{"id":22794583,"url":"https://github.com/colintr/projet_de_session_techniques_apprentissage","last_synced_at":"2025-09-13T07:44:44.639Z","repository":{"id":91542519,"uuid":"299550305","full_name":"ColinTr/Projet_de_session_techniques_apprentissage","owner":"ColinTr","description":"Machine Learning, comparaison de 6 méthodes de classification de données","archived":false,"fork":false,"pushed_at":"2020-12-18T14:03:46.000Z","size":3707,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-30T17:46:22.269Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","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/ColinTr.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}},"created_at":"2020-09-29T08:17:11.000Z","updated_at":"2021-01-12T15:49:26.000Z","dependencies_parsed_at":null,"dependency_job_id":"85ecce77-338d-47b9-9767-bd7d6d3ee33d","html_url":"https://github.com/ColinTr/Projet_de_session_techniques_apprentissage","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ColinTr/Projet_de_session_techniques_apprentissage","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ColinTr%2FProjet_de_session_techniques_apprentissage","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ColinTr%2FProjet_de_session_techniques_apprentissage/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ColinTr%2FProjet_de_session_techniques_apprentissage/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ColinTr%2FProjet_de_session_techniques_apprentissage/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ColinTr","download_url":"https://codeload.github.com/ColinTr/Projet_de_session_techniques_apprentissage/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ColinTr%2FProjet_de_session_techniques_apprentissage/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274935970,"owners_count":25376834,"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","status":"online","status_checked_at":"2025-09-13T02:00:10.085Z","response_time":70,"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":[],"created_at":"2024-12-12T04:09:23.088Z","updated_at":"2025-09-13T07:44:44.620Z","avatar_url":"https://github.com/ColinTr.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"Note : Le rapport lié au projet peut être trouvé ici : [Rapport_projet_de_session](Rapport_projet_de_session_TROISEMAINE_LEVIEUX.pdf)\n\nIFT712_Projet_de_session : Comparaison de 6 méthodes de classification de données\n==============================\n\nCe projet de session a pour but de comparer 6 classifieurs différents : AdaBoost, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Logistic Regression, Neural Networks, Perceptron, Ridge Regression, Support Vector Machines et Naive Bayes. Les points de comparaison principaux sont le type de données, le type de traitement des données requis, le temps d'exécution, la log loss et le score de test et d’entraînement. Nous passons également en revue quelques bonnes pratiques à appliquer et comparons nos résultats à d’autres personnes ayant utilisé le même ensemble de données provenant du challenge Kaggle “Leaf Classification”.\n\n***\n\n## Prérequis\nPour fonctionner, ce projet a besoin d'un certain nombre de bibliothèques python que vous trouverez dans requirements.txt\n\nPour les installer, lancez ```pip install -r requirements.txt```\n\n***\n\n## Fonctionnement\nIl est possible de lancer ce programme à partir de la commande suivante :\n\n```python\npython main.py train_data_input_filepath output_filepath classifier grid_search data_preprocessing use_pca\n\n    classifier : 0=\u003eAll, 1=\u003eNeural Networks, 2=\u003eLinear_Discriminant_Analysis, 3=\u003eLogistic_Regression, 4=Ridge, 5=\u003ePerceptron, 6=\u003eSVM, 7=\u003e AdaBoost, 8=\u003eQuadratic_Discriminant Analysis, 9=\u003eNaive_Bayes, 10=Class_grouping\n\n    grid_search : 0=\u003eno grid search, 1=\u003euse grid search\n\n    data_preprocessing : 0=\u003eraw data, 1=\u003ecentered + standard deviation normalization, 2=\u003ecentered + mean deviation normalization\n\n    use_pca : 0=\u003eno, 1=\u003eyes\n```\nExemple (Windows): ```python main.py data\\\\raw\\\\train\\\\leaf-classification-train.csv data\\\\processed 0 0 0 0```\n\nExemple (Linux): ```python main.py data/raw/train/leaf-classification-train.csv data/processed 0 0 0 0```\n\n***\n\nProject Organization\n------------\n\n    ├── README.md          \u003c- The top-level README for developers using this project.\n    │\n    ├── data\n    │   ├── processed      \u003c- The final, canonical data sets for modeling.\n    │   └── raw            \u003c- The original, immutable data dump.\n    │       ├── train      \u003c- Data used to train the model.\n    │       └── test       \u003c- Data used to test the model.\n    │\n    ├── setup.py           \u003c- makes project pip installable (pip install -e .) so src can be imported\n    │\n    ├── src                \u003c- Source code for use in this project.\n    │   ├── __init__.py    \u003c- Makes src a Python module\n    │   │\n    │   ├── data           \u003c- Scripts to download or generate data\n    │   │   ├── data_handler.py\n    │   │   └── data_preprocesser.py\n    │   │\n    │   ├── models         \u003c- Scripts to train models and then use trained models to make predictions\n    │   │   ├── adaboost_classifier.py\n    │   │   ├── base_classifier.py\n    │   │   ├── linear_discriminant_analysis.py\n    │   │   ├── logistic_regression.py\n    │   │   ├── naive_bayes.py\n    │   │   ├── neural_networks.py\n    │   │   ├── perceptron.py\n    │   │   ├── quadratic_discriminant_analysis.py\n    │   │   ├── ridge_regression.py\n    │   │   ├── super_classifier.py\n    │   │   └── support_vector_machines.py\n    │   │\n    │   ├── visualization  \u003c- Scripts to create exploratory and results oriented visualizations\n    │   │   └── visualize.py\n    │   │\n    │   └── main.py        \u003c- main script that launches everything needed to generate the results\n    │\n    └── requirements.txt   \u003c- The requirements file for reproducing the analysis environment, e.g.\n                              generated with `pip freeze \u003e requirements.txt`\n\n\n--------\n\n\u003cp\u003e\u003csmall\u003eProject based on the \u003ca target=\"_blank\" href=\"https://drivendata.github.io/cookiecutter-data-science/\"\u003ecookiecutter data science project template\u003c/a\u003e. #cookiecutterdatascience\u003c/small\u003e\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcolintr%2Fprojet_de_session_techniques_apprentissage","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcolintr%2Fprojet_de_session_techniques_apprentissage","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcolintr%2Fprojet_de_session_techniques_apprentissage/lists"}