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A Comparison of Classification Methods for Credit Card Approval Using R  

Song, Jong-Woo (Dept. of Statistics, Ewha Womans University)
Publication Information
Journal of Korean Society for Quality Management / v.36, no.1, 2008 , pp. 72-79 More about this Journal
Abstract
The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of packages (libraries) made by R-users in the world. We will use most widely used methods, LDNQDA, Logistic Regression, CART (Classification and Regression Trees), neural network, and SVM (Support Vector Machines) for comparisons.
Keywords
R; Credit; Classification; Supervised Learning; Cross-Validation;
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