  The main objective of higher education is to provide quality education to
students. One way to achieve highest level of quality in higher education
system is by discovering knowledge for prediction regarding enrolment of
students in a course. This paper presents a data mining project to generate
predictive models for student retention management. Given new records of
incoming students, these predictive models can produce short accurate
prediction lists identifying students who tend to need the support from the
student retention program most. This paper examines the quality of the
predictive models generated by the machine learning algorithms. The results
show that some of the machines learning algorithms are able to establish
effective predictive models from the existing student retention data.
