{"id":26149215,"url":"https://github.com/antonygiomarxdev/flower_classification","last_synced_at":"2026-04-18T21:04:04.974Z","repository":{"id":267388420,"uuid":"901093811","full_name":"antonygiomarxdev/flower_classification","owner":"antonygiomarxdev","description":null,"archived":false,"fork":false,"pushed_at":"2024-12-10T03:36:37.000Z","size":337,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-11T05:32:24.961Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/antonygiomarxdev.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":"2024-12-10T03:08:32.000Z","updated_at":"2024-12-10T03:36:41.000Z","dependencies_parsed_at":"2024-12-10T04:29:30.270Z","dependency_job_id":null,"html_url":"https://github.com/antonygiomarxdev/flower_classification","commit_stats":null,"previous_names":["antonygiomarxdev/flower_classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/antonygiomarxdev/flower_classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/antonygiomarxdev%2Fflower_classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/antonygiomarxdev%2Fflower_classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/antonygiomarxdev%2Fflower_classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/antonygiomarxdev%2Fflower_classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/antonygiomarxdev","download_url":"https://codeload.github.com/antonygiomarxdev/flower_classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/antonygiomarxdev%2Fflower_classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31984558,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-18T20:23:30.271Z","status":"ssl_error","status_checked_at":"2026-04-18T20:23:29.375Z","response_time":103,"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":[],"created_at":"2025-03-11T05:29:19.396Z","updated_at":"2026-04-18T21:04:04.936Z","avatar_url":"https://github.com/antonygiomarxdev.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Clasificación de Flores Iris con scikit-learn\n\n## Descripción\n\nEste proyecto tiene como objetivo clasificar diferentes especies de flores Iris utilizando algoritmos de machine\nlearning implementados con **scikit-learn**. Analizando características como la longitud y anchura del sépalo y el\npétalo, los modelos predicen la especie de Iris con alta precisión.\n\n## Tecnologías Utilizadas\n\n- **Lenguaje de Programación**: Python\n- **Librerías**:\n    - [numpy](https://numpy.org/)\n    - [pandas](https://pandas.pydata.org/)\n    - [matplotlib](https://matplotlib.org/)\n    - [seaborn](https://seaborn.pydata.org/)\n    - [scikit-learn](https://scikit-learn.org/stable/)\n    - [jupyter](https://jupyter.org/)\n\n## Estructura del Proyecto\n\n```\nflower_classification/\n│\n├── data/\n│   └── iris.csv                  # (Opcional) Archivo CSV del dataset Iris\n├── notebooks/\n│   └── Iris_Classification.ipynb  # Notebook principal con el código del proyecto\n├── src/\n│   └── main.py                   # (Opcional) Script Python con funciones reutilizables\n├── requirements.txt              # Lista de dependencias del proyecto\n└── README.md                     # Documentación del proyecto\n```\n\n## Instrucciones\n\n### 1. Clonar el Repositorio\n\nPrimero, clona este repositorio en tu máquina local:\n\n```bash\ngit clone https://github.com/antonygiomarxdev/flower_classification.git\ncd flower_classification\n```\n\n### 2. Crear y Activar un Entorno Virtual\n\nEs recomendable utilizar un entorno virtual para gestionar las dependencias del proyecto:\n\n```bash\npython -m venv venv\n```\n\n- **En Windows**:\n\n  ```bash\n  venv\\Scripts\\activate\n  ```\n\n- **En macOS/Linux**:\n\n  ```bash\n  source venv/bin/activate\n  ```\n\n### 3. Instalar las Dependencias\n\nCon el entorno virtual activado, instala las librerías necesarias:\n\n```bash\npip install -r requirements.txt\n```\n\n### 4. Ejecutar el Notebook de Jupyter\n\nInicia Jupyter Notebook y abre el archivo principal:\n\n```bash\njupyter notebook notebooks/Iris_Classification.ipynb\n```\n\nEn la interfaz de Jupyter, navega hasta la carpeta `notebooks/` y abre `Iris_Classification.ipynb`.\n\n## Resultados\n\n### Comparación de Precisión de Modelos\n\n| Modelo              | Precisión |\n|---------------------|-----------|\n| Regresión Logística | 1.00      |\n| K-Nearest Neighbors | 1.00      |\n| Árbol de Decisión   | 0.90      |\n| Random Forest       | 1.00      |\n\n## Próximos Pasos\n\n1. **Optimización de Modelos**: Experimentar con ajuste de hiperparámetros utilizando Grid Search o Random Search.\n2. **Validación Cruzada**: Implementar técnicas de validación cruzada para evaluar la estabilidad del modelo.\n3. **Implementación de Pipelines**: Crear pipelines de scikit-learn para automatizar los pasos de preprocesamiento y\n   entrenamiento.\n4. **Despliegue del Modelo**: Aprender a desplegar el modelo como una API utilizando frameworks como Flask o FastAPI.\n5. **Versionado de Datos y Modelos**: Implementar herramientas para versionar datos y modelos, facilitando la\n   reproducibilidad.\n6. **Integración Continua**: Configurar pipelines de integración continua para automatizar pruebas y despliegues.\n\n## Contacto\n\nPara cualquier pregunta o sugerencia, no dudes en contactarme a través\nde [antonygiomarx@gmail.com](mailto:tu_email@example.com).\n\n---\n\n## Notas Adicionales\n\n- **Dataset**: Aunque **scikit-learn** facilita la carga del dataset Iris, tienes la opción de descargarlo manualmente y\n  almacenarlo en la carpeta `data/iris.csv` para mayor flexibilidad.\n\n    - **Descarga del Dataset**: Puedes obtener el dataset\n      desde [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris).\n\n    - **Carga desde CSV**:\n\n      ```python\n      import pandas as pd\n  \n      df = pd.read_csv('data/iris.csv')\n      ```\n\n- **Control de Versiones con Git**:\n\n  Asegúrate de realizar commits frecuentes con mensajes descriptivos para rastrear los cambios en tu proyecto.\n\n  ```bash\n  git add .\n  git commit -m \"Descripción de los cambios realizados\"\n  git push origin master\n  ```\n\n- **Actualización de Dependencias**:\n\n  Si añades nuevas librerías al proyecto, actualiza el archivo `requirements.txt` ejecutando:\n\n  ```bash\n  pip freeze \u003e requirements.txt\n  ```\n\n## Referencias\n\n- [Documentación de scikit-learn](https://scikit-learn.org/stable/documentation.html)\n- [Tutoriales de pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html)\n- [Guía de visualización con Seaborn](https://seaborn.pydata.org/tutorial.html)\n- [Documentación de Jupyter Notebook](https://jupyter-notebook.readthedocs.io/en/stable/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fantonygiomarxdev%2Fflower_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fantonygiomarxdev%2Fflower_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fantonygiomarxdev%2Fflower_classification/lists"}