The student retention rate is a highly focused and actively tracked metric for all institutions to evaluate progress toward growth goals and objectives. Innovations to use SAS® Visual Data Mining and Machine Learning to facilitate higher education retention success are in high demand. However, institutions must move beyond descriptive and prescriptive analytics and use more advanced AI and machine learning models to predict the retention result early enough to implement measures to alter student outcomes. The audience got an overview of the work done in this area. We shared the success stories, the lessons learned in building a predictive model for higher education and the important things to consider in the context of higher education. The audience gained practical end-to-end techniques and skills to get started, develop and implement a model that uses SAS Visual Data Mining and Machine Learning. We shared challenges faced in each step of the project and the modeling and technical resolutions we implemented to overcome the challenges. The presentation included hands-on demonstrations of how to prepare the data, choose the variables and build complex analytics pipelines with a range of models to demonstrate the power of machine learning. Learn how to tune and optimize the model and produce model interpretability plots to help stakeholders gain actionable insights.
Presentation slides are attached to this post.
