Leveraging machine learning for food waste reduction: an analysis of predictive models

HIGHLIGHTS

• What: 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. This study shows the effectiveness of using machine_learning, specifically the Random Forest model, for classifying and predicting food waste across various sectors. The analysis reveals that household-level food waste estimates, particularly on a per capita basis, are the most influential factors driving food waste predictions. This study provides targeted suggestions to address food waste based on insights from Random Forest model analysis, offering strategies for . . .
  

   

  

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