An Intelligent Software Project Risk Prediction Engine Using Machine Learning and Web-Based Decision Support Systems

The significant contribution of this research is bridging the gap between theoretical machine learning models and practical industrial application in software project management. It develops an end-to-end Intelligent Software Project Risk Prediction Engine using a Random Forest classifier and seamlessly integrates it into a real-time, Django-based Web Decision Support System (DSS). This integration provides project managers with an accessible, user-friendly platform to dynamically assess early-stage project risks based on 51 historical project features, overcoming the limitations of traditional manual assessments and standalone “black box” ML scripts.