1. Title: 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: 15 + class attribute 7. Attribute Information: A1: b, a. A2: continuous. A3: continuous. A4: u, y, l, t. A5: g, p, gg. A6: c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff. A7: v, h, bb, j, n, z, dd, ff, o. A8: continuous. A9: t, f. A10: t, f. A11: continuous. A12: t, f. A13: g, p, s. A14: continuous. A15: continuous. A16: +,- (class attribute) 8. Missing Attribute Values: 37 cases (5%) have one or more missing values. The missing values from particular attributes are: A1: 12 A2: 12 A4: 6 A5: 6 A6: 9 A7: 9 A14: 13 9. Class Distribution +: 307 (44.5%) -: 383 (55.5%)