Food Recommendation System Using Machine Learning

This research paper explores the use of cosine similarity in personalized food recommendation systems. With the increasing number of food items available to consumers, it has become essential to provide relevant and personalized food recommendations to improve customer satisfaction. This study aims to contribute to the field of food technology by introducing a more accurate and efficient method for recommending food items to users. By utilizing unsupervised machine learning techniques, this study seeks to provide insights into the factors that influence food preferences and how cosine similarity can help improve recommendation accuracy. The findings of this research can assist food industry stakeholders in making better decisions related to menu planning, food marketing, and personalized food delivery, ultimately leading to improved customer experiences and increased revenue