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master's thesis
Primjena strojnog učenja u otkrivanju prijevara s kreditnim karticama
2023. urn:nbn:hr:166:230824
Vidović, Nikolina
University of Split
Faculty of Science
Department of Informatics

Cite this document

Vidović, N. (2023). Primjena strojnog učenja u otkrivanju prijevara s kreditnim karticama (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:230824

Vidović, Nikolina. "Primjena strojnog učenja u otkrivanju prijevara s kreditnim karticama." Master's thesis, University of Split, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:166:230824

Vidović, Nikolina. "Primjena strojnog učenja u otkrivanju prijevara s kreditnim karticama." Master's thesis, University of Split, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:166:230824

Vidović, N. (2023). 'Primjena strojnog učenja u otkrivanju prijevara s kreditnim karticama', Master's thesis, University of Split, Faculty of Science, accessed 16 April 2024, https://urn.nsk.hr/urn:nbn:hr:166:230824

Vidović N. Primjena strojnog učenja u otkrivanju prijevara s kreditnim karticama [Master's thesis]. Split: University of Split, Faculty of Science; 2023 [cited 2024 April 16] Available at: https://urn.nsk.hr/urn:nbn:hr:166:230824

N. Vidović, "Primjena strojnog učenja u otkrivanju prijevara s kreditnim karticama", Master's thesis, University of Split, Faculty of Science, Split, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:166:230824

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Metadata
TitlePrimjena strojnog učenja u otkrivanju prijevara s kreditnim karticama
Title (english)Application of machine learning in credit card fraud detection
AuthorNikolina Vidović
MentorGoran Zaharija (mentor)
Committee memberGoran Zaharija (predsjednik povjerenstva)
Committee memberDivna Krpan (član povjerenstva)
Committee memberDino Nejašmić (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Informatics)
Split
Defense date and country2023-09-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom diplomskom radu istražena je učinkovitost algoritama strojnog učenja - logističke regresije, slučajne šume i SVM-a - u detekciji prijevara s kreditnim karticama na nebalansiranim podacima. Primijenjene su tehnike undersampling, oversampling, SMOTE i ADASYN kako bi se izbalansirali podaci. Rezultati su analizirani na nebalansiranom i balansiranom skupu podataka. Rezultati pokazuju kako se balansiranje podataka značajno može poboljšati pomoću tih tehnika, te kako takvi balansirani skupovi dovode do poboljšanja performansi algoritama u detekciji prijevara.
Abstract (english)
This thesis examines the effectiveness of machine learning algorithms - logistic regression, random forest, and SVM - in credit card fraud detection on imbalanced data. Undersampling, oversampling, SMOTE, and ADASYN techniques were applied to balance the data. Results were evaluated on both imbalanced and balanced datasets. The findings indicate that data balancing techniques, particularly oversampling and SMOTE, significantly enhance performance by addressing class imbalance issues, leading to improved fraud detection accuracy.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:230824
Study programmeTitle: Data Science and Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra podatkovne znanosti i inženjerstva (magistar/magistra podatkovne znanosti i inženjerstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2023-09-18 11:35:29