  In the last two decades, number of Higher Education Institutions (HEI) grows
rapidly in India. Since most of the institutions are opened in private mode
therefore, a cut throat competition rises among these institutions while
attracting the student to got admission. This is the reason for institutions to
focus on the strength of students not on the quality of education. This paper
presents a data mining application to generate predictive models for
engineering student's dropout management. Given new records of incoming
students, the predictive model can produce short accurate prediction list
identifying students who tend to need the support from the student dropout
program most. The results show that the machine learning algorithm is able to
establish effective predictive model from the existing student dropout data.
