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@article{184421,
        author = {Adebanjo, Adedoyin Samuel and Anyanwu, Chiamaka Grace and Adeoti, Babajide Ebenezer and Mgbeahuruike, Emmanuel},
        title = {A Logistics Regression-Based Student Performance Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3166-3171},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184421},
        abstract = {Student performance prediction has become an important focus in educational data mining. Institutions are looking for ways to use data to identify at-risk learners and improve academic outcomes. Logistic Regression, a widely used statistical and machine learning model, offers a clear way to model categorical outcomes, such as pass/fail or high/low performance. This study uses Logistic Regression on student performance data to examines how demographic, behavioral, and academic features affect learning outcomes. By using its probabilistic framework, Logistic Regression predicts student success with high accuracy. It also reveals the importance of various predictors, helping educators design targeted interventions. The findings show that Logistic Regression is an effective predictive model as it balances accuracy and clarity, contributing to early warning systems and better decision-making in higher education.},
        keywords = {Logistics Regression, Machine Learning, Student Performance Prediction, Supervised Learning.},
        month = {September},
        }