| tags: | |
| - xgboost | |
| - tabular-classification | |
| - education | |
| - student-success | |
| # 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. | |