Niloy Roy

Work place: Department of Computer Science, Faculty of Science and Technology, American International University-Bangladesh (AIUB), Dhaka 1229, Bangladesh



Research Interests: Machine Learning, Data Mining


Niloy Roy received B.Sc. in Computer Science and Engineering at the Faculty of Science and Technology from American International University-Bangladesh (AIUB), in 2022. His major was Software Engineering. Currently, he is a general secretary of the AIUB Computer Club. His research interest includes Data Mining, Data Warehouse, Machine Learning, and AI. Interesting in the working area is Software development.

Author Articles
Investigation of Student Dropout Problem by Using Data Mining Technique

By Sadi Mohammad Ibrahim Adnan Chowdhury Niloy Roy Md. Nazim Hasan Dip Nandi

DOI:, Pub. Date: 8 Oct. 2023

Throughout the past twenty years, we've seen a huge increase in the number of school universities. Given the intense competition among major universities and schools, this attracts students to apply for admission to these institutions. Early school dropout prediction is a critical problem for learners, and it is hard to tackle. And a wide number of factors can impact student retention. In order to attain the best accuracy, the conclusion of the program, the standard classification approach that was used to solve this problem frequently needs to be applied the majority of organizations and courses launched by universities operate on either an auto model, therefore they always prefer course enrollment over student caliber. As a result, many students stop taking the course after the first year. In order to manage student dropout rates, this research provides a data mining application. The predictive model may provide an effective predictive list of students who typically require the greatest help from the student dropout program given updated data on new students. The results indicate that the object classification algorithm Random Forest data mining technique can create a reliable prediction model using existing student academic data. Future research on student dropout rates will continue to be vital for informing policy decisions, identifying at-risk populations, evaluating interventions, enhancing support services, predicting trends, understanding long-term consequences, and promoting global learning and collaboration in education.

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