{"id":27936075,"url":"https://github.com/codyadam/datachallenge2023","last_synced_at":"2025-10-06T20:33:12.986Z","repository":{"id":67511481,"uuid":"591439014","full_name":"CodyAdam/datachallenge2023","owner":"CodyAdam","description":"Our AI solution for the Data Challenge 2023, An optimisation programming challenge on 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Data Challenge 2023 Winner\n\n[data-challenge.webm](https://github.com/CodyAdam/datachallenge2023/assets/60227150/e925bf78-98f6-49c1-8689-ac18846024b6)\n\n**Première place** sur 18 équipes au Data Challenge 2023.\n\n**L'Équipe** :\n- Adam Cody\n- Aussant Antoine \n- Delapart Thomas\n- Delisle Juliette\n- Larmet-Demenay Gwendal \n\n\n![Price](documentation/img/pres.jpg)\n![Price](documentation/img/working.jpg)\n![Photo](documentation/img/win.jpg)\n\n# Data Challenge 2023\n\nLe Master Mathématiques Appliquées, Statistique (Universités de Rennes 1 et Rennes 2), le Master Monnaie, Banque, Finance Assurance (Université de Rennes 1), TAC ECONOMICS et l'association Rennes Data Science, organisent un data challenge les 20 et 21 janvier 2023 à la Faculté des Sciences Economiques de Rennes.\n\n# Le problème\n\nVoir les 2 sujets du data challenge [ici](documentation/challenge/documentation.pdf).\n\nNous avons choisi le sujet suivant : **Optimisation du positionnement des sites Enedis en Bretagne**.\n\n# Notre solution\n\n![Solution](data/img/b40_gen_200.png)\n\nAnimation des générations de notre algorithme génétique :\n\n![SolutionGif](documentation/img/40b.gif)\n\n### Détail de notre solution [ici (`documentation/rapport_technique`)](documentation/rapport_technique.md).\n\n### Structure du projet\n\n```\n📦datachallenge2023\n ┣ 📂data\n ┃ ┣ 📂bzh_shapefile                        // Shapefile de la Bretagne\n ┃ ┣ 📂img                                  // Plots Saved\n ┃ ┣ 📜communes_bre.csv\n ┃ ┣ 📜niveau_interventions.csv\n ┃ ┣ 📜niveau_interventions_improved.csv\n ┃ ┣ 📜temps_trajet30.csv\n ┃ ┗ 📜temps_trajet30_filtered.csv\n ┣ 📂documentation                          \n ┣ 📂src\n ┃ ┣ 📜data challenge_doc final.R           // Script R prétraitement\n ┃ ┣ 📜main.py                              // Programme principal\n ┃ ┣ 📜parse.py                             // Fonctions de traitement\n ┃ ┣ 📜genetic.py                           // Algorithme génétique\n ┃ ┗ 📜utils.py                             // Fonctions utilitaires\n ┣ 📜.gitignore\n ┣ 📜README.md\n ┗ 📜requirements.txt                       // packages Pythons nécessaires\n ```\n\n# Installation et utilisation\n\n## Prérequis\n\n- Python 3.X\n- Pip\n\nPour installer les packages nécessaires, exécuter la commande suivante :\n\n```bash\npip install -r requirements.txt\n```\n\n## Utilisation\n\nPour lancer le programme, exécuter la commande suivante :\n\n```bash\npython main.py\n```\n\n\n\n# Les sponsors et partenaires\n\n#### Un grand merci aux sponsors de l'événement\n\n\n\n\u003ca href=\"https://www.enedis.fr\" target=\"_blank\"\u003e\u003cimg src=\"documentation/img/logo_enedis.png\" width=\"100\"\u003e\u003c/a\u003e \u0026nbsp;\u0026nbsp; \u003ca href=\"https://www.groupama.fr/\" target=\"_blank\"\u003e\u003cimg src=\"documentation/img/Groupama_FB_RVB.jpg\" width=\"100\"\u003e\u003c/a\u003e \u0026nbsp;\u0026nbsp; \u003ca href=\"https://fondation.univ-rennes.fr/\" target=\"_blank\"\u003e\u003cimg src=\"documentation/img/logo-Fondation-Rennes1-couleur-nobaseline.png\" width=\"100\"\u003e\u003c/a\u003e\n\n#### Ainsi qu'aux organisateurs et partenaires\n\n\u003ca href=\"https://eco.univ-rennes.fr/amsr\" target=\"_blank\"\u003e\u003cimg src=\"documentation/img/logo_amsr.jpg\" width=\"100\"\u003e\u003c/a\u003e \u0026nbsp;\u0026nbsp; \u003ca href=\"https://eco.univ-rennes.fr/aerief\" target=\"_blank\"\u003e\u003cimg src=\"documentation/img/logo_aerief.jpg\" width=\"100\"\u003e\u003c/a\u003e \u0026nbsp;\u0026nbsp; \u003ca href=\"https://www.univ-rennes.fr/\" target=\"_blank\"\u003e\u003cimg src=\"documentation/img/UNIRENNES_LOGOnoir_0.png\" width=\"100\"\u003e\u003c/a\u003e \u0026nbsp;\u0026nbsp; \u003ca href=\"https://taceconomics.com\" target=\"_blank\"\u003e\u003cimg src=\"documentation/img/taceconomics-100px-white.png\" width=\"50\"\u003e\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodyadam%2Fdatachallenge2023","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodyadam%2Fdatachallenge2023","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodyadam%2Fdatachallenge2023/lists"}