Description of the Dataset: THIS CREDIT DATA ORIGINATES FROM QUINLAN (see below). 1. Title: Australian Credit Approval 2. Sources: (confidential) Submitted by quinlan@cs.su.oz.au 3. Past Usage: See Quinlan, * "Simplifying decision trees", Int J Man-Machine Studies 27, Dec 1987, pp. 221-234. * "C4.5: Programs for Machine Learning", Morgan Kaufmann, Oct 1992 4. Relevant Information: This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data. This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values. 5. Number of Instances: 690 6. Number of Attributes: 14 + class attribute 7. Attribute Information: THERE ARE 6 NUMERICAL AND 8 CATEGORICAL ATTRIBUTES. THE LABELS HAVE BEEN CHANGED FOR THE CONVENIENCE OF THE STATISTICAL ALGORITHMS. FOR EXAMPLE, ATTRIBUTE 4 ORIGINALLY HAD 3 LABELS p,g,gg AND THESE HAVE BEEN CHANGED TO LABELS 1,2,3. A1: 0,1 CATEGORICAL a,b A2: continuous. A3: continuous. A4: 1,2,3 CATEGORICAL p,g,gg A5: 1, 2,3,4,5, 6,7,8,9,10,11,12,13,14 CATEGORICAL ff,d,i,k,j,aa,m,c,w, e, q, r,cc, x A6: 1, 2,3, 4,5,6,7,8,9 CATEGORICAL ff,dd,j,bb,v,n,o,h,z A7: continuous. A8: 1, 0 CATEGORICAL t, f. A9: 1, 0 CATEGORICAL t, f. A10: continuous. A11: 1, 0 CATEGORICAL t, f. A12: 1, 2, 3 CATEGORICAL s, g, p A13: continuous. A14: continuous. A15: 1,2 +,- (class attribute) 8. Missing Attribute Values: 37 cases (5%) HAD one or more missing values. The missing values from particular attributes WERE: A1: 12 A2: 12 A4: 6 A5: 6 A6: 9 A7: 9 A14: 13 THESE WERE REPLACED BY THE MODE OF THE ATTRIBUTE (CATEGORICAL) MEAN OF THE ATTRIBUTE (CONTINUOUS) 9. Class Distribution +: 307 (44.5%) CLASS 2 -: 383 (55.5%) CLASS 1 10. There is no cost matrix.