---
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.