Advanced Data Science Suite

Production‑ready analytics, machine learning, and AI demos
🌐 Live Demo · 🐛 Report Bug · 💬 Discussions

• ✨ Overview
• 📦 Applications
• 🏗️ Architecture
• 🛠️ Installation & Setup
• ▶️ Running the Suite
• 📂 Project Structure
• ⚙️ Configuration
• 🧪 Development
• 🔐 Security
• 🤝 Contributing
• 📄 License

This repository contains a collection of advanced, interactive data science applications built with Streamlit. Each app demonstrates cutting‑edge techniques in machine learning, natural language processing, computer vision, time series, and explainable AI. The suite is designed to be modular, extensible, and ready for both learning and prototyping.

Each app is a self‑contained Streamlit module. A quick overview:

graph TD
    A[User] --> B[Streamlit UI]
    B --> C[Application Logic]
    C --> D[Data Processing]
    C --> E[ML / AI Models]
    C --> F[External APIs]
    D --> G[Pandas / NumPy]
    E --> H[Scikit‑learn / XGBoost]
    F --> I[OpenAI / yFinance / NewsAPI]

All apps share:

• Streamlit for the frontend
• Pandas & NumPy for data handling
• Scikit‑learn for classical ML
• Plotly for interactive visualizations
• OpenAI (optional) for LLM‑powered explanations

• Python 3.10+
• pip
• (Optional) OpenAI API key for LLM features

git clone https://github.com/yourusername/advanced-data-science-suite.git
cd advanced-data-science-suite

python -m venv .venv
source .venv/bin/activate      # Linux/macOS
.venv\Scripts\activate         # Windows

pip install -r requirements.txt

Note: requirements.txt includes all necessary packages. See the file for the complete list.

For apps that use OpenAI or external APIs, create a .streamlit/secrets.toml file:

[openai]
api_key = "your_openai_key"

[tmdb]
api_key = "your_tmdb_key"

[newsapi]
api_key = "your_newsapi_key"

[finnhub]
api_key = "your_finnhub_key"

Each app is a standalone script. Run any of them with:

streamlit run app_name.py

For example:

streamlit run decision_tree.py

To run the entire suite, you can use a launcher script or manually start each app on a different port.

Create a run_all.py that uses streamlit.web.cli to launch multiple apps, or use st.sidebar navigation in a main page.

advanced-data-science-suite/
│
├── app1.py                # decision_tree.py
├── app2.py                # sen.py
├── ...                    # all other apps
│
├── requirements.txt
├── README.md
├── .streamlit/
│   └── secrets.toml.example
│
└── models/                # (optional) pre‑trained models

• Secrets: Stored in .streamlit/secrets.toml (not committed)
• Sample Data: Most apps generate synthetic data or use built‑in datasets.
• Model Paths: Some apps (e.g., music) may expect pre‑trained models in models/. Place your models there or update the path.

1. Create a new .py file following the same UI pattern (header, sidebar, tabs).
2. Import shared utilities if any.
3. Add the app to the documentation.

Currently no test suite is included, but we encourage adding unit tests for critical functions.

• Never commit API keys or secrets.
• Use .streamlit/secrets.toml locally and exclude it from version control.
• Rotate keys if accidentally exposed.

Contributions are welcome! Please:

1. Fork the repository.
2. Create a feature branch.
3. Make your changes.
4. Submit a pull request with a clear description.

For major changes, open an issue first to discuss.

This project is distributed under the MIT License. See LICENSE for details.

Made with ❤️ by GZ and contributors.