diplomsko delo
David Šeruga (Author), Jure Žabkar (Mentor)

Abstract

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.

Keywords

analiza podatkov;podatkovno rudarjenje;stečajni postopek;napovedovanje stečajnih postopkov;napovedovanje dolžin stečajnih postopkov;visokošolski strokovni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [D. Šeruga]
UDC: 004.85:347.736(043.2)
COBISS: 189956099 Link will open in a new window
Views: 37
Downloads: 3
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: English
Secondary title: Analysis of data on bankruptcy proceedings
Secondary 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.
Secondary keywords: analysis;data mining;machine learning;bankruptcy;bankruptcy process;diploma;Strojno učenje;Umetna inteligenca;Računalništvo;Stečaj;Univerzitetna in visokošolska dela;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000470
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 50 str.
ID: 23520398