Welcome to the Machine Learning Classifier Comparison project! In this project, we explore and compare four popular machine learning classification algorithms: K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, and Support Vector Machine (SVM).

• Introduction
• Features
• Getting Started
• Installation
• Usage
• Results
• Contributing
• License

Machine learning classifiers are a crucial part of data analysis, and choosing the right algorithm can significantly impact the quality of results. In this project, we aim to compare the performance of four popular classification algorithms: KNN, Naive Bayes, Decision Tree, and SVM. By the end of this project, you will have a clear understanding of how these algorithms work and their effectiveness in various scenarios.

• Compare the performance of KNN, Naive Bayes, Decision Tree, and SVM.
• Visualize and interpret the results.
• Understand the pros and cons of each classification algorithm.
• Use this project as a reference for your own classification tasks.

To get started with this project, make sure you have the following prerequisites:

• Python 3.x
• Jupyter Notebook
• Required Python libraries (NumPy, Pandas, Matplotlib, Scikit-learn)

1. Clone this repository to your local machine using git clone.
2. Navigate to the project directory.

1. Open Jupyter Notebook and load the project's main notebook.
2. Follow the step-by-step instructions to explore and compare the classification algorithms.
3. Visualize the results and gain insights into each algorithm's performance.

This project provides a detailed comparison of KNN, Naive Bayes, Decision Tree, and SVM. You will find interactive visualizations and insights in the Jupyter Notebook. Feel free to use the results as a reference for your own machine learning projects.

We welcome contributions! If you want to improve this project or add new features, please follow these steps:

1. Fork this repository.
2. Create a new branch for your feature: git checkout -b feature-name.
3. Commit your changes: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature-name.
5. Submit a pull request.

This project is open-source and available under the MIT License.

Happy machine learning!