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This paper aims to predict the calories burnt by a person using machine learning models built on several regression algorithms like linear, random forest, XGBoost, and CatBoost based on gender, age, height, weight, duration of exercise, body temperature, and heartbeat of the person. In addition, the analysis compares the algorithms based on performance metrics like mean absolute error (MAE), mean square error (MSE), and R2 score and determines the most effective algorithm for calorie prediction.

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Regression Techniques for Calorie Prediction: A Comparative Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Mallu Praneeth Reddy,
  • T. A. S. Vardhan,
  • Kura Bhargava Gupta,
  • Nagireddy Deekshitha,
  • Pudari Shrainya Goud,
  • Khalvida Pamarty,
  • Sushama Rani Dutta

摘要

This paper aims to predict the calories burnt by a person using machine learning models built on several regression algorithms like linear, random forest, XGBoost, and CatBoost based on gender, age, height, weight, duration of exercise, body temperature, and heartbeat of the person. In addition, the analysis compares the algorithms based on performance metrics like mean absolute error (MAE), mean square error (MSE), and R2 score and determines the most effective algorithm for calorie prediction.