Agile Project Status Prediction Using Interpretable Machine Learning
Ali Akbar ForouzeshNejad, Farzad Arabikhan, Nigel Williams, ALEXANDER E. GEGOV, Omer Faruk Sari, Mohamed Bader · 2024
Monitoring and forecasting the progress of information technology projects stands as a significant challenge in project management. Over the past two decades, agile project management has become a crucial factor influencing project success. Despite this, existing research has not presented a comprehensive model capable of predicting project outcomes based on agile features. In light of this, this study aims to develop a predictive model for information technology project outcomes using agility metrics. The results indicate that metrics related to teamwork and the team's capabilities, along with their collective experience, have the most significant impact on project success. The study employs the Decision Tree as an interpretable model to establish rules and predict project success. The accuracy of the model designed in this study is an impressive 97%, surpassing the accuracy of SVM at 71 % and KNN at 82%.