A Review of Machine Learning Models for Predicting Agile Methodology

Agile methodologies., particularly the Scrum framework, have emerged as integral tools for addressing complex challenges and delivering high value software products. This research delves into the intersection of agile methodologies and machine learning, proposing a predictive approach to forecast Scrum Agile adoption. As agile methodologies gain prominence across diverse sectors, the integration of predictive and prescriptive analytics emerges as a potent strategy for unraveling intricate interdependencies impacting agile project outcomes. This study follows the data science lifecycle, employing the scientific method to iteratively evaluate and enhance the predictive model for Scrum Agile adoption. Beginning with issue definition and data collection, the research progresses through stages of data preparation, exploration, and feature extraction. Leveraging machine learning techniques, predictive models are developed, tested, and assessed to provide insights into the likelihood of successful Scrum Agile adoption.