This project aims to develop a machine learning–based system that predicts student academic performance using historical academic records, attendance, and assessment data. The system helps institutions identify at-risk students early and supports data-driven academic interventions.
Collect and preprocess student datasets including marks, attendance, and internal assessments Perform data cleaning, handling missing values, and feature selection Apply supervised learning algorithms such as Linear Regression, Decision Tree, and Random Forest Train and test models using appropriate evaluation metrics (accuracy, RMSE, precision) Compare multiple algorithms to select the most accurate prediction model Visualize performance trends using graphs and dashboards Develop a user interface for data input and prediction output Integrate the trained model with the frontend application Implement basic authentication for authorized academic staff Prepare technical documentation including system architecture and workflow Conduct testing and validate prediction results Present findings with performance analysis and future improvement scope
