title	emoji	colorFrom	colorTo	sdk	pinned	app_port
Nuqta - AI Stock Prediction System	📈	blue	green	docker	false	7860

� Nuqta | AI Market Insight & Stock Predictor

Nuqta is an advanced End-to-End Machine Learning System designed for real-time stock price prediction and market regime analysis. It leverages ensemble modeling (Linear Regression, Random Forest, SVM) and unsupervised learning to provide actionable financial insights through a premium, "Modern Islamic FinTech" aesthetic dashboard.

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🚀 Live Demo

Check out the deployed application on Hugging Face Spaces:

👉 Nuqta Stock Predictor (Live App)

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🌟 Key Features

• 📈 Multi-Model AI Predictions:
  combines Regression (price targets), Classification (trend direction), and Clustering (market volatility regimes) for robust decision support.
• ⏱️ Real-Time Market Data:
  Fetches live stock data for global markets (USA, Pakistan, India, UK, etc.) using the Alpha Vantage API.
• 🎨 Premium UI/UX:
  A highly responsive, glassmorphism-inspired interface built with Streamlit and custom CSS, featuring interactive Plotly charts.
• 🔔 Smart Notifications:
  Integrated Discord Alerts to notify users of significant price movements and prediction updates.
• 🔄 Automated MLOps Pipeline:
  Fully automated training and deployment pipelines using Prefect for orchestration and GitHub Actions for CI/CD.
• ☁️ Cloud Native:
  Containerized with Docker and deployed seamlessly on cloud platforms.

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🛠️ Tech Stack

• Frontend: Streamlit, Plotly, HTML/CSS (Custom Styling)
• Backend & Logic: Python, Scikit-Learn, Pandas, NumPy, SciPy
• Data Source: Alpha Vantage, Yahoo Finance (yfinance)
• DevOps & MLOps: Docker, GitHub Actions, Prefect, Hugging Face Hub

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� Project Structure

mlops-project/
├── .github/              # CI/CD workflows (GitHub Actions)
├── data/                 # Raw and processed datasets
├── models/               # Serialized trained models (.pkl)
├── src/                  # Source code modules
│   ├── api/              # API endpoints (if applicable)
│   ├── ingestion/        # Data fetching scripts
│   ├── processing/       # Feature engineering & preprocessing
│   ├── models/           # Model definitions & training logic
│   ├── orchestration/    # Prefect flows & monitoring scripts
├── tests/                # Unit and integration tests
├── app.py                # Main Streamlit application entry point
├── Dockerfile            # Container configuration
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

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⚙️ Installation & Setup

Follow these steps to run the project locally.

1. Prerequisites

• Python 3.10 or higher
• Git
• Alpha Vantage API Key (Free key available)

2. Clone the Repository

git clone https://github.com/Isaadqurashi/mlops-project.git
cd mlops-project

3. Set Up Environment

Create a .env file in the root directory and add your API keys:

# .env
ALPHA_VANTAGE_API_KEY=your_alpha_vantage_key
HF_TOKEN=your_huggingface_token  # Optional: for cloud training
WEBHOOK_URL=your_discord_webhook   # Optional: for notifications

4. Install Dependencies

It is recommended to use a virtual environment:

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

pip install -r requirements.txt

5. Run the Application

streamlit run app.py

The app will open in your browser at http://localhost:8501.

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🐳 Docker Setup

You can also run the application using Docker to ensure a consistent environment.

# Build the image
docker build -t nuqta-predictor .

# Run the container
docker run -p 7860:7860 --env-file .env nuqta-predictor

Access the app at http://localhost:7860.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Built with ❤️ by the Nuqta Team