This file concerns credit card applications of 690 households.
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This data set has been split into two components for the convenience of the model training.
X consists of with 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.
y indicates whether the application has been
The training set
AusCredit.tr contains a randomly selected set of 400 subjects,
AusCredit.te contains the remaining 290 subjects.
all 690 objects.
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.
Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
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