credit-g.arff

是UCI数据库中的一些有代表性的数据集

% 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
%