Predictive Modelling on Caloric Expenditure: Exploring Machine Learning Techniques for Enhanced Analysis

Predicting caloric expenditure accurately remains a significant challenges due to individual variability, which frequently results in mistakes in established approaches such as metabolic computation and self-reported data. This research tackles this issue by examining the efficacy of several machine learning methods for forecasting calorie expenditure. The study compares a variety of models, including Linear Regression, eXtreme Gradient Boosting (XGB), Gradient Boosting Regressor (GBR), Ada Regressor, ExtraTrees Regressor, Random Forest, and Stacking Regressor. The XGBoost model has outstanding performance characteristics, including a mean absolute error (MAE) of 8.494312, mean square error (MSE) of 159.6425, root mean square error (RMSE) of 12.6350, and a high R-squared (R2) score of 0.9603. These results demonstrate that machine learning algorithms can produce more precise forecasts of calorie expenditure than traditional methods. This study improves knowledge and implementation of predictive models in the realm of health by employing sophisticated modeling methodologies.