Student Churn Prophecy Using Machine Learning

Abstract

Dropout is one of the critical issues faced by educational institutions in terms of both academic performance and institutional efficiency. The project, Student Churn Prophecy Using Machine Learning, aims at predicting the likelihood of dropout using machine learning algorithms. A broad range of student data, such as academic performance, attendance records, demographic details, and engagement levels, is processed and analyzed to identify key patterns and trends. This system generates predictive models that forecast at-risk students and allows timely intervention strategies. The approach enhances student retention and overall educational outcomes of institutions by adopting this methodology.