Diet and Workout Recommendation System Using KNN

The “Diet and Workout Recommendation System” is a personalized platform that provides real-time dietary and fitness guidance based on individual user profiles. Through an intuitive web interface, the system collects data, including age, weight, height, fitness goals, and dietary preferences. The backend processes this data with API integrations and machine learning techniques, including a K-Nearest Neighbors (KNN) model, to generate dynamic meal and workout plans that adapt to user feedback and progress. Using OpenCV for exercise tracking and Flask for efficient data handling, the system delivers instant workout feedback and personalized dietary suggestions. Additionally, location-based APIs recommend local dining options, connecting digital health guidance with real-world choices. Unlike one-size-fits-all platforms, this system emphasizes personalization and adaptability to address the limitations of traditional health tools, offering scalable, user-centered recommendations. The architecture supports future integrations with wearable devices and social features, promoting sustainable lifestyle changes and long-term engagement. This project demonstrates a comprehensive approach to personalized health management, focusing on usability, real-world application, and an evolving user experience.