipment,2333,'no known savings','<1',4,'male single',none,2,'life insurance',29,bank,own,1,'unskilled resident',1,none,yes,good '0<=X<200',27,'delayed previously','used car',5965,'<100','>=7',1,'male single',none,2,car,30,none,own,2,'high qualif/self emp/mgmt',1,yes,yes,good 'no checking',12,'existing paid',radio/tv,1262,'<100','1<=X<4',3,'male single',none,2,car,25,none,own,1,skilled,1,none,yes,good 'no checking',18,'existing paid','used car',3378,'no known savings','1<=X<4',2,'male single',none,1,'life insurance',31,none,own,1,skilled,1,yes,yes,good '0<=X<200',36,'delayed previously','new car',2225,'<100','>=7',4,'male single',none,4,'no known property',57,bank,'for free',2,skilled,1,yes,yes,bad 'no checking',6,'all paid','new car',783,'no known savings','1<=X<4',1,'male single',guarantor,2,'real estate',26,stores,own,1,'unskilled resident',2,none,yes,good '0<=X<200',12,'existing paid',radio/tv,6468,'no known savings',unemployed,2,'male single',none,1,'no known property',52,none,own,1,'high qualif/self emp/mgmt',1,yes,yes,bad 'no checking',36,'critical/other existing credit',radio/tv,9566,'<100','1<=X<4',2,'female div/dep/mar',none,2,car,31,stores,own,2,skilled,1,none,yes,good '>=200',18,'existing paid','new car',1961,'<100','>=7',3,'female div/dep/mar',none,2,car,23,none,own,1,'high qualif/self emp/mgmt',1,none,yes,good '<0',36,'critical/other existing credit',furniture/equipment,6229,'<100','<1',4,'female div/dep/mar','co applicant',4,'no known property',23,none,rent,2,'unskilled resident',1,yes,yes,bad '0<=X<200',9,'existing paid',business,1391,'<100','1<=X<4',2,'male mar/wid',none,1,'real estate',27,bank,own,1,skilled,1,yes,yes,good '0<=X<200',15,'critical/other existing credit',radio/tv,1537,'no known savings','>=7',4,'male single',guarantor,4,'real estate',50,none,own,2,skilled,1,yes,yes,good '0<=X<200',36,'no credits/all paid',business,1953,'<100','>=7',4,'male single',none,4,'no known property',61,none,'for free',1,'high qualif/self emp/mgmt',1,yes,yes,bad '0<=X<200',48,'no credits/all paid',business,14421,'<100','1<=X<4',2,'male single',none,2,car,25,none,own,1,skilled,1,yes,yes,bad 'no checking',24,'existing paid',radio/tv,3181,'<100','<1',4,'female div/dep/mar',none,4,'life insurance',26,none,own,1,skilled,1,yes,yes,good 'no checking',27,'existing paid',repairs,5190,'no known savings','>=7',4,'male single',none,4,'life insurance',48,none,own,4,skilled,2,yes,yes,good 'no checking',12,'existing paid',radio/tv,2171,'<100','<1',2,'female div/dep/mar',none,2,car,29,bank,own,1,skilled,1,none,yes,good '0<=X<200',12,'existing paid','new car',1007,'>=1000','1<=X<4',4,'male mar/wid',none,1,'real estate',22,none,own,1,skilled,1,none,yes,good 'no checking',36,'existing paid',education,1819,'<100','1<=X<4',4,'male single',none,4,'no known property',37,stores,'for free',1,skilled,1,yes,yes,bad 'no checking',36,'existing paid',radio/tv,2394,'no known savings','1<=X<4',4,'female div/dep/mar',none,4,car,25,none,own,1,skilled,1,none,yes,good 'no checking',36,'existing paid','used car',8133,'<100','1<=X<4',1,'female div/dep/mar',none,2,'life insurance',30,bank,own,1,skilled,1,none,yes,good 'no checking',7,'critical/other existing credit',radio/tv,730,'no known savings','>=7',4,'male single',none,2,'life insurance',46,none,rent,2,'unskilled resident',1,yes,yes,good '<0',8,'critical/other existing credit',other,1164,'<100','>=7',3,'male single',none,4,'no known property',51,bank,'for free',2,'high qualif/self emp/mgmt',2,yes,yes,good '0<=X<200',42,'critical/other existing credit',business,5954,'<100','4<=X<7',2,'female div/dep/mar',none,1,'real estate',41,bank,own,2,'unskilled resident',1,none,yes,good '<0',36,'existing paid',education,1977,'no known savings','>=7',4,'male single',none,4,'no known property',40,none,own,1,'high qualif/self emp/mgmt',1,yes,yes,bad '<0',12,'critical/other existing credit','used car',1526,'<100','>=7',4,'male single',none,4,'no known property',66,none,'for free',2,'high qualif/self emp/mgmt',1,none,yes,good '<0',42,'existing paid',radio/tv,3965,'<100','<1',4,'male single',none,3,car,34,none,own,1,skilled,1,none,yes,bad '0<=X<200',11,'delayed previously',radio/tv,4771,'<100','4<=X<7',2,'male single',none,4,'life insurance',51,none,own,1,skilled,1,none,yes,good 'no checking',54,'no credits/all paid','used car',9436,'no known savings','1<=X<4',2,'male single',none,2,'life insurance',39,none,own,1,'unskilled resident',2,none,yes,good '0<=X<200',30,'existing paid',furniture/equipment,3832,'<100','<1',2,'male mar/wid',none,1,'life insurance',22,none,own,1,skilled,1,none,yes,good 'no checking',24,'existing paid',radio/tv,5943,'no known savings','<1',1,'female div/dep/mar',none,1,car,44,none,own,2,skilled,1,yes,yes,bad 'no checking',15,'existing paid',radio/tv,1213,'500<=X<1000','>=7',4,'male single',none,3,'life insurance',47,stores,own,1,skilled,1,yes,yes,good 'no checking',18,'existing paid',business,1568,'100<=X<500','1<=X<4',3,'female div/dep/mar',none,4,'life insurance',24,none,rent,1,'unskilled resident',1,none,yes,good '<0',24,'existing paid',other,1755,'<100','>=7',4,'female div/dep/mar',guarantor,4,'real estate',58,none,own,1,'unskilled resident',1,yes,yes,good '<0',10,'existing 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