This paper is conducted to develop a system that can aid universities in tracking students' academic progress by accurately determining dropout rates using machine learning techniques. This has been done by collecting and organizing data into machine readable forms, using predictive methods that determines significant patterns and trends, constructing different models involving machine learning techniques: Random Forest, Logistic Regression, and Neural Network, for forecasting and predictions, and finally interpreting the statistical results of the model. After the evaluation, it has been concluded that the Random Forest achieved the highest accuracy of 95.00%, precision of 97.78%, and F1 score of 96.70%. Despite these results, the researchers recommend developing a more interesting and engaging prototype GUI and utilizing a more balanced and diverse dataset that will enable higher accuracy and deeper understanding of the results.
Towards Predicting Student’s Dropout in Higher Education Using Supervised Machine Learning Techniques
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