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master's thesis
Model glasača na predsjedničkim izborima baziran na metodama strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:136863
Markovinović, Mariela
University of Zagreb
Faculty of Electrical Engineering and Computing

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Markovinović, M. (2020). Model glasača na predsjedničkim izborima baziran na metodama strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:136863

Markovinović, Mariela. "Model glasača na predsjedničkim izborima baziran na metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:136863

Markovinović, Mariela. "Model glasača na predsjedničkim izborima baziran na metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:136863

Markovinović, M. (2020). 'Model glasača na predsjedničkim izborima baziran na metodama strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:136863

Markovinović M. Model glasača na predsjedničkim izborima baziran na metodama strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 December 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:136863

M. Markovinović, "Model glasača na predsjedničkim izborima baziran na metodama strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:136863

Metadata
TitleModel glasača na predsjedničkim izborima baziran na metodama strojnog učenja
Title (english)Voter Model in Crotian Presidential Elections Based on Machine Learning Methods
AuthorMariela Markovinović
MentorDario Bojanjac (mentor)
Committee memberDario Bojanjac (predsjednik povjerenstva)
Committee memberSonja Grgić (član povjerenstva)
Committee memberTomislav Kos (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovome radu prikazani su različiti algoritmi storojnog učenja. Analizirana je ovisnost točnosti modela o hiperparametrima kao i o odabranom skupu podataka za predviđanje. Skup podataka za analiziranje čine dvije ankete koje ispituju stavove glasača neposredno prije prvog i drugog kruga predsjedničkih izbora. Opisane su metode prikupljanja, pripreme i analize anketa. Prvi dio rezultata čine testovi šest algoritama, prikazana je ovisnost točnosti o različitim hiperparametrima. Drugi dio čine predviđanja distribucije glasova pomoću algoritma slučajne šume. Najbolji rezultati dobivaju se korištenjem algoritma slučajne šume, gdje je točnost predviđene distribucije glasova 85%.
Abstract (english)
This master's thesis presents tests of several different machine learning algorithms. The dependence of the accuracy of the model on the hyperparameters, as well as on the choice of the data-set used for the predictions, were analyzed. The data-set used in the analyses consists of two polls designed to examine voter attitudes just before the first and second rounds of the presidential election. Methods of collecting, preparing, and analyzing surveys are described in the master's thesis. The first part of the thesis presents the results of the tests of six different algorithms, expressed via the model accuracy dependence on different hyperparameters. The second part presents the distribution of votes as predicted using a random forest algorithm. The best results and highest accuracy are obtained using a random forest algorithm, where the accuracy of the predicted distribution of votes is 85%.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:136863
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-13 13:00:43