Diet Recommendation System Using Machine Learning
Raman Rawal1, Anusha Sahi Mithapani2, Sana Qureshi3, Prof. A.D. Wankhade4
1Department of Information Technology, Government college of Engineering Amravati 444604.
2Department of Information Technology, Government College of Engineering Amravati 444604.
3Department of Information Technology, Government College of Engineering Amravati 444604.
4Department of Information Technology, Government College of Engineering Amravati 444604.
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ABSTRACT - This paper presents a personalized diet recommendation system that leverages machine learning algorithms to provide customized diet plans based on personal health data, dietary habits, and food preferences. The aim of this system is to provide users with the necessary tools to make informed dietary decisions, improve overall well-being, and reduce the risk of lifestyle-related diseases, such as obesity, diabetes, and cardiovascular disease. Personal health data, dietary habits, and food preferences were collected from a large user population, which underwent preprocessing to extract relevant features. Machine learning models were developed to generate personalized diet recommendations based on the user's information. The nearest-neighbour algorithm provided real-time feedback and guidance on eating habits, and the system also provided access to various recipes and meal ideas. The effectiveness of the system was assessed by measuring changes in Body Mass Index (BMI) following adherence to the personalized diet plan, which produced a significant improvement in users' dietary habits and a substantial reduction in BMI. This research demonstrates the potential of machine learning in creating personalized healthcare applications that offer more accurate and effective healthcare services, and the proposed system could have a significant impact on public health by promoting healthy dietary habits and lowering the risk of lifestyle-related diseases.
Key Words: Personalized diet recommendation, Machine Learning, Nearest-Neighbor Algorithm, Body Mass Index, User preferences, Public Health
