{"id":21246715,"url":"https://github.com/soumyadipta2020/ml-pyspark","last_synced_at":"2026-06-08T16:04:39.965Z","repository":{"id":257892261,"uuid":"869107740","full_name":"Soumyadipta2020/ML-PySpark","owner":"Soumyadipta2020","description":"Machine Learning in PySpark","archived":false,"fork":false,"pushed_at":"2024-11-21T15:09:25.000Z","size":3384,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-10T01:32:36.282Z","etag":null,"topics":["coding","data-science","jupyter-notebook","machine-learning","machine-learning-algorithms","modelling","notebook","notebook-jupyter","pyspark","python","statistical-analysis"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Soumyadipta2020.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":"2024-10-07T18:21:07.000Z","updated_at":"2024-11-21T15:09:30.000Z","dependencies_parsed_at":"2025-03-20T00:00:46.572Z","dependency_job_id":null,"html_url":"https://github.com/Soumyadipta2020/ML-PySpark","commit_stats":null,"previous_names":["soumyadipta2020/ml-pyspark"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Soumyadipta2020/ML-PySpark","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumyadipta2020%2FML-PySpark","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumyadipta2020%2FML-PySpark/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumyadipta2020%2FML-PySpark/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumyadipta2020%2FML-PySpark/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Soumyadipta2020","download_url":"https://codeload.github.com/Soumyadipta2020/ML-PySpark/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soumyadipta2020%2FML-PySpark/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34069500,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-08T02:00:07.615Z","response_time":111,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["coding","data-science","jupyter-notebook","machine-learning","machine-learning-algorithms","modelling","notebook","notebook-jupyter","pyspark","python","statistical-analysis"],"created_at":"2024-11-21T02:03:22.107Z","updated_at":"2026-06-08T16:04:39.948Z","avatar_url":"https://github.com/Soumyadipta2020.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning with PySpark 🤖🧠👾\n\n![GitHub Repo stars](https://img.shields.io/github/stars/Soumyadipta2020/ML-PySpark?style=social)\n![GitHub forks](https://img.shields.io/github/forks/Soumyadipta2020/ML-PySpark?style=social)\n![GitHub license](https://img.shields.io/github/license/Soumyadipta2020/ML-PySpark)\n[![HitCount](https://hits.dwyl.com/Soumyadipta2020/ML-PySpark.svg?style=flat-square)](http://hits.dwyl.com/Soumyadipta2020/ML-PySpark)\n\nThis repository contains a **Jupyter Notebook** that demonstrates how to perform **Machine Learning** tasks using **PySpark**. It is designed to provide a hands-on, practical introduction to using the Spark MLlib library for scalable machine learning.\n\n## 📚 Contents\n\n- **Notebook**: \n  - `ML-pyspark.ipynb`: A sample Jupyter Notebook showcasing end-to-end machine learning workflows in PySpark.\n  \n- **Sample Dataset**: \n  - Includes a small dataset for demonstration purposes.\n\n## 🌟 Features Demonstrated\n\n1. **Data Loading and Exploration**\n   - Loading datasets using Spark's DataFrame API.\n   - Data cleaning and preprocessing.\n\n2. **Feature Engineering**\n   - Transforming and scaling features.\n   - Using `VectorAssembler` and other Spark tools.\n\n3. **Model Training**\n   - Training ML models using Spark MLlib (e.g., Linear Regression, Random Forest).\n   - Cross-validation and hyperparameter tuning.\n\n4. **Model Evaluation**\n   - Using metrics such as RMSE, R2, accuracy, etc., to evaluate models.\n\n5. **Prediction**\n   - Generating predictions on test datasets.\n   - Saving and loading trained models.\n\n\n## 📑 Requirements\n\n- **Python**: 3.8+\n- **PySpark**: 3.x\n- **Jupyter Notebook**: Installed via Anaconda or pip.\n- **Docker** (Optional): For setting up a PySpark environment. (being used here)\n\n## 📢 Getting Started\n\n1. **Clone the Repository**:\n   ```bash\n   git clone https://github.com/Soumyadipta2020/ML-PySpark.git\n   cd machine-learning-pyspark\n   ```\n\n2. **Set Up the Environment**:\n   - Install dependencies:\n     ```bash\n     pip install -r requirements.txt\n     ```\n   - Alternatively, set up a Docker container:\n     ```bash\n     docker pull quay.io/jupyter/all-spark-notebook\n     docker run -it --name pyspark-jupy --mount type=bind,source=your_location,target=/app/data --rm -p 8888:8888 quay.io/jupyter/all-spark-notebook\n     ```\n\n3. **Launch the Notebook**:\n   ```bash\n   jupyter notebook ML-pyspark.ipynb\n   ```\n\n4. **Run the Notebook**:\n   - Follow the step-by-step instructions in the notebook to explore PySpark’s MLlib capabilities.\n\n## ⚡ Usage\n\nThis notebook is for:\n- **Beginners**: Learn the basics of machine learning with PySpark.\n- **Practitioners**: Use it as a template for building scalable ML solutions.\n- **Educators**: Teach Spark MLlib concepts interactively.\n\n## 🤝 Contributing\nWe welcome contributions! If you have additional sample codes or improvements, please:\n\n - Fork this repository.\n - Create a feature branch:\n```bash\ngit checkout -b feature/your-feature-name\n```\n - Commit your changes and push the branch:\n```bash\ngit push origin feature/your-feature-name\n```\n - Open a Pull Request.\n\nMake sure your code follows the repository's style and is well-documented.\n\n## 🪧 Acknowledgments\n\n- **Apache Spark**: The framework powering this project.\n- **Jupyter Project**: For the interactive environment.\n- Sample datasets sourced from publicly available resources for demonstration purposes. \n\n--- \n\nHappy learning! 🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoumyadipta2020%2Fml-pyspark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsoumyadipta2020%2Fml-pyspark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoumyadipta2020%2Fml-pyspark/lists"}