Welcome to the research project report on Student performance Indicator. It is very important for any means of improving educational outcomes. The present study explores the potential of a wide range of machine learning algorithms: Linear Regression, Lasso, Ridge, K-Nearest Neighbors Regressor, Decision Tree, Random Forest Regressor, XGBoost Regressor, CatBoosting Regressor, and AdaBoost Regressor in determining student performance indicators. Through comparison of how these models perform in the prediction of student performance, we identify the best way to identify the superior approach for institutions in educational practice that are to use machine learning for data-driven decision-making and personalized learning strategies
You can read the attached pdf file to learn more about the models used and EDA performed.