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master's thesis
Prediktivna analiza podataka s vremenskim slijedom iz domene sporta
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. urn:nbn:hr:168:649677
Črljenec, Nino
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Črljenec, N. (2014). Prediktivna analiza podataka s vremenskim slijedom iz domene sporta (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:649677

Črljenec, Nino. "Prediktivna analiza podataka s vremenskim slijedom iz domene sporta." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. https://urn.nsk.hr/urn:nbn:hr:168:649677

Črljenec, Nino. "Prediktivna analiza podataka s vremenskim slijedom iz domene sporta." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. https://urn.nsk.hr/urn:nbn:hr:168:649677

Črljenec, N. (2014). 'Prediktivna analiza podataka s vremenskim slijedom iz domene sporta', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:649677

Črljenec N. Prediktivna analiza podataka s vremenskim slijedom iz domene sporta [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2014 [cited 2025 January 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:649677

N. Črljenec, "Prediktivna analiza podataka s vremenskim slijedom iz domene sporta", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2014. Available at: https://urn.nsk.hr/urn:nbn:hr:168:649677

Metadata
TitlePrediktivna analiza podataka s vremenskim slijedom iz domene sporta
Title (english)Predictive analysis of time series data in sports
AuthorNino Črljenec
MentorMarko Banek (mentor)
Committee memberMarko Banek (predsjednik povjerenstva)
Committee memberBoris Vrdoljak (član povjerenstva)
Committee memberDamir Pintar (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2014-07-04, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada bio je razviti model dubinske analize podataka za predviđanje ishoda nogometne utakmice na temelju prethodnih rezultata. Željeni ishod obavljenog istraživanja je bio odrediti idealni vremenski interval koji daje najbolje rezultate predviđanja. Kao pristup rješavanju navedenog problema odabran je CRISP – DM model. Podaci za analizu su preuzeti s web stranice sportskih rezultata te transformirani i učitani u bazu podataka. Izgrađena su tri različita modela podataka za predviđanje ishoda utakmice s naglaskom na različite vremenske intervale. Svaki od modela evaluiran je pomoću programskog alata Weka koji je potom generirao prediktivne modele pomoću tri različita algoritma: stabla odluke J48, linearne regresije i logističke regresije. Za svaki model, dani su rezultati predikcije koji su međusobno uspoređeni te je odabran najbolji za korištenje budućih predviđanja. Također, razvijeno je korisničko sučelje za pregledniji prikaz dobivenih rezultata.
Abstract (english)
The topic of the thesis was to develop a data mining model for predicting the outcome of football matches based on previous results. The desired outcome of the research was to determine the ideal time interval that gives the best prediction results. The CRISP - DM model was selected as a solution for described problems. Data for the analysis was downloaded from the sports results website which was later transformed and loaded into the database. Three different data models were built, each of them using different time intervals for prediciting the outcome. Each of the models was evaluated using the Weka software tool which then generated predictive models by using three different algorithms: J48 decision tree, linear regression and logistic regression. For each built model, the results of the predictions were compared with other models. After that the best model was selected for future predictions. Also, a graphic user interface was developed for better preview of collected data.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:649677
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2018-02-06 08:18:58