Abstract
Addressing the persistent issue of cost overruns in construction projects, our study explores the potential of machine learning algorithms for accurately predicting these overruns, utilizing an expansive set of project parameters. We draw a comparison between these innovative techniques and traditional cost estimation methods, unveiling the superior predictive accuracy of machine learning approaches. This research contributes to existing literature by presenting a data-driven, reliable strategy for anticipating and managing construction costs. Our findings have significant implications for project management, offering a path towards more efficient and financially sound practices in the construction industry. The improved prediction capabilities could revolutionize cost management, facilitating better planning, risk mitigation, and stakeholder satisfaction.
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