Machine Learning Techniques find a myriad of applications in different fields. One such application is the use of these techniques in education. The research in the educational field that involves machine learning techniques is rapidly increasing. Applying machine learning techniques in an educational background aims to discover hidden knowledge and patterns about student’s performance. This work aims to develop student’s academic performance prediction model, among the various students from various departments using machine learning classification methods; K-Nearest Neighbor, Decision Tree, Support Vector Machines, Random Forest, and Gradient Descent Boost Algorithms. Parameters like living area, mother father relation, education and their employment, backlogs, attendance, Internet connection availability and smart phone usage are used. Resultant prediction model can be used to identify student’s performance in the final examination and anticipate the final grade. Thereby, the college management or lecturers can classify students and take an early action to improve their performance. Due to early prediction, solutions can be sought for better results in the final exams.

Educational Data Mining, Machine Learning, Classification, Student Academic Performance

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