Algorithm Visualization for Student Performance

● The proposed website aims to provide a comprehensive visualization platform for five fundamental machine learning algorithms: K-Nearest Neighbours (KNN), Logistic Regression, Decision Tree, Support Vector Machine (SVM) and Random Forest. These algorithms will be demonstrated in the context of analysing a student performance dataset, offering an interactive and educational experience for users interested in understanding how these methods work in practice.
● In addition to the visualization component, the project will utilize the trained algorithms to predict future academic outcomes of students. It will also produce detailed insights into the factors affecting student performance.