Canalyser: An Intelligent Project Risk Analysis and Visualization System Using Machine Learning

Project risk management plays a crucial role in ensuring the successful execution of projects across domains such as software development, construction, and enterprise systems. Traditional risk assessment techniques rely heavily on manual evaluation, expert judgment, and qualitative models, which are often time-consuming, inconsistent, and prone to human bias. These limitations reduce the effectiveness of decision-making, particularly in large-scale and data-intensive environments. This paper presents Canalyser, an intelligent web-based system designed to automate project risk analysis using machine learning and interactive visualization techniques. The system accepts multiple project parameters, including budget, duration, team size, complexity, and resource availability, and predicts the associated risk level categorized as low, medium, or high. Furthermore, it provides real-time graphical insights through dashboards, enabling users to analyze trends and make informed decisions. Experimental observations indicate that the proposed system improves efficiency, reduces manual effort, and enhances predictive accuracy compared to traditional approaches.