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The aim of this paper is the analysis of which are the characteristics of an individual that most accurately predict the capability of being a solvable creditor. We used three different classification techniques, namely: logistic regression, k-nearest neighbors and support vector machines. This dataset was taken from a Taiwanese bank facing the credit card crisis of 2006 (http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients)
Assessing multiple model iterations to find the best predictor to whether or not a person will default on their bank loan. The data comes from the Machine Learning Repository - https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients