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This study compares four machine learning models—LSTM, Facebook Prophet, XGBoost, and Gradient Boosting Regressor—for inventory demand forecasting in a Nigerian food manufacturing SME. LSTM consistently outperformed the other models in predictive accuracy, as indicated by lower RMSE and MAE values and higher R² scores, making it the most effective choice for SMEs with dynamic demand profiles. The findings emphasize the importance of adopting advanced ML techniques to enhance forecasting accuracy and support

Author

taominhanh1406

Language

EN