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master's thesis
Primjena strojnog učenja pri čišćenju podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:033600
Marjanović, Vitomir
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Marjanović, V. (2021). Primjena strojnog učenja pri čišćenju podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:033600

Marjanović, Vitomir. "Primjena strojnog učenja pri čišćenju podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:033600

Marjanović, Vitomir. "Primjena strojnog učenja pri čišćenju podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:033600

Marjanović, V. (2021). 'Primjena strojnog učenja pri čišćenju podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 15 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:033600

Marjanović V. Primjena strojnog učenja pri čišćenju podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 June 15] Available at: https://urn.nsk.hr/urn:nbn:hr:168:033600

V. Marjanović, "Primjena strojnog učenja pri čišćenju podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:033600

Metadata
TitlePrimjena strojnog učenja pri čišćenju podataka
Title (english)Applying maching learning to data cleansing
AuthorVitomir Marjanović
MentorBoris Vrdoljak (mentor)
Committee memberBoris Vrdoljak (predsjednik povjerenstva)
Committee memberMario Brčić (član povjerenstva)
Committee memberMirta Baranović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Cilj istraživanja provedenog ovim diplomskim radom je ispitati razne metode strojnog učenja pri postupku čišćenja podataka u svrhu unapređenja daljnjeg procesa analize podataka. Postupak čišćenja podijeljen je u tri koraka – uklanjanje dupliciranih vrijednosti, detekcija i uklanjanje anomalija te umetanje vrijednosti koje nedostaju. Za svaki od navedenih koraka ispitane su implementacije odabranih algoritama u programskim jezicima Python i R te je u konačnici napravljena njihova usporedba. Prilikom analize odabranih metoda, korišteni su skupovi podataka s karakteristikama koje odgovaraju scenarijima koje se ispituju kako bi rješenje bilo što uočljivije i kvalitetnije. Po završetku usporedbe odabranih metoda, prikazani su zaključci te potencijalni problemi prilikom implementacije navedenih metoda i samog postupka čišćenja u cijelosti.
Abstract (english)
The aim of the conducted research as a part of this master's thesis was to invesigate different machine learning methods in data cleansing in order to improve the following dana analysis process. Data cleansing process was divided in three steps – removing duplicated values, anomaly detection and anomaly removal and missing values imputation. For each of the named steps a selected set of algorithms implemented in Python and R programming languages were tested, and the testing results were provided and compaired to summarize it. In the process, the datasets for the testing were selected to have as many characteristics that describe the tested problem as possible so the output results are clearly visible and representative. By the end of the method comparison, some important conclusions were made, but also some of the underlying problems were pointed out when implementing the tested methods and conducting a data cleansing process as a whole.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:033600
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-13 15:31:14