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Machine learning framework predicts restaurant food waste

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine learning framework to estimate daily food waste in restaurants, using operational data, weather, and event indicators. The study constructed a dataset of 77,980 records and employed four regression models, with Random Forest achieving the best performance. Key predictive factors identified include menu diversity, operational area, and temporal activity patterns. The dataset, code, and experimental configurations are publicly available to encourage further research, particularly with empirically measured waste data. AI

IMPACT Provides a framework for restaurants to reduce waste and improve sustainability through data-driven insights.

RANK_REASON Academic paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning framework predicts restaurant food waste

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Academic paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Md Mehedi Hasan Naeem, Md Ashraful Islam, Moumita Barua, Ishtiyak Ahmmad Araf, Md. Arefin Haque Mahir ·

    A Machine Learning Framework for Predicting Restaurant Food Waste to Support Sustainable Food Management

    arXiv:2609.08078v1 Announce Type: new Abstract: Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an exploratory machine learning framework for estimating daily restaurant food waste quan…

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