Comparison of regression methods and tools using the example of predicting the success of graduate master’s students in different fields of education

9th International Scientific Conference Technics and Informatics in Education – TIE 2022 (2022) стр. 237-242

АУТОР(И): Katarina Karić, Andrijana Gaborović, Marija Blagojević, Danijela Milošević, Katarina Mitrović, Jelena Plašić

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DOI: 10.46793/TIE22.237K


With the rapid development of ICT, the fields of Artificial Intelligence and Machine Learning and data mining techniques, there is a need for research in which they are applied, in various domains. In this paper, the analysis of the data set was conducted using regression methods, as one of the „Data mining“ and prediction techniques, in order to predict further development in the future, ie. number of graduate master’s students in all fields of education. The aim of this research is to monitor the current number of students and compare them with the previous one – in academic education of the second degree, in order to predict the number of students annually and possible factors affecting academic university education in the Republic of Serbia. The obtained results related to the number of master’s degree students in the field of education in all territorial parts of the Republic of Serbia, may, also indicate the implementation of certain reforms in academic education in the future, adding innovative ideas, student exchange and others.


regression; data mining; master studies; education


  • [1] Branković, S. (2017). Artificial intelligence and society. Serbian political thought. 56. 13-32. 10.22182/spm.5622017.1.
  • [2] Bell, J. (2014.). Machine learning: Hands-on for developers and technical professionals. John Wiley & Sons
  • [3] Blagojević, М. (2010). Application of web mining in education. 3rd International Conference on Technology and Informatics in Education, Čačak, Serbia, retrieved from:
  • [4] Halili, F., & Rustemi, A. (2016). Predictive modeling: data mining regression technique applied in a prototype. International Journal of Computer Science and Mobile Computing, 5(8), 207-215, retrieved from:
  • [5] Janković, S., Kukić, K., Uzelac, A., & Maraš, V. (2019). Traffic prediction in the local computer network using supervised machine learning. XXXVII Symposium on New Technologies in Postal Telecommunication Traffic – PosTel 2019, 3 – 4 December 2019., Serbia: Belgrade, retrieved from:,%20mreze%20i%20servisi/
  • [6] Acharya, M. S., Armaan, A., & Antony, A. S. (2019). A comparison of regression models for prediction of graduate admissions. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 21 – 23 Feb. 2019., India: Chennai, (pp. 1-5),
  • [7] Huang, S., & Fang, N. (2010, June). Regression models for predicting student academic performance in an engineering dynamics course. In 2010 Annual Conference & Exposition (pp. 15-1026), retrieved from:
  • [8] Janeska, M., & Sotiroski, K. (2005). Data mining-The road to competitiveness, retrieved from:
  • [9] Srivastava, J., Desikan, P., & Kumar, V. (2002, November). Web mining: Accomplishments and future directions. In National Science Foundation Workshop on Next Generation Data Mining (NGDM’02) (pp. 1-148), retrieved from:
  • [10] Javatpoint: Data Mining Techniques, retrieved from:
  • [11] Datascience foundation, retrieved from:
  • [12] A Tutorial on Multiple Linear Regression, retrieved from:
  • [13] Pallant, J. (2009). SPSS: survival manual: a step-by-step guide to data analysis using SPSS. Belgrade: Mikro knjiga.
  • [14] NCSS Software, retrieved from:
  • [15] Betterevaluation, Rapidminer, retrieved from:
  • [16] Open Data Portal, Republic of Serbia, retrieved from: