AI-Driven Risk Assessment and Mitigation Strategies for Optimizing Project Success

In this paper, we present a predictive analytics framework for risk assessment and mitigation in software development projects, aimed at enhancing project quality and success rates. Software projects often face challenges due to their dynamic and complex nature, making early risk detection crucial. To address this, we collected data from developers, project managers, and QA engineers using targeted sampling techniques. The dataset underwent preprocessing, including extensive cleaning and transformation, before being analyzed using supervised machine learning models: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Logistic Regression. These models were evaluated for their predictive accuracy and integrated into a real-time Stream lit application for live risk assessment and decision support. The results demonstrate the effectiveness of predictive analytics in identifying correlations between project parameters and potential quality risks, enabling proactive measures to address them. Among the models, Support Vector Machines exhibited superior performance, effectively handling high-dimensional project datasets. The developed Stream lit application allows project teams to visualize risk predictions, supporting informed and immediate decision-making. This research contributes to advancing software project management by introducing a proactive, data-driven approach to quality assurance and risk mitigation. Future research can explore integrating additional machine learning models and expanding datasets to further validate and optimize predictive performance.