A Machine Learning-Based Web Application to Provide Personalized Diet and Yoga Recommendations

Abstract

In this paper, we propose a machine learning-based web application that provides personalized diet and yoga recommendations. This application integrates multiple machine learning models and image processing techniques to deliver tailored suggestions based on user input. The system employs natural language processing (NLP) for extracting relevant features from user queries and utilizes deep learning models for image classification to recommend yoga poses. Key innovations include a Word2Vec-based model for benefit extraction and a K-Nearest Neighbors (KNN) algorithm for dietary suggestions. We address challenges such as improving prediction accuracy, optimizing image processing, and enhancing overall system reliability. Experimental results demonstrate significant improvements in recommendation accuracy and user satisfaction compared to existing systems. The system achieves these advancements through a well-defined architecture and rigorous evaluation metrics