Calories Burnt Prediction Using Machine Learning Approach
Mohammad Tarek Aziz, R Sudheesh, Renzon Daniel Cosme Pecho, Nayeem Uddin Ahmed Khan, Akba Ull Hasna Era, MD. Abir Chowdhury
Abstract
Calorie burnt prediction by machine learning algorithm” aim to predict the number of calories burnt by an individual during physical activity using machine learning techniques. We collected a dataset that includes features such as heart rate, body temperature, and duration of activity. We used various machine learning models, including XGBoost, linear regression, SVM and random forest, to predict calorie burn based on 15,000 records with seven features. The results indicate that the XGBboost model can accurately predict calorie burn with a minimum mean absolute error of calories. This work contributes to the growing body of research on using machine learning for health and fitness applications and has potential implications for personalized health coaching and wellness tracking. The highest accuracy of training and testing is gained by the XGBboost model with 99.67% with mean absolute error is almost 1.48%.