Mining Educational Data to Reduce Dropout Rates of Engineering Students

In the last two decades, number of Engineering Institutes and Universities grows rapidly in India. This causes a tight competition among these institutions and Universities while attracting the student to get admission in these Institutions/Universities. Most of the institutions and courses opened in Universities are in self finance mode, so all time they focused to fill all the seats of the courses not on the quality of students. Therefore a large number of students drop the course after first year. This paper presents a data mining application to generate predictive models for student's dropout management of Engineering. Given new records of incoming students, the predictive model can produce 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.