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Improving Credit Classification Using Machine Learning Techniques

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Title: Improving Credit Classification Using Machine Learning Techniques
Author: Lazure, Adam
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Kim, PeterFeng, Zeny
Abstract: The quantification of credit risk is an ever expanding topic of discussion in the field of finance. In order to prevent economic loss, risk management is necessary. A popular method of risk management is the use of statistical techniques in conjunction with machine learning. This thesis takes a unique machine learning approach to credit classification. In particular, it conducts a missing information simulation study on German credit data and makes use of the random forest (RF), support vector machine (SVM), multiple imputation by chained equations (MICE) and predictive mean matching (PMM) methodologies. Results give indication that using MICE in tandem with PMM can be an optimal method of imputation within the context of credit risk data.
Date: 2017-12
Rights: Attribution-NonCommercial-NoDerivs 2.5 Canada
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Attribution-NonCommercial-NoDerivs 2.5 Canada Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 2.5 Canada