Paper Key : IRJ************478
Author: Gurrappagaru Sanjana Reddy,Singareddy Velankini Suhas
Date Published: 11 Oct 2023
Abstract
In an age where health and fitness are of increasing importance, accurately estimating calorie expenditure plays a crucial role in enhancing personal well-being. This paper conducts a comprehensive investigation into the use of machine learning, particularly the XGBoost regression algorithm, to achieve precise calorie burn predictions. Our research capitalizes on datasets containing diverse physical activity and biometric data, facilitating the creation of robust predictive models. By conducting a rigorous comparative analysis against alternative regression methods, our study underscores the superior performance of XGBoost in the realm of calorie burn prediction, employing two datasets comprising over 15,000 data points. These predictions are grounded in the metabolic equivalent of task (MET) chart and associated formulas.
DOI LINK : 10.56726/IRJMETS45191 https://www.doi.org/10.56726/IRJMETS45191