Title: Machine Learning for Credit Scoring Author: Elena Myazina Department / Institute: Department of Theoretical Computer Science and Mathematical Logic Supervisor of the master thesis: Mgr. Martin Pilát, Ph.D, Department of Theoretical Computer Science and Mathematical Logic Abstract: Credit scoring is a technique used by banks to evaluate their clients who ask for different types of loan. Its goal is to predict, whether a given client will pay their loan or not. Traditionally, mathematical models based on logistic regression are used for this task. In this thesis, we approach the problem of credit scoring from a machine learning point of view. We investigate several machine learning methods (including neural networks, random forests, support vector machines and other), and evaluate their performance for the credit scoring task on three publicly available datasets.. Keywords: machine learning, credit scoring,logistic regression, neural networks, random forest
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:368284 |
| Date | January 2017 |
| Creators | Myazina, Elena |
| Contributors | Pilát, Martin, Neruda, Roman |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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