Browsing by Author "Anika Shama Siddique"

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    European Football Player Price Prediction Using Machine Learning
    (North South University, 2023) S M Minhazur Rahman; Rifatul Islam Ovi; Ashfe Asade Simon; Anika Shama Siddique; Dr. Mohammad Ashrafuzzaman Khan (AZK); 1821822642; 1821197642; 1911962642; 1911918642
    European 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.