prikaz prve stranice dokumenta Mikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota
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master's thesis
Mikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:748022
Blagaić, Sven
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Blagaić, S. (2023). Mikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:748022

Blagaić, Sven. "Mikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:748022

Blagaić, Sven. "Mikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:748022

Blagaić, S. (2023). 'Mikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 19 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:748022

Blagaić S. Mikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 19] Available at: https://urn.nsk.hr/urn:nbn:hr:168:748022

S. Blagaić, "Mikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:748022

Metadata
TitleMikroservisna web aplikacija za usporedbu ML algoritama za predviđanje bankrota
Title (english)Microservice web application for comparing ML algorithms for bankruptcy prediction
AuthorSven Blagaić
MentorMarina Bagić Babac (mentor)
Committee memberMarina Bagić Babac (predsjednik povjerenstva)
Committee memberMirjana Domazet-Lošo (član povjerenstva)
Committee memberMarko Horvat (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-10, Croatia
Abstract
Zadnjih godina povećao se broj bankrota raznih tvrtki zbog različitih ekonomskih i neekonomskih razloga. Predviđanje bankrota pokušava detektirati ovakve slučajeve temeljem raznih financijskih značajki kako bi se spriječilo njihovo buduće pojavljivanje. Cilj ovog rada bio je razviti web aplikaciju čija je svrha omogućiti lakšu usporedbu algoritama strojnog učenja koji se koriste za predviđanje bankrota. Dodatno, napravljena je detaljna usporedba korištenih algoritama koristeći rezultate dobivene iz aplikacije. Pet modela koji su se koristili bili su stablo odluke, slučajna šuma, k najbližih susjeda, neuronska mreža - višeslojni perceptron i stroj potpornih vektora te su bili testirani na četiri različite mjere vrednovanja - točnost, preciznost, odziv i F1 mjera. Odziv, odnosno udio stvarno pozitivnih primjera, je smatran najbitnijom mjerom jer propuštanje uočavanja slučaja bankrota ima veće posljedice od pogrešnog klasificiranja solventne tvrtke kao bankrotirajuće.
Abstract (english)
Company bankruptcy cases have risen in recent years due to different economic and non-economic factors. Bankruptcy prediction attempts to detect these events based on different financial features in order to prevent future occurences. The aim of this thesis was to develop a web application whose purpose is to enable easier comparison of different machine learning algorithms used for bankruptcy prediction. In addition, the algorithms were compared in detail using the results gained from the application. The five models in question were Decision tree, Random forest, K-nearest neighbors, Neural network - multilayer perceptron and Support vector machine, and they were tested on four different metrics – accuracy, precision, recall and F1 score while Type I and Type II errors were also considered. Recall, or the true positive rate, was regarded as the most important metric since failing to identify a bankruptcy case is usually more costly than falsely interpreting a solvent company as bankrupt.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:748022
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-10-11 08:26:40