Predictive Modeling for Academic Success: Optimizing Student Outcomes based on Strengths and Weaknesses

This research article explores the use of predictive modeling to enhance academic success and develop personalized academic plans for students in Algeria, particularly focusing on the baccalaureate exam. The study employs a random forest classifier to predict student outcomes based on various academic and demographic factors. The model achieves a high accuracy of 98 percent in predicting student success or failure. The research identifies key subjects that significantly influence student performance, with physics, science, and mathematics emerging as the most important predictors. The study also develops a personalized academic recommendation system that uses the model’s insights to provide tailored advice to students based on their strengths and weaknesses. The article discusses the implications of these findings for educational policy, curriculum design, and the potential for data-driven decision-making in education. It also acknowledges limitations, such as the geographic specificity of the dataset and the exclusion of certain non-academic factors and suggests directions for future research, including expanding the dataset and incorporating additional features.