Smart Diet Recommendations: Integrating Machine Learning for Personalized Nutrition

In response to the growing global concern for health and nutrition, this paper presents NutriFlow, an innovative diet recommendation system that leverages machine learning for personalized dietary guidance. The system addresses the critical need for individualized nutrition advice in today's fast-paced world, where diet-related health issues and weight-related diseases are increasingly prevalent. NutriFlow employs a comprehensive approach by integrating multiple components: BMI calculation for establishing personalized calorie targets, collaborative filtering and fuzzy logic algorithms for food recommendations, and an Androidbased step counter for physical activity monitoring. The system collects user-specific data including health metrics, dietary preferences, and restrictions, then processes this information using predefined nutritional guidelines from established health organizations. The methodology incorporates both content-based and collaborative filtering techniques, creating a hybrid recommendation engine that adapts to user feedback over time. Experimental results demonstrate the system's effectiveness in providing personalized meal plans and nutritional information through an intuitive user interface. The study highlights NutriFlow's success in presenting complex nutritional data in an easily digestible format, enabling users to make informed dietary decisions. This research contributes to the intersection of nutrition science and technology, offering a scalable solution for promoting healthier eating habits while maintaining flexibility for individual preferences and restrictions.