{"id":15063933,"url":"https://github.com/smahala02/svm-machine-learning","last_synced_at":"2026-01-30T15:32:42.049Z","repository":{"id":255860857,"uuid":"853768561","full_name":"smahala02/SVM-Machine-Learning","owner":"smahala02","description":"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.","archived":false,"fork":false,"pushed_at":"2024-09-07T13:39:45.000Z","size":93,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-22T08:38:05.553Z","etag":null,"topics":["classification","data-science","machine-learning","python","scikit-learn","svm"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/smahala02.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-07T13:38:45.000Z","updated_at":"2024-09-07T13:40:28.000Z","dependencies_parsed_at":"2024-09-07T15:09:45.870Z","dependency_job_id":"d904b6b8-db13-479e-b9ce-550da6e0901c","html_url":"https://github.com/smahala02/SVM-Machine-Learning","commit_stats":null,"previous_names":["smahala02/svm-machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smahala02%2FSVM-Machine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smahala02%2FSVM-Machine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smahala02%2FSVM-Machine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smahala02%2FSVM-Machine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/smahala02","download_url":"https://codeload.github.com/smahala02/SVM-Machine-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243779220,"owners_count":20346679,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification","data-science","machine-learning","python","scikit-learn","svm"],"created_at":"2024-09-25T00:08:50.572Z","updated_at":"2026-01-30T15:32:42.005Z","avatar_url":"https://github.com/smahala02.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SVM Machine Learning\n\n## Overview\nThis 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.\n\nSupport Vector Machines are particularly effective in high-dimensional spaces and are often used in applications like image classification, text categorization, and bioinformatics.\n\n## Contents\n1. `SVM Machine Learning.ipynb` – The Jupyter Notebook containing the SVM tutorial and example code.\n2. `README.md` – This file, providing an overview of the repository and usage instructions.\n\n## Navigation\nThe following topics are covered in the Jupyter Notebook:\n- **Introduction to SVM**: Explanation of the theory behind Support Vector Machines.\n- **Mathematical Foundations**: A deep dive into the mathematics and concepts, such as margin, support vectors, and kernel tricks.\n- **SVM for Classification**: Example implementations of SVM for binary and multiclass classification tasks.\n- **Visualization**: Visualization of hyperplanes and decision boundaries created by SVM.\n- **Hyperparameter Tuning**: Techniques for optimizing SVM using parameters like C and gamma.\n  \n## Usage\n\n### Prerequisites\nTo use this repository, you will need the following tools installed:\n- [Python](https://www.python.org/downloads/)\n- [Jupyter Notebook](https://jupyter.org/install)\n- Python libraries: `numpy`, `matplotlib`, `pandas`, `scikit-learn` (install via `pip` if necessary).\n\n### Installation Steps\n\n1. Clone the repository to your local machine:\n   ```bash\n   git clone https://github.com/smahala02/SVM-Machine-Learning.git\n   ```\n\n2. Navigate to the directory:\n   ```bash\n   cd SVM-Machine-Learning\n   ```\n\n3. Open the Jupyter Notebook:\n   ```bash\n   jupyter notebook \"SVM Machine Learning.ipynb\"\n   ```\n\n4. Run the notebook to follow along with the explanations, execute the code, and apply SVM to your own datasets.\n\n## License\nThis project is licensed under the MIT License. See the `LICENSE` file for more details.\n\n## Contributing\nWe welcome contributions to improve this project! If you would like to contribute, follow these steps:\n\n1. Fork this repository.\n2. Create a new branch (`git checkout -b feature-branch`).\n3. Make your changes and commit them (`git commit -m 'Add new feature'`).\n4. Push to the branch (`git push origin feature-branch`).\n5. Open a pull request, and we will review it.\n\nIf you find any bugs or have suggestions for improvements, feel free to raise an issue.\n\n## Author\n- [smahala02](https://github.com/smahala02)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmahala02%2Fsvm-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmahala02%2Fsvm-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmahala02%2Fsvm-machine-learning/lists"}