PREDICTIVE ANALYTICS FOR PROJECT RISK MANAGEMENT USING MACHINE LEARNING

Abstract

Software projects frequently encounter challenges, including schedule delays, cost overruns, quality defects, scope creep, resource shortages, and reduced team productivity. Accurate estimation of project duration and cost, based on project length and complexity, is essential for successful and timely completion. Previously, managers assessed risks and project duration based on their own experience. While greater experience often led to better risk management, this approach was often slow, inaccurate, and subject to bias. The solution is to combine predictive analysis and machine learning. Rather than relying on manual estimation, leveraging machine learning offers fast, accurate, and unbiased prediction. To achieve this, the model is trained on historical data collected from different organizations using machine learning. This data may include project size, actual cost, team details, resource data, requirements, technical complexity factors, quality and testing factors, project management metrics, risk-related variables, and outcome labels. Together, these factors form a powerful dataset for model training. The model generates a prediction based on given inputs. Although machine learning has been widely used in software projects to predict development aspects such as time, cost, and potential risks, few approaches have integrated these elements within a single framework. This study introduces an optimized Deep Neural Network model that unifies time, effort, and cost estimation with risk prediction and offers risk mitigation strategies to enhance project outcomes. The model initially predicts risks and subsequently recommends specific actions to prevent them. Rather than estimating cost and time in isolation, the model incorporates risk predictions to refine time, cost, and effort estimates, providing targeted recommendations for risk mitigation. The proposed model offers a practical, AI-driven approach for predicting risks and identifying effective mitigation strategies. It delivers reliable and accurate risk-aware forecasts, as well as preventive measures, to enhance project outcomes, strengthen project planning, and reduce failure rates.