Abstract:
Education can determines the standard of society, it also make the nation empowered
providing new thoughts and implementation. In the last decades, it is found that number
of higher level educational institutes grows rapidly in Bangladesh. Besides ensuring
quality this increasing number causes tight competition of attracting students to get
admitted in the institutes. This institute have higher rating tendency to fill all the
available seats emphasizing on counting the number of students not on their academic
excellence. Therefore, a remarkable number of student drop the course due to inability of
adjustment with the academics which causing an ultimate loss to the family, society and
educational institute. None knows the proper reason of their leave and what percent or
who of student is going to become critical student. This paper investigated the prediction
of dropout student through data mining approaches. The study predicts critical students
applying different classification algorithm who tend to need support and essential
guidelines from the different perspective. The outcomes are compared with each and also
the models with the best.
