Return to search

Využití vybraných metod strojového učení pro modelování kreditního rizika / Machine Learning Methods for Credit Risk Modelling

This master's thesis is divided into three parts. In the first part I described P2P lending, its characteristics, basic concepts and practical implications. I also compared P2P market in the Czech Republic, UK and USA. The second part consists of theoretical basics for chosen methods of machine learning, which are naive bayes classifier, classification tree, random forest and logistic regression. I also described methods to evaluate the quality of classification models listed above. The third part is a practical one and shows the complete workflow of creating classification model, from data preparation to evaluation of model.

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:360509
Date January 2017
CreatorsDrábek, Matěj
ContributorsWitzany, Jiří, Málek, Jiří
PublisherVysoká škola ekonomická v Praze
Source SetsCzech ETDs
LanguageCzech
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

Page generated in 0.0018 seconds