Intelligent Fitness Data Analysis and training Effect Prediction Based on Machine Learning Algorithms

As smart wearable devices become increasingly popular, the collection and analysis of large-scale sports data has provided new possibilities for optimizing training effects. Based on the fitness tracker dataset, this study conducts in-depth analysis and modeling of user exercise data using machine learning methods. The study compares multiple algorithms including random forest, decision tree, support vector machine, and naive Bayes. Results show that the random forest model performs best in prediction accuracy and model stability, with a Mean Absolute Error (MAE) of 156.42 calories and a coefficient of determination (R²) of 0.857. Feature importance analysis reveals that body fat percentage, maximum heart rate, and age are the three most significant factors affecting exercise outcomes, providing a scientific basis for selecting monitoring indicators in intelligent sports equipment. The study also finds that the optimal single exercise duration is between 1.2-1.6 hours, and immediate physiological indicators are more valuable than long-term exercise habits in predicting short-term exercise effects. The findings of this study provide theoretical support and practical guidance for the optimization of intelligent physical exercise equipment, which is significant for improving user training effects. This study bridges the gap between raw fitness data and actionable insights for equipment optimization.