Food Recommendation System Using K-means Clustering and Random Forest Algorithm

A balanced diet is a cornerstone of a healthy lifestyle, with far-reaching effects on overall well-being and the prevention of chronic diseases. In our increasingly health-conscious world, the importance of tailored dietary choices has never been more apparent. However, achieving and maintaining a balanced diet can be a complex task. To address this challenge, we introduce a novel recommendation system integrated into the "WeCare" platform. Leveraging machine learning techniques, including K-Means clustering and the Random Forest algorithm, this system generates personalized diet plans based on an individual’s unique physical attributes, health goals, and special dietary needs, such as those of athletes, bodybuilders, and pregnant women. By considering factors such as body mass index (BMI), calorie requirements, and health objectives, this system empowers individuals to make informed dietary decisions that align with their long-term well-being. This paper outlines the development and implementation of this comprehensive recommendation system, highlighting its potential to enhance lives through data-driven dietary guidance.