PREDICTION OF 2024 PREMIER LEAGUE FINAL STANDINGS USING RANDOM FOREST ALGORITHM

Ulfatun Nadifa

Abstract


This study aims to predict the final standings of teams in the 2024 Premier League season using a machine learning approach based on practices from the Intro to Machine Learning module on the Kaggle platform. The dataset includes team performance statistics such as points, wins, draws, and losses. The implemented model is Random Forest Regressor, trained using statistical data from the competition season and evaluated with the Mean Absolute Error (MAE) metric. Evaluation results show an MAE value of 1.06, meaning the model's prediction error averages only about one rank from the team's actual position in the final standings. This finding indicates that the Random Forest algorithm is quite effective in capturing the relationship patterns between team performance and final rankings. This study provides evidence that machine learning methods can be effectively applied in the sports domain, particularly for data-driven analysis and prediction of football standings

Keywords


Machine Learning; Random Forest; Premier League; Football Analytics; Sports Prediction;

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