Design of an Intelligent Diet Recommendation Engine Using Content-Based Filtering and PCA Enhanced Feature Modeling

In today's health-conscious era, people from everywhere in the world are seeking healthy lifestyles. While exercise and fast food avoidance are usually promoted, they are alone inadequate for stable health. For stable health need to have a well-balanced, individualized diet. A personalized nutrition plan helps to achieve a preferred weight, with the prevention of chronic conditions, and with improving overall well-being through the consideration of factors like age, weight, height, and lifestyle. Despite this growing awareness, there remains a wide gap between cutting-edge systems that offer personalized nutrition recommendations based on the individual's unique dietary needs and preferences. Our paper outlines an AIpowered individualized diet advising system that employs content-based filtering and a Random Forest Classifier for personalized meal plan suggesting. Unlike traditional systems that suggest commonalities, our framework accounts for userspecific parameters such as BMI, age, gender, meal frequency, dieting targets, and favorite foods. The system judiciously considers user inputs and proposes breakfast, lunch, and dinner with complete nutrition breakdowns and cooking recipes. This strategy provides personalized and specific suggestions according to individual dietary requirements and preferences without depending on data from other users.