🎓 Student Risk Analyzer (Random Forest ML Project)

This project is a machine learning–based academic risk prediction system that classifies students into Low, Medium, or High risk categories based on study behavior and academic performance indicators.

The project demonstrates the complete machine learning lifecycle, including data preprocessing, feature engineering, multi-class classification using Random Forest, model evaluation, explainability through feature importance, and deployment readiness.

🚀 Running & Deploying the Application

Run Locally

git clone https://github.com/YOUR_USERNAME/student-risk-analyzer.git
cd student-risk-analyzer
pip install -r requirements.txt
cd app
python app.py

Then open:
http://127.0.0.1:5000

Deploy on Render (Optional)

Create a new Web Service on Render

Connect the GitHub repository

Use the following settings:

Root Directory: (leave empty)

Build Command:
pip install -r requirements.txt

Start Command:
gunicorn app.app:app

The application is production-ready and uses deployment-safe paths.

This is **huge** for recruiters and collaborators.

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## 5️⃣ Add a “Why This Repo Is Deployable” Section (Very Strong)

Add this to README:

```markdown
## ✅ Deployment-Ready Design

- Training and inference are fully separated
- Model artifacts are versioned and included
- Absolute file paths ensure environment independence
- Minimal dependency specification
- Production-compatible WSGI server (Gunicorn)

The project can be deployed on any standard Python hosting platform.