Result: Agile methodology prediction using machine learning algorithms.

Scrum and other agile approaches are becoming indispensable for handling difficult software development problems and producing worthwhile results. To forecast the adoption of Scrum Agile, this study investigates the relationship between machine learning and agile approaches and suggests a predictive strategy. Through the integration of prescriptive and predictive analytics, this research seeks to comprehend the complex variables influencing the results of agile projects. This study uses a scientific methodology to improve a prediction model for Scrum Agile adoption, adhering to the data science lifecycle. Predictive models are developed, tested, and evaluated by utilising a variety of machine learning approaches via stages of issue description, data gathering, preparation, extraction, and investigation. To improve prediction performance, hybrid algorithms that combine XGBoost with Multilayer Perceptron, Decision Trees, Random Forests, K-Nearest Neighbours, and Support Vector Machines are presented. The objective of this research is to provide practitioners with tools to anticipate and overcome challenges in Scrum Agile adoption, fostering a datadriven approach to project success. [ABSTRACT FROM AUTHOR]