Optimizing Project Budgeting with Machine Learning Predictive Analytics for Cost Control – Exploring how ML models can improve cost estimation and minimize financial risks

Abstract

Project budget is one of the keys to project financial success and actual cost overruns can have a serious impact on projects. Conventional budgeting approaches which rely on historical and expert-based judgment criteria are also problematic in terms of being inaccurate and subjective, and therefore pose financial risks. In this work, we investigate the application of Machine Learning (ML) predictive analytics to the project budgeting process in order to improve cost estimation accuracy and to reduce financial risk. The research shows how these devices can scrutinize large data sets with models like regression analysis, decision trees, and neural networks to uncover hidden patterns and create more accurate cost predictions. In addition to this, the paper discusses different ML methods which can be employed while developing predictive model such as feature engineering, model selection and validation to generating the actionable insights which will help project managers in taking decisions factually. The study offers an in-depth case study of a construction project to demonstrate that ML models have the potential to identify potential cost overruns during early stages of construction projects and help in proactive risk management. The results of the study highlight the possibility of widespread adoption by financial institutions, lottery organisations and other enterprises not only seeking to manage their budgets more efficiently but to secure better overall financial oversight, to ensure that projects are delivered within budget. Overall, this research demonstrates the real potential power of machine learning in today's project management, and proposes an effective tool to reduce cost overrun and improve project efficiency.