{"id":51533055,"url":"https://github.com/smmariquit/ml-iris-workshop","last_synced_at":"2026-07-09T05:01:32.692Z","repository":{"id":369329761,"uuid":"1063744240","full_name":"smmariquit/ml-iris-workshop","owner":"smmariquit","description":"QCU Python data-science workshop materials — hands-on tutorials covering Pandas, scikit-learn, and scikit-image with the classic Iris dataset.","archived":false,"fork":false,"pushed_at":"2026-07-04T18:45:58.000Z","size":10,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-07-04T19:11:56.358Z","etag":null,"topics":["data-science","iris-dataset","machine-learning","pandas","python","scikit-image","scikit-learn","tutorial","workshop"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/smmariquit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-25T04:08:47.000Z","updated_at":"2026-07-04T18:46:02.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/smmariquit/ml-iris-workshop","commit_stats":null,"previous_names":["smmariquit/ml-iris-workshop"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/smmariquit/ml-iris-workshop","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smmariquit%2Fml-iris-workshop","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smmariquit%2Fml-iris-workshop/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smmariquit%2Fml-iris-workshop/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smmariquit%2Fml-iris-workshop/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/smmariquit","download_url":"https://codeload.github.com/smmariquit/ml-iris-workshop/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smmariquit%2Fml-iris-workshop/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35287405,"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":"2026-07-09T02:00:07.329Z","response_time":57,"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":["data-science","iris-dataset","machine-learning","pandas","python","scikit-image","scikit-learn","tutorial","workshop"],"created_at":"2026-07-09T05:01:31.893Z","updated_at":"2026-07-09T05:01:32.668Z","avatar_url":"https://github.com/smmariquit.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Python Data Science Tutorial Collection\n\nA full collection of Python tutorials covering essential data science libraries: **Pandas**, **Scikit-Learn**, and **Scikit-Image**. This repository contains hands-on examples and exercises perfect for learning data manipulation, machine learning, and image processing.\n\n## 📁 Project Structure\n\n```\nQCU/\n├── README.md                    # This file\n├── polyA_helix_CA.pdb          # Protein structure file\n├── iris/                       # Dataset folder\n│   ├── database.sqlite         # SQLite database\n│   └── Iris.csv               # Famous Iris flower dataset\n├── pandas/                     # Pandas tutorials\n│   ├── pandas01.py            # Introduction to data structures\n│   ├── pandas02.py            # Data manipulation basics\n│   ├── pandas03.py            # Advanced data operations\n│   ├── pandas04.py            # Data analysis techniques\n│   ├── pandas05.py            # Data visualization\n│   └── pandas06.py            # Real-world data processing\n├── sklearn/                    # Scikit-Learn tutorials\n│   └── sklearn_tutorial.py    # Complete ML tutorial with Iris dataset\n└── skimage/                    # Scikit-Image tutorials\n    └── skimage_tutorial.py     # Complete image processing tutorial\n```\n\n## 🐼 Pandas Tutorials\n\nThe `pandas/` folder contains a progressive series of tutorials covering:\n\n- **pandas01.py**: Introduction to Pandas data structures (Series, DataFrame)\n- **pandas02.py**: Data manipulation and basic operations\n- **pandas03.py**: Advanced data operations and transformations\n- **pandas04.py**: Data analysis and statistical operations\n- **pandas05.py**: Data visualization with matplotlib integration\n- **pandas06.py**: Real-world data processing examples\n\n### Getting Started with Pandas\n```bash\ncd pandas\npython pandas01.py\n```\n\n## 🤖 Scikit-Learn Tutorial\n\nThe `sklearn/` folder contains a full machine learning tutorial:\n\n- **sklearn_tutorial.py**: Complete ML workflow using the Iris dataset\n - Data loading and exploration\n - Data preprocessing and feature scaling\n - Multiple ML algorithms (Logistic Regression, Decision Trees, Random Forest, SVM)\n - Model evaluation and comparison\n - Visualizations and insights\n\n### Running the ML Tutorial\n```bash\ncd sklearn\npython sklearn_tutorial.py\n```\n\n**Note**: The tutorial automatically loads the Iris dataset from the `../iris/` directory. If the CSV file is not found, it falls back to scikit-learn's built-in dataset.\n\n## 🖼️ Scikit-Image Tutorial\n\nThe `skimage/` folder contains a full image processing tutorial:\n\n- **skimage_tutorial.py**: Complete guide to image processing\n - Loading and displaying images\n - Basic image operations\n - Filtering and enhancement\n - Edge detection and feature extraction\n - Morphological operations\n - Image segmentation\n - Real-world applications\n\n### Running the Image Processing Tutorial\n```bash\ncd skimage\npython skimage_tutorial.py\n```\n\n## 📊 Dataset Information\n\n### Iris Dataset (`iris/`)\n- **Iris.csv**: The famous iris flower dataset\n- **database.sqlite**: SQLite version of the dataset\n- Contains 150 samples of iris flowers with 4 features each\n- Perfect for classification tasks and ML learning\n\n## 🚀 Prerequisites\n\nMake sure you have the following Python libraries installed:\n\n```bash\npip install pandas numpy matplotlib seaborn scikit-learn scikit-image\n```\n\n## 📝 Usage Instructions\n\n1. **Start with Pandas**: Begin with the pandas tutorials to understand data manipulation\n2. **Progress to ML**: Move to the sklearn tutorial to learn machine learning\n3. **Explore Image Processing**: Use the skimage tutorial for computer vision tasks\n\nEach tutorial is self-contained and includes:\n- ✅ Detailed explanations and comments\n- ✅ Code examples with output\n- ✅ Visualizations and plots\n- ✅ Progressive difficulty levels\n- ✅ Real-world applications\n\n## 🎯 Learning Path\n\n### Beginner\n- Start with `pandas01.py` for basic data structures\n- Practice with `pandas02.py` for data manipulation\n- Explore `sklearn_tutorial.py` for your first ML project\n\n### Intermediate\n- Work through all pandas tutorials (`pandas01.py` - `pandas06.py`)\n- Complete the sklearn tutorial with different datasets\n- Begin with basic image processing in `skimage_tutorial.py`\n\n### Advanced\n- Combine techniques from all three libraries\n- Modify tutorials for your own datasets\n- Experiment with advanced algorithms and techniques\n\n## 🔧 Troubleshooting\n\n- **Import Errors**: Make sure all required libraries are installed\n- **File Not Found**: Ensure you're running scripts from the correct directory\n- **Dataset Issues**: The sklearn tutorial will automatically fallback to built-in datasets if files are missing\n\n## 📚 Additional Resources\n\n- [Pandas Documentation](https://pandas.pydata.org/docs/)\n- [Scikit-Learn Documentation](https://scikit-learn.org/stable/)\n- [Scikit-Image Documentation](https://scikit-image.org/)\n\n---\n\n**Happy Learning!** 🎉\n\n*This tutorial collection was organized and documented by GitHub Copilot on September 25, 2025.*","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmmariquit%2Fml-iris-workshop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmmariquit%2Fml-iris-workshop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmmariquit%2Fml-iris-workshop/lists"}