Intelligent Food Ordering System with Behavioral Prediction and Dynamic Pricing: A Machine Learning Approach

Singh, Vaibhav Pratap

doi:https://doi.org/10.55041/ijsmt.v2i5.173

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INTELLIGENT FOOD ORDERING SYSTEM WITH BEHAVIORAL PREDICTION AND DYNAMIC PRICING: A MACHINE LEARNING APPROACH

AUTHORS:

Vaibhav Pratap Singh

Mentor

Minhaj Nezami

Affiliation

Department of Information Technology Noida Institute of Engineering and Technology Greater Noida, Uttar Pradesh, India

DOI: 10.55041/ijsmt.v2i5.173

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

The digital transformation of the food industry has led to a surge in online food delivery platforms. However, most existing systems operate on static models that do not account for individual user behavior or real-time market fluctuations. This paper proposes an Intelligent Food Ordering System (IFOS) that integrates behavioral prediction and dynamic pricing models to optimize user experience and restaurant profitability. By leveraging Long Short-Term Memory (LSTM) networks for predicting user ordering patterns and a Reinforcement Learning (RL) framework for dynamic pricing, the system adapts to demand shifts and individual preferences. Experimental results demonstrate that the proposed system increases customer retention by 15% and improves restaurant revenue by 12% compared to traditional static systems.

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

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