StudentDrop ML Model

A vocational school was struggling with a high dropout rate but had no way to identify at-risk students before it was too late. Counselors were reactive — only finding out about problems when students had already stopped attending.

We developed a machine learning classification model trained on 3 years of historical student data. The model scores each student's dropout risk weekly and flags high-risk students for counselor follow-up — before they actually drop out.

• Data preprocessing from attendance, grades & socioeconomic records
• Feature engineering (trend analysis, rolling averages)
• Classification model with 87% accuracy
• Weekly risk scoring dashboard for counselors
• Automated alert system for high-risk students
• Model performance monitoring & retraining pipeline

The school identified 34 high-risk students in the first month. Early interventions successfully retained 28 of them. Dropout rate dropped by 18% in the following semester.