DIET PLANNING AND RECOMMENDATION SYSTEM USING ML AND MERN STACK

DIET PLANNING AND RECOMMENDATION SYSTEM USING ML AND MERN STACK

Pyla.Uma1 , Chilla Rupa Devi2, Budumuru S.V.S.K.Akshay3, Boddepalli Sai Akhil4, Dasi ArunTeja5
1Assistant Professor, Computer Science and Engineering, Raghu Engineering College, Visakhapatnam

[2-5]B.Tech Students, Computer Science and Engineering, Raghu Institute Of Technology, Visakhapatnam

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ABSTRACT

 The "Diet Planning and Recommendation System Using ML and MERN Stack" project aims to develop an innovative solution to address the challenge of personalized diet planning and recommendation. With the rising awareness of the importance of nutrition in maintaining overall health and wellness, there is a growing demand for tools that can offer tailored dietary guidance to individuals based on their unique needs and preferences.

This project leverages the power of Machine Learning (ML) algorithms and the MERN (MongoDB, Express.js, React.js, Node.js) stack to create a comprehensive and user-friendly platform. The system collects user data encompassing demographic information, health metrics, dietary habits, and goals. Using ML techniques such as regression, classification, and clustering, the system analyzes this data to generate personalized diet plans and recommendations.

The backend of the system, built on Node.js and Express.js, manages data storage and processing, while the frontend, developed with React.js, provides an intuitive interface for users to interact with the system. MongoDB serves as the database, ensuring scalability and flexibility in data management.

The ML models continuously learn and adapt based on user feedback and outcomes, enhancing the accuracy and effectiveness of the recommendations over time. Reinforcement learning techniques are employed to optimize diet plans based on real-world outcomes and user satisfaction.

By integrating ML with the MERN stack, this project offers a novel approach to diet planning and recommendation, empowering individuals to make informed dietary choices and improve their overall health and well-being.

KeyWords:Diet Planning ,Recommendation System,Machine Learning,(ML),MERNStack,Personalized,Nutrition,Health,,,Metrics,Dietary,Habits,Regression,Classification,Node.js, ,React.js,,MongoDB,User,Feedback,ReinforcementLearni,Real-world Outcomes,User Satisfaction,Wellness,Informed Dietary Choices