This section describes main characteristics of the crx data set and its attributes:
General information
Credit Approval data set 
Type  Classification  Origin  Real world 
Features  15  (Real / Integer / Nominal)  (3 / 3 / 9) 
Classes  2 
Missing values?  Yes 
Total instances  690 
Instances without missing values  653 
Attribute description
Attribute  Domain  Attribute  Domain 
A1  {b, a}  A9  {t, f} 
A2  [16.0, 8025.0]  A10  {t, f} 
A3  [0.0, 26335.0]  A11  [0, 67] 
A4  {u, y, l}  A12  {f, t} 
A5  {g, p, gg}  A13  {g, s, p} 
A6  {w, q, m, r, cc, k, c, d, x, i, e, aa, ff, j}  A14  [0, 2000] 
A7  {v, h, bb, ff, j, z, o, dd, n}  A15  [0, 100000] 
A8  [0.0, 14415.0]  Class  {positive, negative} 
Additional 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 data set is interesting because there is a good mix of attributes: continuous, nominal with small numbers of values, and nominal with larger numbers of values. This data set is a extended version of the Australian data set (it has one more attribute, A8).
In this section you can download some files related to the crx data set:
 The complete data set already formatted in KEEL formatcan be downloaded from here.
 A copy of the data set already partitioned by means of a 10folds cross validation procedure can be downloaded from here.
 A copy of the data set already partitioned by means of a 5folds cross validation procedure can be downloaded from here.
 The header file associated to this data set can be downloaded from here.
 This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository . The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Credit+Approval.
