A model for the identification of students at risk of dropout at a university of technology

Development has been seen in the advanced education segment in South Africa. With this development, an expansion in the dropout rate is noticed. This study explores the adequacy of dimensional decrease and concentrates the significant data covered up in the student information for the identification of students in danger of dropout. This study depends on educational data mining techniques and makes forecasts of dropout goal of understudies from courses. In the test, the researchers show promising outcomes with information from the recognition courses of a University of Technology.