In the modern educational landscape, data-
driven decision-making has gained prominence as a
means to enhance student performance and institutional
effectiveness. This research focuses on the development
and implementation of machine learning models to
predict students' academic performance, using Institut
Catholique de Kabgayi (ICK) as a case study. The study
explores the potential of machine learning algorithms to
analyze various academic and non-academic factors that
may influence students' outcomes. The research employs
a comprehensive dataset comprising student
demographics, past academic records, attendance
records, socio-economic background, and other relevant
variables. Several machine learning models, including
Linear Regression Random, Forest Regressor Lasso,
Regressor Gradient, Decision Tree Regressor, Ridge
Regressor, classification models, and ensemble methods,
are utilized to build predictive models. The models are
trained on historical data and fine-tuned to maximize
prediction accuracy. The findings of this study are
expected to provide valuable insights into the factors that
most significantly impact students' performance at ICK.
Additionally, the developed machine learning models can
assist academic advisors and administrators in early
identification of students at risk of underperforming,
allowing for timely intervention and support.
Furthermore, this research contributes to the broader
discourse on leveraging artificial intelligence and machine
learning in education, paving the way for more effective
and personalized student support systems.
Keywords : Machine Learning Models, Student Performance Prediction, Academic Predictive Models, Data-driven Decision Making.