Performance Analysis of Voting Regression-Based Ensemble Learning Methods for Food Demand Forecasting

Abstract

Accurate demand forecasting has become very significant, especially in the food sector, since many products have a limited lifespan, and improper management can cause the organization to incur enormous waste and loss. This research focuses on the problem of analyzing accurate food demand and its prediction through the application of machine learning techniques. An ensemble technique such as voting regression is employed, leveraging Random Forest Regressor and Gradient Boosting Regressor, which were the top-performing models. By integrating these two techniques using voting regression, we can leverage their complementary strengths to enhance prediction accuracy. The ensemble aggregates the predictions of both models, typically by averaging, to produce a final prediction. This technique can assist in reducing overfitting and capturing complex relationships in the data, resulting in more robust and accurate forecasts of food demand. The outcomes of the R2-score, Root Mean Square Error (RMSE) and Mean Average Error (MAE) are 0.99, 0.01, and 0.00, respectively.