International Journal of Scientific Research and Engineering Development

Large-scale electrical engineering project often subjects them to a high risk in terms of cost overruns, time overruns and quality deviations. The conventional approaches of risk assessment, though handy, lack the ability to deal with the dynamic and data intensive environment that such projects present. In the given paper, the authors delve into how Machine Learning (ML) models could be integrated into the risk assessment systems to enhance the ability to predict, classify, and mitigate risks associated with the project. Through the analysis of the project data, including timelines, budgets, environmental conditions, and stakeholder performance, the research compares several ML models, including the Random Forests, Support Vector Machines, and Neural Networks, regarding their predictive accuracy. The results indicate that ML-based methods are superior to traditional models as they provide early warning, adaptive learning, and data-driven insights, thus being invaluable in risk-convinced project planning and decision-making in the electrical engineering field in the future.