Analiza podatkov o stečajnih postopkih

ŠERUGA, DAVID (author), Žabkar, Jure (mentor)

V tej diplomski nalogi se spoprimemo s problemom napovedovanja dolžin stečajnih postopkov v Sloveniji z uporabo različnih metod strojnega učenja. Imamo širok nabor podatkov o samih postopkih, od leta 2008 naprej in tudi podatke o podjetjih in posameznikih v postopkih. Začeli smo s pripravo podatkov na statistično analizo v kateri smo dobili dober vpogled v problem, ki je pred nami. Na koncu smo zaključili z napovedovanjem dolžin stečajnih postopkov, pri čemer smo ugotovili, da je model XGB naboljši pri napovedovanju z MAE 240 dni. Takoj za njim pa sta bila modela naključni gozd in gradient boost z MAE 243 dni.

• Language: Slovenian
• Keywords: analiza, podatkovno rudarjenje, strojno učenje, stačaj, stečajni postopek
• Work type: Bachelor thesis/paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2024
• PID: 20.500.12556/RUL-155971
• COBISS.SI-ID: 189956099
• Publication date in RUL: 25.04.2024

ŠERUGA, DAVID, 2024, Analiza podatkov o stečajnih postopkih [online]. Bachelor’s thesis. [Accessed 19 May 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=155971

Secondary language

• Language: English
• Title: Analysis of data on bankruptcy proceedings

Abstract

In this thesis, we deal with the problem of predicting the length of bankruptcy proceedings in Slovenia using different machine learning methods. We have a wide range of data on the proceedings themselves, from 2008 onwards, as well as data on companies and individuals in the proceedings. We started with the preparation of data for statistical analysis, in which we got a good insight into the problem in front of us. Finally, we ended up predicting the lengths of bankruptcy proceedings, finding that the XGBoost model was the best at predicting with MAE of 240 days. Immediately behind it were the random forest and gradient boost models with MAE of 243 days.

• Keywords: analysis, data mining, machine learning, bankruptcy, bankruptcy process