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README.md
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---
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tags:
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- xgboost
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- tabular-classification
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- education
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- student-success
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---
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# Edustar.AI Risk Predictor (Model 1) 🎓
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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.
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## Model Details
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- **Architecture:** XGBoost Classifier
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- **Features Used:**
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- `absence_rate`: Percentage of school days missed
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- `avg_score`: Average academic score across all assignments
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- **Output:** Binary classification (1 = At Risk, 0 = Safe) with a precisely calculated Risk Probability Percentage.
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## How it Works
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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.
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## Intended Use
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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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