AI Driven Personalized Diet and Nutrition Recommendation System Based on Health Metrics and Food Knowledge Graphs

Abstract: Maintaining a healthy and balanced diet is not an easy task because everyone has different health needs, daily routines, budgets, and eating habits.Most current diet recommendation systems use fixed rules or common food guidelines.These systems often do not consider important personal health details like allergies, daily activity, or calorie requirements.They also ignore when meals should be eaten and whether the food is low-cost, which makes these systems less practical. Because of this, many people find it hard to follow the diet plans they suggest. To solve these problems, this research presents an AI-based system that suggests personalized diet and nutrition plans for each user. The system analyzes user information such as BMI, daily activity, health conditions, allergies, and calorie requirements to create customized meal plans.It uses intelligent techniques to suggest the right type of diet for each person and a food knowledge graph to understand how foods are linked to nutrients and dietary rules. The system also recommends meals based on the time of day and ranks them to ensure proper nutrition and calorie balance.The system also suggests low-cost meals and replaces expensive ingredients with cheaper alternatives. Overall, it gives simple, practical, and personalized diet suggestions that help people eat healthier.

Keywords: Python, ML Classifiers, Nutrition Dataset, Recommendation Engine