FORECASTING THE EFFICIENCY OF SOLID WASTE RECYCLING IN INDUSTRIAL ENTERPRISES
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https://doi.org/10.5281/zenodo.19386438##article.subject##:
XGBoost algorithm, industrial efficiency, machine learning, recycling processes, solid waste, forecasting models, digital economy, resource optimization##article.abstract##
This article provides a comprehensive analysis of modern machine learning methods utilized in industrial
sectors, specifically focusing on the application of the XGBoost algorithm to enhance production productivity. The study
examines the integration of these technologies within solid waste recycling processes at industrial enterprises, while
highlighting the exceptional accuracy levels achieved by the model. Furthermore, the research demonstrates significant
resource optimization capabilities for sustainable manufacturing and effective management.
A particular focus is placed on maintaining performance amidst the noise and insignificant data inherent in complex
industrial datasets. Additionally, the article addresses how the algorithm effectively handles incomplete indicators to
ensure reliable and consistent output. The findings illustrate the practical significance of implementing forecasting models
within the framework of the digital economy. Finally, the conclusions define the key role of machine learning technologies
in improving overall industrial efficiency.
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