International Journal of Advanced Research in Computer and Communication Engineering

Abstract: With the rapid digitalization of health monitoring systems, accurate calorie estimation has become a necessity for individuals aiming to manage fitness and weight effectively. Traditional calorie calculators depend on generalized formulas or physical fitness devices, which often fail to capture individual physiological variations such as metabolism, heart rate, and body temperature. This research presents a Machine Learningbased Calorie Burn Tracker that uses six input features — Age, Weight, Gender, Exercise Duration, Body Temperature, and Heart Rate — to predict calorie burn precisely. The system is implemented in Python using the PyCharm IDE, and employs Linear Regression as the core prediction model. Supporting tools such as Django (for web interface development) and Joblib (for model serialization and deployment) enhance usability. A comparative performance study with Decision Tree and Random Forest algorithms confirms that Linear Regression provides optimal results with over 94% accuracy and minimal computation time. The proposed system delivers a cost-effective, scalable, and reliable health tracking solution suitable for integration into mobile and IoT-based platforms.

Keywords: Machine Learning, Calorie Prediction, Linear Regression, Django, Joblib, Python, Fitness Tracking, Health Analytics.

Downloads: | DOI: 10.17148/IJARCCE.2025.141066