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.