% 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%