Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/5128
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dc.contributor.advisorMokbel, Chaficen_US
dc.contributor.authorHindieh, Jaden_US
dc.date.accessioned2021-07-13T08:35:40Z-
dc.date.available2021-07-13T08:35:40Z-
dc.date.issued2021-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/5128-
dc.descriptionIncludes bibliographical references (p. 60-64)en_US
dc.description.abstractBeing the world’s most popular sports, predicting the results of Football games has always been an interest worldwide. With the increase in availability of football games statistics, using machine learning became more relevant and feasible. This study aims to use previous football matches statistics to predict the future result by applying machine learning on specific subsets of data. The hypothesis is that using machine learning we will be able to have a high prediction percentage given the correct features for the model. Using the convolution neural network machine learning model, we built multiple models to predict football results. We tested the models on a dataset gathered from eleven European countries and spanning across eight football seasons with our main focus being the English Premier League. The results showed that the more data features we were able to provide, the higher the prediction rate for football results. The obtained results showed that by using machine learning we can predict football games results by knowing the participating players in the future matches. These results can open up further research topics such as creating a model to assist managers in team selection for optimizing results.en_US
dc.description.statementofresponsibilityby Jad Hindiehen_US
dc.format.extent1 online resource (x, 64 pages) : ill., tablesen_US
dc.language.isoengen_US
dc.rightsThis object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holderen_US
dc.subjectFootball, Machine Learning, Convolution Neural Network, Dataset, Prediction rate for football resultsen_US
dc.subject.lcshMachine learningen_US
dc.subject.lcshDissertations, Academicen_US
dc.subject.lcshUniversity of Balamand--Dissertationsen_US
dc.titleMachine learning : football results prediction modelen_US
dc.typeThesisen_US
dc.contributor.corporateUniversity of Balamanden_US
dc.contributor.departmentDepartment of Computer Engineeringen_US
dc.contributor.facultyFaculty of Engineeringen_US
dc.contributor.institutionUniversity of Balamanden_US
dc.date.catalogued2021-07-13-
dc.description.degreeMS in Computer Engineeringen_US
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=http://olib.balamand.edu.lb/projects_and_theses/290033.pdfen_US
dc.identifier.OlibID290033-
dc.provenance.recordsourceOliben_US
Appears in Collections:UOB Theses and Projects
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