MACHINE LEARNING APPROACHES FOR CALORIE BURN ANALYSIS AND PREDICTION
Keywords:
Machine learning, precision, accuracy, classification, XGBoost regressor, Linear Regression.Abstract
Aim: It is widely acknowledged that running is the most effective activity for burning maximum calories per hour. Additionally, fixed bicycling, swimming, and HIIT (High-Intensity Interval Training) exercises are also excellent choices for calorie burning. HIIT exercises, in particular, have been shown to continue calorie burning for up to 24 hours after the workout. However, accurately predicting the number of calories burned during a specific exercise remains a challenge due to variations in individual physiology and fitness levels. To address this challenge, we utilized an exercise dataset obtained from the UCI Machine Learning repository to forecast the calories burned during different exercises.
Methodology: Many individuals are curious about the calories burned during their workouts and the effectiveness of their weight loss plans. To tackle this issue, we employed machine learning algorithms such as XGBoost regressor and Linear Regression.
Results: The results demonstrate that XGBoost regressor and Linear Regression outperform other approaches in predicting calorie expenditure during exercise.
