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Machine learning applications to credit risk analysis

Fabrizio Santoriello

Machine learning applications to credit risk analysis.

Rel. Patrizia Semeraro. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2022

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The thesis deals with the theme of machine learning applied to credit risk. Starting from a theoretical introduction of the tools that will be used in the course of the work, we get to observe the performance of 5 different algorithms for binary classification : Logistic regression, Naive Bayes, K-Nearest neighbors, Support Vector Machines and Ensemble trees. In particular, two different datasets are used: a synthetic one, and a real one. Then Ensemble Classifiers, which can be intuitively interpreted as a way to combine predictions generated by different algorithms, are introduced. Several ways of mixing prediction are observed, starting from hard- voting, which is simply a majority voting system, and coming up to more sophisticated methods like stacking and blending. Furthermore, an appreciable improvement in several measures of the classification performance is obtained.

Relators: Patrizia Semeraro
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 101
Corso di laurea: Corso di laurea magistrale in Ingegneria Matematica
Classe di laurea: New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/24056
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