International Journal of Science, Engineering and TechnologyAuthors: Jettty Varshitha, Gottipati Abhinaya, Kolusu Ankitha, Ms. S.A. Neelavani
Abstract: Smart Nutribot is an intelligent web- based application designed to provide personalized dietary recommendations using advanced machine learning techniques such as XGBoost and Random Forest. The system aims to address the growing need for customized nutrition plans by analyzing individual user data, including age, gender, weight, health conditions, dietary preferences, and lifestyle habits. By leveraging the predictive capabilities of ensemble learning models, the application generates accurate and tailored meal suggestions that promote healthy living and disease prevention. The web interface ensures user- friendly interaction, allowing users to input their details and receive instant recommendations in an accessible format. XGBoost enhances the model’s performance through efficient gradient boosting, while Random Forest improves robustness by reducing overfitting and increasing prediction accuracy. The integration of these models enables Smart Nutribot to deliver reliable and data-driven dietary guidance. Overall, this project demonstrates the practical application of machine learning in healthcare and nutrition, providing a scalable and efficient solution for personalized diet planning.
