Artificial intelligence based customer churn prediction model for post- graduation students in Saudi universities

Hussein Moselhy Syaed Ahmed; Safaa Sayed Mahmoud; Nabil Mohamed Alabsy

Author(s)

Hussein Moselhy Syaed Ahmed Department of Business Administration, College of Business, University of Bisha,Bisha 61922,Saudi Arabia

Safaa Sayed Mahmoud Department of Business Administration, College of Business, University of Bisha,Bisha 61922,Saudi Arabia

Nabil Mohamed Alabsy Department of Business Administration, College of Business, University of Bisha,Bisha 61922,Saudi Arabia

Abstract

Despite improvements in undergraduate student retention, the problem of student attrition continues to exist and remains a challenge for the academic institutes, but especially for the post graduate programs where retention is essential for academic success and growth of the institute. It is an Artificial Intelligence (AI) based Churn Prediction model to identify the risk of disengagement or dropout from the postgraduate students of Saudi Universities. The dataset used in the research consists of academic, demographic, behavioral, and financial factors. Finally, we apply some machine learning algorithms like Random Forest and Gradient Boosting to predict churn with very high accuracy. We evaluate the performance of the model using metrics such as precision, recall, and ROC-AUC to validate that the model is able to identify at-risk students. Burke finds that academic performance, attendance pattern, and financial stability have a strong positive effect on churn likelihood. This first model has important implications for proactive interventions by universities administrators, such as targeted support strategies. The study contributes to foster academic excellence and education institutions sustainability by improving retention rates.

Publication Details

Page(s) 368-378

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

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Keywords

academic performance, Artificial Intelligence, machine learning, Educational Data Mining, Churn Prediction, predictive analysis, student retention

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