References
- Anaconda.com (2022). Anaconda. [Accessed 6 November 2022]. Available from: https://www.anaconda.com/.
- Assiri, B., Bashraheel, M. and Alsuri, A. (2022). Improve the Accuracy of Students Admission at Universities Using Machine Learning Techniques In: 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA). IEEE, pp.127–132.
- Brownlee, J. (2020). Why Do I Get Different Results Each Time in Machine Learning? https://machinelearningmastery.com/. [Online]. [Accessed 1 December 2022]. Available from: https://machinelearningmastery.com/different-results-each-time-in-machine-learning/.
- Colegiul Național Militar ‘Dimitrie Cantemir’ Breaza (2022). Tabel nominal cu rezultatele candidaților arondați să susțină admiterea în Colegiul Național Militar ‘Dimitrie Cantemir’ în ordinea descrescătoare a mediilor de admitere, cu unitatea școlară la care au fost adimiși - Rezultate finale.
- Direcția Generală Învățământ Universitar | Ministerul Educației (2021). Lista Instituțiilor de Învățământ Superior de Stat Militare. edu.ro. [Online]. [Accessed 30 October 2022]. Available from: https://www.edu.ro/sites/default/files/fisiere%20articole/Lista%20IISSM%202021.pdf.
- Embarak, O. (2020). Apply Machine Learning Algorithms to Predict At-Risk Students to Admission Period, In: 2020 Seventh International Conference on Information Technology Trends (ITT). IEEE, pp.190–195. https://www.w3schools.com/2022. Machine Learning - Confusion Matrix. https://www.w3schools.com/.
- Ministerul Apărării Naționale Colegiul Național Militar ‘Dimitrie Cantemir’ (2022). Metodologie de admitere, pp.15–20. [Accessed 23 October 2022]. Available from: https://www.cantemircml.ro/.
- Pasic, D., Kucak, D. (2020). Machine learning model for detecting high school students as candidates for drop-out from a study program, In: 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). IEEE, pp.1140–1144.
- Ujkani, B., Minkovska, D. and Stoyanova, L. (2021). A Machine Learning Approach for Predicting Student Enrollment in the University, In: 2021 XXX International Scientific Conference Electronics (ET). IEEE, pp.1–4.