Explainable AI package using SHAP
Project description
🧠 AutoExplainML
A production-ready machine learning explainability tool that converts complex ML model behavior into simple human-readable insights using SHAP.
🌐 Live Demo
- Backend API: https://autoexplainml.onrender.com
- Frontend App: https://autoexplainml-ui.onrender.com
🚀 Features
- Upload trained ML models (.pkl)
- Upload datasets (.csv)
- Get automatic feature importance explanations
- SHAP-based model interpretation
- Clean web UI (Streamlit)
- FastAPI backend
- Cloud deployment (Render)
📌 Use Cases
🧠 Data Scientists
Understand which features influence model predictions.
🎓 Students
Learn how ML models behave internally.
🏢 Business Analysts
Gain transparency in AI-based decisions.
🧠 Tech Stack
- Python
- FastAPI
- Streamlit
- SHAP
- Pandas, NumPy
- Scikit-learn
- Render (Deployment)
📦 Architecture
Frontend (Streamlit UI) ↓ Backend API (FastAPI) ↓ SHAP Explainer Engine ↓ ML Model Output
📸 Screenshots
⚙️ Run Locally
Backend
uvicorn backend.api:app --reload
Frontend
streamlit run frontend/app.py
Backend
👨💻 Author
Sidhant Narang
Release history Release notifications | RSS feed
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