Improving project cost forecasts: an analysis of combining unsupervised and supervised machine learning algorithms with real project data

Purpose This study aims to develop a new hybrid algorithm that combines supervised and unsupervised ML techniques to improve the precision of predictions for project progress and cost performance measures. Design/methodology/approach This study utilizes a hybrid project cost forecasting methodology by combining supervised and unsupervised machine learning algorithms. Findings Computational results of this study demonstrate the superiority of the XGBoost and Random Forest algorithms, both ensemble methods, and confirm the accuracy of this forecasting methodology. Originality/value Unlike earlier studies that rely on artificially generated data, this study uses a dataset of 117 real-world projects to test the new forecasting technique. The clustering approach to the cost dataset, a novel contribution of the current study, demonstrates enhanced prediction accuracy. Cluster-aware estimate-at-completion forecasting is studied as a process-level decision-support tool.