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Improving Credit Classification Using Machine Learning Techniques

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dc.contributor.advisor Kim, Peter
dc.contributor.advisor Feng, Zeny
dc.contributor.author Lazure, Adam
dc.date.accessioned 2017-12-05T19:55:02Z
dc.date.available 2017-12-05T19:55:02Z
dc.date.copyright 2017-12
dc.date.created 2017-11-14
dc.date.issued 2017-12-05
dc.identifier.uri http://hdl.handle.net/10214/11962
dc.description.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. en_US
dc.language.iso en en_US
dc.rights Attribution-NonCommercial-NoDerivs 2.5 Canada *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/2.5/ca/ *
dc.subject credit risk en_US
dc.subject multiple imputation by chained equations en_US
dc.subject support vector machine en_US
dc.subject random forest en_US
dc.subject predictive mean matching en_US
dc.title Improving Credit Classification Using Machine Learning Techniques en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Bachelor of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US


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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