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This paper presents a stacking ensemble model for food demand forecasting aimed at reducing food waste in the retail sector. The model combines multiple machine learning techniques and demonstrates superior accuracy compared to traditional models, achieving a coefficient of determination score of 0.99 and a prediction accuracy of 98.2%. The findings suggest that accurate demand predictions can significantly enhance inventory management and minimize food waste.

Author

mrrandom112001

Language

EN