Αλέξανδρος Ζερβόπουλος (ΠΜΕ201905)
This project focuses on the prediction of students' academic performance using publically available datasets acquired from the UCI repository and Kaggle, which were originally presented in [1] and [2], respectively.
Prediction is done using a standard Python-based toolkit, rather than WEKA, which is used in the original paper.
Utilized packages:
- pandas
- scikit-learn
- seabornplot
- jupyter notebooks
[1] Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students’ academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447-459.
[2] Amrieh, E. A., Hamtini, T., & Aljarah, I. (2016). Mining educational data to predict student’s academic performance using ensemble methods. International Journal of Database Theory and Application, 9(8), 119-136.