🤖 Machine Learning Algorithms

This repository contains implementations of various machine learning algorithms in Python. Each algorithm is implemented from scratch to provide a deeper understanding of how they work.

📋 Table of Contents

1. Installation
2. Usage
3. Algorithms Implemented
4. Contributing
5. License

🛠️ Installation

To use the algorithms provided in this repository, you'll need Python 3.x installed on your system. Additionally, you can install the required dependencies using pip:

pip install -r requirements.txt

▶️ Usage

Each algorithm is implemented as a separate Python script in the algorithms directory. To use a specific algorithm, simply run its corresponding script. For example:

python decision_tree.py

This will execute the Decision Tree algorithm and display the results.

🧠 Algorithms Implemented

Currently, the following algorithms have been implemented:

• Decision Tree
• K-Nearest Neighbors
• Linear Regression
• Logistic Regression
• Naive Bayes
• Random Forest
• Support Vector Machine

Each algorithm is well-documented with explanations of its implementation and usage.

🤝 Contributing

Contributions to this repository are welcome. If you'd like to contribute an implementation of a new algorithm or improve an existing one, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/improvement)
3. Implement your changes.
4. Test your changes thoroughly.
5. Commit your changes (git commit -am 'Add new feature')
6. Push to the branch (git push origin feature/improvement)
7. Create a new Pull Request.

📄 License

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