{"id":26802157,"url":"https://github.com/mileristovski/machinelearning-workbench","last_synced_at":"2025-03-29T21:16:28.526Z","repository":{"id":271675878,"uuid":"914210467","full_name":"Mileristovski/MachineLearning-Workbench","owner":"Mileristovski","description":"Une API pour expérimenter et comparer des algorithmes de Machine Learning à partir de fichiers CSV","archived":false,"fork":false,"pushed_at":"2025-03-16T11:04:30.000Z","size":16,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-16T11:29:41.735Z","etag":null,"topics":["ai","data-science","machine-learning","machine-learning-algorithms","model-training","python"],"latest_commit_sha":null,"homepage":"","language":"Python","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/Mileristovski.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":"2025-01-09T06:43:45.000Z","updated_at":"2025-03-16T11:04:33.000Z","dependencies_parsed_at":"2025-01-09T08:28:03.790Z","dependency_job_id":"07de9577-f186-458a-9c40-803270e07f10","html_url":"https://github.com/Mileristovski/MachineLearning-Workbench","commit_stats":null,"previous_names":["mileristovski/cicd","mileristovski/tmp_cicd","mileristovski/machinelearning-workbench"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mileristovski%2FMachineLearning-Workbench","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mileristovski%2FMachineLearning-Workbench/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mileristovski%2FMachineLearning-Workbench/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mileristovski%2FMachineLearning-Workbench/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mileristovski","download_url":"https://codeload.github.com/Mileristovski/MachineLearning-Workbench/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246243547,"owners_count":20746312,"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":["ai","data-science","machine-learning","machine-learning-algorithms","model-training","python"],"created_at":"2025-03-29T21:16:27.942Z","updated_at":"2025-03-29T21:16:28.517Z","avatar_url":"https://github.com/Mileristovski.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Workbench\n\n## Description\nCe projet sert de Workbench pour expérimenter et rechercher différents algorithmes de Machine Learning. Il permet de tester, comparer et analyser différentes approches en utilisant des fichiers `.csv` comme source de données. L'application est exposée sous forme d'API pour faciliter l'interaction avec les modèles entraînés.\n\n## Technologies utilisées\n- Python\n- Pandas (pour la manipulation des données)\n- Scikit-learn (pour l'entraînement des modèles)\n- Flask (pour l'API)\n- Docker (pour la conteneurisation de l'application)\n\n## Utilisation\n1. Cloner le dépôt :\n   ```bash\n   git clone https://github.com/Mileristovski/MachineLearning-Workbench.git\n   cd MachineLearning-Workbench\n   ```\n2. Installer les dépendances :\n   ```bash\n   pip install -r requirements.txt\n   ```\n3. Exécuter l'API :\n   ```bash\n   python app.py\n   ```\n\n## Exécution avec Docker\n1. Construire l'image Docker :\n   ```bash\n   docker build -t ml-workbench .\n   ```\n2. Exécuter le conteneur :\n   ```bash\n   docker run --rm -p 5000:5000 ml-workbench\n   ```\n\n## Objectifs du projet\n- Tester et comparer différents algorithmes de Machine Learning\n- Expérimenter le prétraitement des données et l'ingénierie des features\n- Visualiser les performances des modèles\n- Servir de base pour de futurs projets ML\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmileristovski%2Fmachinelearning-workbench","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmileristovski%2Fmachinelearning-workbench","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmileristovski%2Fmachinelearning-workbench/lists"}