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master's thesis
Web struganje u strojnom učenju
2023. urn:nbn:hr:166:052230
Zelić, Petra
University of Split
Faculty of Science
Department of Informatics

Cite this document

Zelić, P. (2023). Web struganje u strojnom učenju (Master's thesis). Split: University of Split, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:166:052230

Zelić, Petra. "Web struganje u strojnom učenju." Master's thesis, University of Split, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:166:052230

Zelić, Petra. "Web struganje u strojnom učenju." Master's thesis, University of Split, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:166:052230

Zelić, P. (2023). 'Web struganje u strojnom učenju', Master's thesis, University of Split, Faculty of Science, accessed 29 October 2024, https://urn.nsk.hr/urn:nbn:hr:166:052230

Zelić P. Web struganje u strojnom učenju [Master's thesis]. Split: University of Split, Faculty of Science; 2023 [cited 2024 October 29] Available at: https://urn.nsk.hr/urn:nbn:hr:166:052230

P. Zelić, "Web struganje u strojnom učenju", Master's thesis, University of Split, Faculty of Science, Split, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:166:052230

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Metadata
TitleWeb struganje u strojnom učenju
Title (english)Web Scraping In Machine Learning
AuthorPetra Zelić
MentorSaša Mladenović (mentor)
Committee memberSaša Mladenović (predsjednik povjerenstva)
Committee memberDivna Krpan (član povjerenstva)
Committee memberDino Nejašmić (član povjerenstva)
GranterUniversity of Split
Faculty of Science
(Department of Informatics)
Split
Defense date and country2023-09-22, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Razvojem interneta, povećala se dostupnost ogromne količine raznolikih podataka na webu, no većina tih informacija je nestrukturirana, što otežava njihovu obradu i analizu. Cilj ovog rada je prikazi mogućnosti automatiziranog prikupljanja i pretvaranja nestrukturiranih podataka iz web stranica u strukturiranu formu,tehnikama i metodama web struganja stvarajući kvalitetan skup podataka potreban za razvoj uspješnih modela strojnog učenja. Rezultati ukazuju na prednosti i nedostatke u razvoju web strugača, ali također i na potencijal pristupa koji bi se mogao ostvariti na reprezentativnom skupu podataka.
Abstract (english)
The development of the Internet has increased the availability of vast amounts of diverse data on the web. However, the majority of this information is unstructured, which presents a challenge in its processing and analysis. The goal of this paper is to demonstrate the possibilities of automated data collection and conversion of unstructured web data into structured data using web scraping techniques and methods. This creates a high-quality dataset necessary for the development of successful machine learning models. The results discuss the advantages and disadvantages of web scraping development, as well as the potential approach that can be achieved on a representative dataset.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:166:052230
Study programmeTitle: Computer Science; specializations in: major in teaching
Course: major in teaching
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra edukacije informatike (magistar/magistra edukacije informatike)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2023-09-25 11:03:01