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This thesis investigates the applicability of using machine learning for demand forecasting at HMS Networks. Using the design science research methodology, multiple forecasting artifacts were developed and tested iteratively, using historical sales data. Four tree-based models; Decision Tree, Random Forest, LightGBM and XGBoost, were tested with different pre-processing and feature engineering tec
