Predviđanje bankrota tvrtke modelima strojnog učenja

undergraduate thesis

• Title (english): Predicting company bankruptcy by machine learning models
• Author: Karlo Šutalo
• Mentor: Marin Golub (mentor)
• Committee member: Marin Golub (predsjednik povjerenstva)
• Committee member: Antonio Močinić (član povjerenstva)
• Committee member: Bojan Novković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-07-01, 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: strojno učenje; logistička regresija; naivni Bayesov klasifikator; nebalansirani podatci; predviđanje bankrota tvrtke
• Keywords (english): machine learning; logistic regression; naive Bayes classifier; unbalanced data; predicting company bankruptcy
• Language: croatian
• URN:NBN: urn:nbn:hr:168:849724
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-15 11:21:45