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July 10, 2025Transactions on Computer Science and Intelligent Systems Research

Implementation of Random Forest Algorithm Based Medal Count Prediction

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Authors

SCShuai ChenShanghai Polytechnic UniversityJWJunjie WangUniversity of North Carolina at Chapel HillYWYuhang WuThe University of Sydney

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Implication

This analysis predicts medal outcomes for the Olympic Games using random forest and machine learning, suggesting coaching effects may influence results.

Key Points

  • The predictive model identifies potential medal outcomes using random forest regression, focusing on accuracy.
  • The analysis predicts 2028 Olympic medal counts for non-winning countries, enhancing competitive insights.
  • A regression model centered on 'coaching effect' assesses impacts on medal counts linked to coaching investments.
  • This methodological approach highlights the significant role of coaching in sports performance predictions.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68af55ccad7bf08b1eadc15dhttps://doi.org/10.62051/mddr0e54
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Also Consider

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  1. 1Research on Medal Prediction Model for 2028 Olympic Games Based on Linear Regression and Random Forests2025
  2. 2Research on Olympic Medal Prediction Based on Random Forest Regression2025
  3. 3Prediction Of Medals Based on Machine Learning and OLS Statistical Regression Models2025
  4. 4Prediction on Olympic Medal Based on Random Forest and Logistic Regression2025
  5. 5Research on Olympic Medals Prediction Model Based on Linear Regression and Logistic Regression2025