This Machine Learning project predicts student math performance based on educational, demographic, and academic factors such as reading score, writing score, parental education, lunch type, and test preparation. The project demonstrates an end-to-end ML pipeline with preprocessing, model training, and deployment using Flask.
Uses student academic records and educational factors to generate predictive insights.
Includes data ingestion, preprocessing, model training, and prediction workflow.
Instantly predicts the expected math score using trained ML models.
Analyze how different educational and social factors influence student academic performance.
Beautiful responsive frontend design compatible with desktop, tablet, and mobile devices.
Connected with Flask backend to collect data and display prediction results dynamically.
Covers complete machine learning lifecycle from data preprocessing to deployment.