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undergraduate thesis
Predviđanje bankrota tvrtke modelima strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:849724
Šutalo, Karlo
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šutalo, K. (2022). Predviđanje bankrota tvrtke modelima strojnog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:849724

Šutalo, Karlo. "Predviđanje bankrota tvrtke modelima strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:849724

Šutalo, Karlo. "Predviđanje bankrota tvrtke modelima strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:849724

Šutalo, K. (2022). 'Predviđanje bankrota tvrtke modelima strojnog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:849724

Šutalo K. Predviđanje bankrota tvrtke modelima strojnog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 June 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:849724

K. Šutalo, "Predviđanje bankrota tvrtke modelima strojnog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:849724

Metadata
TitlePredviđanje bankrota tvrtke modelima strojnog učenja
Title (english)Predicting company bankruptcy by machine learning models
AuthorKarlo Šutalo
MentorMarin Golub (mentor)
Committee memberMarin Golub (predsjednik povjerenstva)
Committee memberAntonio Močinić (član povjerenstva)
Committee memberBojan Novković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Tema ovog rada je predviđanje bankrota tvrtke modelima strojnog učenja. Na početku je ukratko opisana povijest i podijela strojnog čcenja te problemi koji se rješavaju njime. Nakon toga, objašnjeni su modeli logističke regresije i naivnog bayesovog klasifikatora. Navedeni modeli koriste se kao implementacija iz alata Scikit-learn u sklopu praktičnog rada napisanog u jeziku Python. Napravljeno je i optimiranje parametara modela kako bi se poboljšale performanse. Rezultati modela prije i nakon optimizacije prikazani su grafički i tablično te je njihova usporedba napravljena na kraju 5. poglavlja.
Abstract (english)
The topic of this paper is the prediction of company bankruptcy by machine learning models. At the beginning, the history and division of machine learning and the problems it solves are briefly described. After that, the models of logistic regression and the naive Bayesian classifier are explained. These models are used as an implementation of the Scikit-learn tool as part of a practical paper written in Python. Optimization of model parameters has also been done to improve performance. The results of the models before and after the optimization are presented graphically and tabularly and their comparison is made at the end of Chapter 5.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:849724
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-15 11:21:45