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master's thesis
Predviđanje ishoda Formule 1 pomoću strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:525617
Kaselj, Josipa
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Kaselj, J. (2023). Predviđanje ishoda Formule 1 pomoću strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:525617

Kaselj, Josipa. "Predviđanje ishoda Formule 1 pomoću strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:525617

Kaselj, Josipa. "Predviđanje ishoda Formule 1 pomoću strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:525617

Kaselj, J. (2023). 'Predviđanje ishoda Formule 1 pomoću strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 28 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:525617

Kaselj J. Predviđanje ishoda Formule 1 pomoću strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 December 28] Available at: https://urn.nsk.hr/urn:nbn:hr:168:525617

J. Kaselj, "Predviđanje ishoda Formule 1 pomoću strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:525617

Metadata
TitlePredviđanje ishoda Formule 1 pomoću strojnog učenja
Title (english)Predicting Formula 1 Outcomes with Machine Learning
AuthorJosipa Kaselj
MentorMarina Bagić Babac (mentor)
Committee memberMarina Bagić Babac (predsjednik povjerenstva)
Committee memberMirjana Domazet-Lošo (član povjerenstva)
Committee memberMarko Horvat (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-10, Croatia
Abstract
Fokus ovog diplomskog rada bio je razviti učinkovito i djelotvorno algoritamsko rješenje koje koristi tehnike strojnog učenja za točno predviđanje budućih ishoda utrke Formule 1. Analizom povijesnih podataka koji su uključivali statistiku performansi vozača i tima, uz informacije o vremenu, rezultati kvalifikacija i rezultati utrka korišteni su za treniranje modela. Prethodno obrađivanje podataka uključivalo je uklanjanje nedosljednosti u skupovima podataka i prilagodbu samog formata podataka. Implementacijom različitih metoda kao što su linearna regresija, regresor gradijentnog pojačanja, regresor slučajne šume i regresor višeslojnog perceptrona, fokus je bio na predviđanju završnih pozicija svakog od vozača. Drugi cilj bio je predvidjeti samo pobjednika utrke korištenjem modela poput logističke regresije, klasifikatora slučajne šume, klasifikatora k susjeda i klasifikatora višeslojnog perceptrona. Nakon učenja svakog od ovih modela, za njegovu svrhu, testirana je točnost i ukupna izvedba kako bi se provjerila sposobnost modela da generalizira nove podatke.
Abstract (english)
In this Master’s Thesis project main focus was to develop an efficient and effective algorithmic solution utilizing Machine Learning techniques to make accurate predictions about Formula 1 racing future outcomes. By analyzing historical data that included driver and team performance statistics, alongside weather information, qualifying results, and races results were used to train the model. Data pre-processing took many forms including cleaning up inconsistencies in datasets and adapting data formats. Implementing various methods such as Linear Regression, Gradient Boosting Regressor, Random Forest Regressor, and Multi-Layer Perceptron Regressor models, the focus was on forecasting all driver’s finishing positions. The second goal was to predict only the race winner by leveraging Logistic Regression, Random forest Classifier, k−Neighbors Classifier, and Multi-Layer Perceptron Classifier models. After training each of this model, for its purpose, its accuracy and overall performance was tested in order to check model’s ability to generalize to new data.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:525617
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 on2023-10-11 09:16:07