Student Performance Prediction and Analytics Dashboard Using Machine Learning

Student academic performance is a crucial indicator of educational outcomes and institutional effectiveness.However, traditional evaluation methods often fail to provide early insights into learning challenges faced by students.This project proposes a machine learning-based prediction system that analyzes academic and behavioral datasets to predict student performance and helps educators take data-driven decisions.The Random Forest algorithm is implemented to build a robust prediction model due to its high accuracy, ability to handle noisy data, and effective feature importance analysis.