Leveraging Machine Learning for Food Waste Reduction: An Analysis of Predictive Models

Food waste remains a significant global issue with severe environmental, economic, and social impacts. Recent advancements in machine learning (ML) have shown promise in tackling this problem across various sectors, including household, catering, and hospitality industries. This study explores the application of machine learning models to predict and minimize food waste, focusing on key predictors such as household consumption patterns, retail demand, and food service estimates. By analyzing the effectiveness of random forests, this research highlights the ability of these models to enhance forecasting accuracy and waste management efficiency. Findings underscore the potential of ML to support sustainable food consumption, reduce waste generation, and achieve environmental benefits. This research offers insights into the practical deployment of ML for food waste mitigation, providing a roadmap for stakeholders aiming to adopt technology-driven waste reduction strategies.