% 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%)@relation credit-rating@attribute A1		{b,a}@attribute A2		REAL@attribute A3		REAL@attribute A4		{u, y, l, t}@attribute A5		{g, p, gg}@attribute A6		{c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff}@attribute A7		{v, h, bb, j, n, z, dd, ff, o}@attribute A8		REAL@attribute A9		{t,f}@attribute A10		{t,f}@attribute A11		REAL@attribute A12		{t,f}@attribute A13		{g, p, s}@attribute A14		REAL@attribute A15		REAL@attribute class	{+, -}@data%% 690 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