ORDERISTA-AI BASED FOOD ORDERING AND CANTEEN SYSTEM

Abstract

This paper presents Orderista, an AI-based food ordering and canteen management system designed to enhance user experience, optimize operational efficiency, and provide intelligent decision-making support. The proposed system integrates machine learning algorithms, recommendation engines, and real-time analytics to streamline the food ordering process. Orderista processes user preferences, order history, and contextual data to generate personalized food recommendations. Additionally, the system incorporates sales forecasting models to predict demand trends, enabling better inventory management and reduced food wastage. The architecture consists of modules such as user interface, recommendation engine, order processing system, chatbot support, and analytics dashboard. The system is implemented using a Django-based backend and a responsive web interface for seamless interaction across devices. Experimental results demonstrate improved order accuracy, reduced waiting time, and enhanced user satisfaction. The proposed solution is highly scalable and can be deployed in canteens, restaurants, and institutional food services.