ElevateEats: ML-Based Food Ordering System for Health-Conscious Diners

In this paper, we discuss a new approach toward the delivery of personalized food recommendations that cater to individual dietary needs and health goals. Most of the existing food delivery systems focus on convenience over the nutritional and health aspects that are important for users who want to lead a balanced lifestyle. This paper integrates a recommendation system called “ElevateEats” that considers user preferences, nutritional requirements, and dietary restrictions. The system uses various algorithms, amongst which is content-based filtering, in order to offer the user personalized meal suggestions; k-Nearest Neighbors (k-NN) is used to find meals or users with similar preferences; clustering algorithms group meals and users based on their common attributes, while the Convolutional Neural Networks (CNNs) analyze meal images to extract nutritional knowledge. A series of mathematical models refine and improve the suggestions of meals, ensuring that the platform really contributes to health-conscious eating without losing high user satisfaction. Experimental results show that ElevateEats achieved an overall accuracy of 85 % in delivering personalized and nutritionally appropriate meal recommendations.