European Football Player Price Prediction Using Machine Learning

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dc.contributor.advisorDr. Mohammad Ashrafuzzaman Khan (AZK)
dc.contributor.authorS M Minhazur Rahman
dc.contributor.authorRifatul Islam Ovi
dc.contributor.authorAshfe Asade Simon
dc.contributor.authorAnika Shama Siddique
dc.contributor.id1821822642
dc.contributor.id1821197642
dc.contributor.id1911962642
dc.contributor.id1911918642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025
dc.date.accessioned2025-07-15T06:24:58Z
dc.date.available2025-07-15T06:24:58Z
dc.date.issued2023
dc.description.abstractEuropean Football Player Price Prediction Using Machine Learning In most sports, especially football, most coaches and analysts search for key performance indicators using notational analysis. The prediction of European football player prices is an important task for clubs, agents, and investors in the football industry. Making informed judgments about player transfers, contract negotiations, and investments is made possible by accurate price projection. The opportunity to create data-driven models for player price prediction has arisen in recent years due to the accessibility of enormous volumes of player performance data and market information. There are certain factors that influence player prices including individual statistics, age, position, market value and others. Traditionally predictions are made on the basis of these factors. Machine Learning techniques have been a significant source of advanced opportunities to analyze, predict and visualize player prices. In this paper, we estimate players’ market values using four regression models that were tested on the full set of features—linear regression, XG boost, AdaBoost, SVR, Gradient Boosting, and random forests. The dataset containing 19,240 records of Football Player is attained from European Football League and Country. In addition, we want to analyze the information and identify the key elements influencing the estimation of the player market value. For predicting the market values of the players, random forest performed better than other algorithms. In comparison to the baseline, it has the best accuracy score and the lowest error ratio.
dc.description.degreeUndergraduate
dc.identifier.cd600000179
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1265
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU Library
dc.titleEuropean Football Player Price Prediction Using Machine Learning
oaire.citation.endPage56
oaire.citation.startPage1
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