ML-Notebook 🧠

A collection of Jupyter notebooks for ML experiments—linear regression, classification, data visualization, and more. Built for learning, sharing, and collaborating on machine learning workflows.

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📋 Table of Contents

• About
• Dependencies
• Setup
• Usage
• Contributing
• License

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ℹ️ About

This repo contains various Jupyter notebooks demonstrating core machine learning techniques using Python, scikit-learn, and deep learning libraries. Whether you’re learning or teaching ML, these examples are meant to be educational, reusable, and shareable.

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🧰 Dependencies

The project uses the following tools and libraries:

• Python 3.8+
• JupyterLab or Jupyter Notebook
• numpy, pandas, scikit-learn, matplotlib, seaborn
• (Optional) torch, torchvision, tensorflow for deep learning

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🚀 Setup

1. Clone the repo

   git clone https://github.com/Developer-Tanay/ML-Notebook.git
   cd ML-Notebook

2. Create a virtual environment

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

   If requirements.txt isn’t provided, install manually:

   pip install numpy pandas scikit-learn matplotlib seaborn

4. Launch JupyterLab

   jupyter lab

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💻 Usage

• Open any notebook in JupyterLab
• Run all cells via Menu → Run → Run All Cells
• Modify parameters, models, or datasets as you like
• Save your notebook to preserve your analysis or share your changes

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🤝 Contributing

Contributions are welcome! Here’s how you can help:

1. Fork the repo

2. Create a new branch:

   git checkout -b feature/my-feature

3. Make your changes, add or update notebooks

4. Commit your work:

   git commit -m "Add feature: my feature"

5. Push your branch:

   git push origin feature/my-feature

6. Open a Pull Request—I’ll review and merge it!

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📝 License

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

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TL;DR: clone ➕ install ➕ run ➕ explore ➕ update ➕ PR = ❤️

Happy modeling! 🚀