{"id":22204823,"url":"https://github.com/orliluq/inmersion-datos-python","last_synced_at":"2026-04-02T18:52:09.567Z","repository":{"id":264073893,"uuid":"892284423","full_name":"Orliluq/Inmersion-Datos-Python","owner":"Orliluq","description":"Desarrollar modelos de machine learning para predecir la probabilidad de incumplimiento crediticio de los clientes, utilizando diferentes algoritmos de clasificación (Regresión Logística, Árboles de Decisión, Random Forest, Naive Bayes). ","archived":false,"fork":false,"pushed_at":"2024-11-21T23:13:02.000Z","size":1488,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-30T04:14:17.265Z","etag":null,"topics":["colab-notebook","numpy","pandas","python","scikit-learn"],"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/Orliluq.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":"2024-11-21T20:39:30.000Z","updated_at":"2024-11-21T23:13:06.000Z","dependencies_parsed_at":"2024-11-21T21:40:31.389Z","dependency_job_id":null,"html_url":"https://github.com/Orliluq/Inmersion-Datos-Python","commit_stats":null,"previous_names":["orliluq/inmersion-datos-python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Orliluq%2FInmersion-Datos-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Orliluq%2FInmersion-Datos-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Orliluq%2FInmersion-Datos-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Orliluq%2FInmersion-Datos-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Orliluq","download_url":"https://codeload.github.com/Orliluq/Inmersion-Datos-Python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245388760,"owners_count":20607163,"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":["colab-notebook","numpy","pandas","python","scikit-learn"],"created_at":"2024-12-02T17:20:06.011Z","updated_at":"2025-12-30T21:40:11.301Z","avatar_url":"https://github.com/Orliluq.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📚 Inmersión de Datos con Python\n![Python](https://img.shields.io/badge/Python-FFD43B?style=for-the-badge\u0026logo=python\u0026logoColor=blue)\n![Scikit-learn](https://img.shields.io/badge/scikit_learn-F7931E?style=for-the-badge\u0026logo=scikit-learn\u0026logoColor=white)\n![Pandas](https://img.shields.io/badge/Pandas-2C2D72?style=for-the-badge\u0026logo=pandas\u0026logoColor=white)\n![Numpy](https://img.shields.io/badge/Numpy-777BB4?style=for-the-badge\u0026logo=numpy\u0026logoColor=white)\n\n## 🏛️ Análisis del Crédito Bancario Alemán\nEste proyecto realiza un análisis exploratorio y visualización de datos relacionados con un caso de crédito bancario alemán. Su propósito es entender mejor las características de los clientes y las variables que afectan la decisión crediticia.\n\n## 📋 Objetivos\n- Explorar los datos del conjunto proporcionado.\n- Identificar patrones y relaciones clave entre las variables.\n- Generar visualizaciones para apoyar la toma de decisiones.\n\n## 📊 Contenido del Proyecto\nEl análisis está estructurado en las siguientes secciones:\n\n1. **Carga de Datos**: Importación y preprocesamiento del conjunto de datos.\n2. **Análisis Exploratorio (EDA)**: \n   - Estadísticas descriptivas.\n   - Inspección de valores faltantes y datos categóricos.\n3. **Visualización de Datos**:\n   - Gráficos que muestran la distribución de las variables.\n   - Comparaciones entre clientes con crédito aprobado y denegado.\n4. **Conclusiones**:\n   - Insights obtenidos del análisis de los datos.\n\n## 📈 Requisitos\nEste proyecto utiliza Python y las siguientes bibliotecas:\n- `pandas`\n- `numpy`\n- `matplotlib`\n- `seaborn`\n- `scikit-learn` (opcional para tareas adicionales)\n\n## 💱 Uso\n1. Abre el archivo en Google Colab:\n   - Sube el archivo `credito_banco_aleman_inmersion_Dia1.ipynb` directamente a Google Colab.\n   - O haz clic en el botón a continuación si lo configuras en un repositorio público:\n     \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/alura-es-cursos/Inmersion-en-Datos-con-Python/blob/aula01/credito_banco_aleman_inmersion_Dia1.ipynb\"\u003e\n       \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n    \u003c/a\u003e\n\n2. Instala los requisitos si es necesario:\n   ```\n   !pip install pandas numpy matplotlib seaborn scikit-learn\n\n3. Ejecuta las celdas en el orden indicado.\n\n## 📝 Notas Adicionales\nEste análisis está basado en un conjunto de datos ficticio representativo del sector financiero. Los resultados y visualizaciones pueden utilizarse para comprender mejor el riesgo crediticio.\n\n## 👩‍💻 Autor\n\u003ca target=\"_blank\" href=\"https://www.linkedin.com/in/orlibetdungonzalez\"\u003e\n   \u003cimg src=\"https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white()\"/\u003e\n\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Forliluq%2Finmersion-datos-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Forliluq%2Finmersion-datos-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Forliluq%2Finmersion-datos-python/lists"}