Machine Learning Algorithms

Welcome to the 🤖 Machine Learning Algorithms 📊 repository! This repository contains a comprehensive collection of machine learning algorithm implementations, organized by topic. Each section includes detailed examples, code, and explanations to help you understand and apply various machine learning techniques.

Repository Structure

• 1. Introduction to Machine Learning: Basics of machine learning, key concepts, and foundational knowledge.
• 2. Linear Regression: Implementation and explanation of linear regression algorithms.
• 3. Logistic Regression: Detailed guide on logistic regression for classification tasks.
• 4. K-Nearest Neighbors (KNN): Example and code for KNN algorithm.
• 5. CART (Classification and Regression Trees): Implementation of decision trees for both classification and regression.
• 6. Advanced Tree: Advanced techniques and improvements on basic decision trees.
• 7. Unsupervised Learning: Algorithms and examples for clustering and other unsupervised learning techniques.
• catboost_info: Information and examples related to the CatBoost algorithm.
• Solutions: Solutions to exercises and problems presented in the various sections.

Getting Started

Prerequisites

To run the code in this repository, you'll need Python 3.x 🐍 and some common machine learning libraries. It's recommended to use a virtual environment to manage dependencies.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Machine-learning-algorithms.git
   cd Machine-learning-algorithms

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

Usage

Navigate to the folder of the algorithm you are interested in and follow the instructions provided in the respective README files or notebooks.

For example, to run the Linear Regression example:

cd 2. Linear Regression
python linear_regression.py

Contributing

Contributions are welcome! If you have an improvement or a new algorithm to add, please fork the repository and submit a pull request.

• Fork the repository
• Create a new branch (git checkout -b feature-branch)
• Make your changes
• Commit your changes (git commit -am 'Add new feature')
• Push to the branch (git push origin feature-branch)
• Create a new Pull Request

License

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