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Predicering av låntagares återbetalningsförmåga med hjälp av maskininlärningsmetoder: En jämförelse av metoderna logistisk regression, random forest, K-nearest neighbor och support vector machines
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2020 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis aims to investigate how statistical machine learning methods can be used to predict an individual's risk of default with regards to chosen model evaluation parameters. Logistic regression, random forest, K-nearest neighbor and support vector machines were the investigated techniques. The methods were applied on a dataset from the international consumer finance provider Home Credit Group. The results show that none of the implemented models give useful predictions for customers default risk. The reason is that all models struggle to identify individuals who do not repay their loans. The thesis concludes that an improved variable selection method and enhanced data processing probably could increase the accuracy of the models.

Place, publisher, year, edition, pages
2020. , p. 37
Keywords [sv]
Logistisk regression, random forest, K-nearest neighbor, support vector machines
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-403607OAI: oai:DiVA.org:uu-403607DiVA, id: diva2:1390163
Subject / course
Statistics
Supervisors
Examiners
Available from: 2020-02-03 Created: 2020-01-31 Last updated: 2020-02-03Bibliographically approved

Open Access in DiVA

Uppsats statistik C Jakob Leth och Ellen Ahlberg(674 kB)18 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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