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https://github.com/smahala02/svm-machine-learning
This repository provides an in-depth tutorial and practical implementation of Support Vector Machines (SVM) for classification tasks, using Python and popular data science libraries.
https://github.com/smahala02/svm-machine-learning
classification data-science machine-learning python scikit-learn svm
Last synced: 26 days ago
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This repository provides an in-depth tutorial and practical implementation of Support Vector Machines (SVM) for classification tasks, using Python and popular data science libraries.
- Host: GitHub
- URL: https://github.com/smahala02/svm-machine-learning
- Owner: smahala02
- License: mit
- Created: 2024-09-07T13:38:45.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T13:39:45.000Z (2 months ago)
- Last Synced: 2024-10-12T23:43:35.738Z (26 days ago)
- Topics: classification, data-science, machine-learning, python, scikit-learn, svm
- Language: Jupyter Notebook
- Homepage:
- Size: 90.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# SVM Machine Learning
## Overview
This repository contains a tutorial and practical implementation of **Support Vector Machines (SVM)**, a powerful supervised machine learning algorithm used for classification and regression tasks. The **Jupyter Notebook** provided explains the theory behind SVM, demonstrates how the algorithm works, and showcases practical examples of its application on real datasets.
Support Vector Machines are particularly effective in high-dimensional spaces and are often used in applications like image classification, text categorization, and bioinformatics.
## Contents
1. `SVM Machine Learning.ipynb` – The Jupyter Notebook containing the SVM tutorial and example code.
2. `README.md` – This file, providing an overview of the repository and usage instructions.
## Navigation
The following topics are covered in the Jupyter Notebook:
- **Introduction to SVM**: Explanation of the theory behind Support Vector Machines.
- **Mathematical Foundations**: A deep dive into the mathematics and concepts, such as margin, support vectors, and kernel tricks.
- **SVM for Classification**: Example implementations of SVM for binary and multiclass classification tasks.
- **Visualization**: Visualization of hyperplanes and decision boundaries created by SVM.
- **Hyperparameter Tuning**: Techniques for optimizing SVM using parameters like C and gamma.
## Usage
### Prerequisites
To use this repository, you will need the following tools installed:
- [Python](https://www.python.org/downloads/)
- [Jupyter Notebook](https://jupyter.org/install)
- Python libraries: `numpy`, `matplotlib`, `pandas`, `scikit-learn` (install via `pip` if necessary).
### Installation Steps
1. Clone the repository to your local machine:
```bash
git clone https://github.com/smahala02/SVM-Machine-Learning.git
```
2. Navigate to the directory:
```bash
cd SVM-Machine-Learning
```
3. Open the Jupyter Notebook:
```bash
jupyter notebook "SVM Machine Learning.ipynb"
```
4. Run the notebook to follow along with the explanations, execute the code, and apply SVM to your own datasets.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more details.
## Contributing
We welcome contributions to improve this project! If you would like to contribute, follow these steps:
1. Fork this repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes and commit them (`git commit -m 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Open a pull request, and we will review it.
If you find any bugs or have suggestions for improvements, feel free to raise an issue.
## Author
- [smahala02](https://github.com/smahala02)