A comprehensive educational technology project that leverages machine learning to predict student academic outcomes across multiple dimensions. The system uses three specialized models to help educational institutions identify at-risk students, forecast exam performance, and implement targeted interventions to improve student success rates.
- Early Intervention: Identify struggling students before academic failure using predictive analytics
- Performance Forecasting: Predict final exam scores based on attendance and coursework data
- Dropout Prevention: Forecast student retention/dropout risk with 97% accuracy
- Data-Driven Education: Provide educational institutions with actionable insights
- Resource Optimization: Enable targeted allocation of support resources
- Privacy-Conscious Design: Implement responsible data handling with security measures
Built with Flask, scikit-learn, and modern web technologies, this project demonstrates the practical application of machine learning in educational settings to improve student outcomes.
