Edustar.AI Risk Predictor (Model 1) 🎓

This is the core AI engine for the Edustar.AI platform. It is a Machine Learning model trained to predict the likelihood of a student falling behind or dropping out based on their academic and attendance footprint.

Model Details

Architecture: XGBoost Classifier
Features Used:
- absence_rate: Percentage of school days missed
- avg_score: Average academic score across all assignments
Output: Binary classification (1 = At Risk, 0 = Safe) with a precisely calculated Risk Probability Percentage.

How it Works

The AI compares a student's current attendance and grading trajectory against a massive historical dataset. It identifies if the student's metrics match the mathematical fingerprint of historical students who eventually failed.

Intended Use

This model is designed to be integrated into school management dashboards (like the Edustar Dashboard) to provide early-warning signals to teachers and principals, allowing for timely intervention before a student actually fails.

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