Supervised Learning Algorithms in Machine Learning

This repository focuses on various supervised learning algorithms in machine learning. It provides a comprehensive overview and implementation of popular algorithms such as k Nearest Neighbors (kNN), Decision Trees, Boosting, and Support Vector Machines (SVM).

Table of Contents

• Introduction
• Algorithms Covered
• Usage
• Contributing

Introduction

In this repository, you will find code implementations, explanations, and practical examples of supervised learning algorithms. Each algorithm is explored in detail, covering concepts, advantages, limitations, and implementation considerations.

Algorithms Covered

The repository covers the following supervised learning algorithms:

• k Nearest Neighbors (kNN)
• Decision Trees
• Boosting
• Support Vector Machines (SVM)
• Other Ensemble Techniques

Usage

To explore and utilize the code and resources in this repository, follow these steps:

1. Clone the repository to your local machine using the command: git clone https://github.com/your-username/supervised-learning-algorithms-ml.git
2. Navigate to the cloned directory: cd supervised-learning-algorithms-ml
3. Choose the algorithm of interest and access the corresponding folder.
4. Inside each algorithm folder, you will find code examples, documentation, and any additional resources related to that algorithm.

Feel free to customize and modify the code to suit your specific needs. Contributions and enhancements are always welcome.

Contributing

Contributions to this repository are greatly appreciated. If you find any issues or have ideas for improvement, please open an issue or submit a pull request.