ML platform · 2024
Dropout Prediction & Counseling
Early-warning system for student dropout risk
- Prediction accuracy
- 87%
- Students monitored
- 200+
- Mentor alerting
- Automated
The problem
Institutions only discovered at-risk students after attendance and grades had already collapsed — far too late for counselling to help.
What we built
An ensemble model scores risk continuously from attendance, assessment and engagement signals, and Celery workers push alerts to mentors the moment a student crosses a threshold.
Key capabilities
Ensemble scoring
Logistic Regression + Decision Tree ensemble at 87% accuracy.
Continuous monitoring
Risk recalculated as new attendance and marks land.
Automated alerts
Celery workers notify mentors and counsellors on threshold breach.
Counselling tracker
Intervention notes tied to each student's risk timeline.