Welcome to the Student Mark Prediction System! This project predicts student performance based on factors such as gender, ethnicity, parental education level, lunch habits, and test preparation completion. Employing modular programming, we emulate industry standards for scalability and robustness.
This machine learning endeavor integrates seamlessly with a Flask-based web application. Nine regression models are deployed to forecast student marks, utilizing study hours and previous grades as features. Model performance metrics include RMSE, MAE, and R2 Score.
Moreover, this project offers hands-on experience with GitHub version control and deployment on Amazon Web Services (AWS) EC2, empowering educators with insights to implement targeted interventions.
All dependencies are listed in the requirements.txt file.
