{"id":21055410,"url":"https://github.com/mugisha-pascal/machine-learning","last_synced_at":"2026-02-12T15:32:26.781Z","repository":{"id":247842897,"uuid":"825758800","full_name":"MUGISHA-Pascal/Machine-Learning","owner":"MUGISHA-Pascal","description":"A variety of projects expressing my full journey in machine learning and deep learning using python and jupyter notebook for documentation","archived":false,"fork":false,"pushed_at":"2026-02-03T09:15:40.000Z","size":13254,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-02-03T20:31:14.422Z","etag":null,"topics":["joblib","machine-learning","matplotlib","pandas","sklearn","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/MUGISHA-Pascal.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":"2024-07-08T12:46:12.000Z","updated_at":"2026-02-03T09:15:45.000Z","dependencies_parsed_at":"2025-02-05T20:36:50.011Z","dependency_job_id":"36a49cfc-2a92-4feb-9257-f37cd4bf3fbc","html_url":"https://github.com/MUGISHA-Pascal/Machine-Learning","commit_stats":null,"previous_names":["mugisha-pascal/machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/MUGISHA-Pascal/Machine-Learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MUGISHA-Pascal%2FMachine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MUGISHA-Pascal%2FMachine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MUGISHA-Pascal%2FMachine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MUGISHA-Pascal%2FMachine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MUGISHA-Pascal","download_url":"https://codeload.github.com/MUGISHA-Pascal/Machine-Learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MUGISHA-Pascal%2FMachine-Learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29370547,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-12T08:51:36.827Z","status":"ssl_error","status_checked_at":"2026-02-12T08:51:26.849Z","response_time":55,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["joblib","machine-learning","matplotlib","pandas","sklearn","tensorflow"],"created_at":"2024-11-19T16:44:30.143Z","updated_at":"2026-02-12T15:32:26.761Z","avatar_url":"https://github.com/MUGISHA-Pascal.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Repository\n\nA comprehensive collection of machine learning projects, tutorials, experiments, and learning sessions covering various ML algorithms, datasets, and real-world applications.\n\n## Repository Structure\n\n```\n.\n├── notebooks/              # Jupyter notebooks organized by purpose\n│   ├── tutorials/         # Learning materials and algorithm implementations\n│   │   ├── algorithms/    # Linear regression, MNIST, and other algorithms\n│   │   ├── backpropagation/\n│   │   ├── computer-vision/\n│   │   └── convolutional-neural-network/\n│   ├── examples/          # Dataset examples and demonstrations\n│   │   ├── breastCancer.ipynb\n│   │   ├── CaliforniaHousing.ipynb\n│   │   └── SVM(irisdataset).ipynb\n│   ├── experiments/       # Test notebooks and custom implementations\n│   │   ├── CSV_to_dataset_keras.ipynb\n│   │   └── Keras_custom_model.ipynb\n│   └── visualization/     # Data visualization notebooks and resources\n│       └── data_visualization.ipynb\n├── projects/              # Production-ready ML projects with Flask APIs\n│   ├── breast-cancer-project/\n│   ├── california-housing-project/\n│   ├── diabetes-project/\n│   ├── irisFeature-project/\n│   ├── music-genre-generation-project/\n│   ├── student-grade-project/\n│   ├── student_performance_index/\n│   ├── videoGame-project/\n│   └── wine-project/\n└── sessions/              # Learning sessions and practice work\n    ├── 02-02-2026/\n    ├── 19-01-2026/\n    └── 22-01-2026_Classification/\n```\n\n## Getting Started\n\n1. **Learning**: Navigate to `notebooks/tutorials/` for algorithm implementations and learning materials\n2. **Examples**: Check `notebooks/examples/` for dataset-specific demonstrations (Breast Cancer, California Housing, Iris SVM)\n3. **Experiments**: Explore `notebooks/experiments/` for custom Keras models and data processing techniques\n4. **Projects**: Browse `projects/` for complete ML applications with APIs and demos\n5. **Sessions**: Review `sessions/` for dated learning sessions and classification work\n\n## Projects\n\nEach project folder typically contains:\n- **Training scripts** (`train.py`) - Model training and evaluation\n- **Flask API** (`app.py`) - REST API for model predictions\n- **Trained models** (`model/`) - Serialized model files\n- **Demo applications** (`demo/`, `nodeApp/`) - Frontend interfaces for testing\n\n### Available Projects\n- **Breast Cancer Detection** - Classification model for cancer diagnosis\n- **California Housing** - Regression model for housing price prediction\n- **Diabetes Prediction** - Healthcare prediction model\n- **Iris Feature Classification** - Classic iris dataset classification\n- **Music Genre Generation** - Audio/music classification\n- **Student Grade Prediction** - Educational performance prediction\n- **Student Performance Index** - Academic performance analysis\n- **Video Game Analysis** - Gaming data analysis\n- **Wine Quality** - Wine classification/regression\n\n## Notebooks\n\n### Tutorials\nComprehensive learning materials covering:\n- Algorithm implementations (linear regression, neural networks, etc.)\n- Backpropagation fundamentals\n- Computer vision techniques\n- Convolutional neural networks (CNNs)\n\n### Examples\nReal-world dataset implementations:\n- Breast cancer classification using various algorithms\n- California housing price prediction\n- Support Vector Machines (SVM) on iris dataset\n\n### Experiments\nCustom implementations and explorations:\n- CSV to Keras dataset conversion\n- Custom Keras model architectures\n\n### Visualization\nData analysis and visualization techniques for ML datasets\n\n## Sessions\n\nDated learning sessions containing practice work, experiments, and specific topic explorations (e.g., classification techniques, recommendation systems)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmugisha-pascal%2Fmachine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmugisha-pascal%2Fmachine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmugisha-pascal%2Fmachine-learning/lists"}