Authors - Sergey Kubinski, Emil Hadzhikolev Abstract - This paper presents a Smart Fitness Assistant system for generating personalized workout and dietary recommendations using machine learning and domain-informed physiological feature engineering. The proposed approach incorporates indicators such as Basal Metabolic Rate, Total Daily Energy Expenditure, and target caloric intake derived from user data. The recommendation task is formulated as a multi-class classification problem for exercise and diet planning. Decision Tree, Multilayer Perceptron, and Random Forest models are evaluated using both baseline and enriched feature sets. Experimental results demonstrate that the inclusion of physiological features improves predictive performance, with Random Forest achieving the highest accuracy. The developed system is implemented within a modular software architecture that supports user interaction, recommendation generation, data management, and progress tracking.
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