alicevillar / SML-Comparative-Study

Supervised Machine Learning Algorithms for Predicting Student Dropout and Academic Success: A Comparative Study

Welcome to the official repository for the paper titled "Supervised Machine Learning Algorithms for Predicting Student Dropout and Academic Success: A Comparative Study." This repository contains the necessary code and data to ensure that the research project is transparent and reproducible.

▶️ Authors : Alice Villar and Carolina de Andrade

Project Files

• SML-Code.ipynb Machine Learning code - This file contains the project's code.
• Dimensions CSV File Dimensions file- We downloaded this CSV file from Dimensions on August 21, 2023, for use in our Systematic Literature Review (SLR).
• Values_for_STAC_test.csv Values for STAC - This file contains the values employed in our statistical tests on the STAC platform.
• statistical_test_t_test_paired.png t-test paired - This file displays the results of the paired t-test.

Useful Links

• Dimensions Academic Database
• STAC Platform
• Bibliometrix
• Biblioshiny