Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{197949,
        author = {Ali Kudia and Bhagyashri Pawar and Tanish Razdan and Shreya Dixit},
        title = {Food waste prediction using Machine Learning : An Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7047-7052},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197949},
        abstract = {Food wastage remains a significant challenge in the food service industry, particularly for small businesses that rely on manual estimation techniques for demand forecasting. Inaccurate predictions often lead to overproduction or underproduction, resulting in financial losses and reduced customer satisfaction. This study proposes a machine learning-based approach to predict daily food demand using historical sales data and relevant temporal features. Multiple models, including Linear Regression, Decision Tree, Random Forest, and XGBoost, are evaluated to identify the most effective method. The results indicate that ensemble learning models provide higher prediction accuracy compared to traditional techniques. The proposed framework offers a practical and efficient solution for small food businesses to optimize inventory, reduce waste, and improve decision-making.},
        keywords = {Demand Forecasting, Food Waste Reduction, Inventory Optimization, Machine Learning, Predictive Analytics, Random Forest, Small Businesses, XGBoost.},
        month = {April},
        }