{"id":26500007,"url":"https://github.com/paudefclasspy/analisis-de-datos-logisticos","last_synced_at":"2026-05-11T02:40:49.838Z","repository":{"id":277554673,"uuid":"932794978","full_name":"paudefclasspy/analisis-de-datos-logisticos","owner":"paudefclasspy","description":"Proyecto que analiza datos de entregas a domicilio, como tiempos de entrega, eficiencia y costos.","archived":false,"fork":false,"pushed_at":"2025-03-06T10:39:19.000Z","size":11,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-06T11:33:53.730Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/paudefclasspy.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":"2025-02-14T14:35:49.000Z","updated_at":"2025-03-06T10:39:22.000Z","dependencies_parsed_at":"2025-02-21T16:25:45.918Z","dependency_job_id":null,"html_url":"https://github.com/paudefclasspy/analisis-de-datos-logisticos","commit_stats":null,"previous_names":["paudefclasspy/analisis-de-datos-logisticos"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paudefclasspy%2Fanalisis-de-datos-logisticos","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paudefclasspy%2Fanalisis-de-datos-logisticos/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paudefclasspy%2Fanalisis-de-datos-logisticos/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paudefclasspy%2Fanalisis-de-datos-logisticos/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/paudefclasspy","download_url":"https://codeload.github.com/paudefclasspy/analisis-de-datos-logisticos/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244637098,"owners_count":20485446,"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":[],"created_at":"2025-03-20T15:20:08.834Z","updated_at":"2026-05-11T02:40:44.817Z","avatar_url":"https://github.com/paudefclasspy.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📊 Análisis de Datos Logísticos - Tiempos de Entrega, Eficiencia y Costos\n\nEste proyecto tiene como objetivo analizar datos de entregas a domicilio, evaluando tiempos de entrega, eficiencia y costos. El análisis se lleva a cabo utilizando técnicas estadísticas y visualizaciones para identificar áreas de mejora en la logística de entregas. El proyecto se ha desarrollado en **Python** utilizando bibliotecas como **Pandas**, **NumPy** y **Plotly**.\n\n---\n\n## 🚀 Características principales\n\n- **Análisis de tiempos de entrega**: Estudio detallado de los tiempos promedio de entrega y factores que los afectan.\n- **Eficiencia en la logística**: Cálculo de la eficiencia operativa mediante el análisis de la relación entre los recursos utilizados y los resultados obtenidos.\n- **Costos de entrega**: Análisis de los costos asociados a las entregas, identificando patrones y oportunidades de ahorro.\n- **Visualizaciones interactivas**: Gráficos interactivos utilizando Plotly para representar datos clave y resultados del análisis.\n- **Identificación de áreas de mejora**: Uso de estadísticas y análisis para señalar posibles mejoras en los procesos logísticos de entrega.\n\n---\n\n## 🛠️ Tecnologías utilizadas\n\n- **Python**  \n- **Pandas** - Análisis y manipulación de datos.  \n- **NumPy** - Cálculos matemáticos y estadísticas.  \n- **Plotly** - Visualización interactiva de datos.\n- **Scikit-learn**\n\n---\n\n## 📥 Instalación\n\n1️⃣ Clona este repositorio:\n```\ngit clone https://github.com/tuusuario/analisis-datos-logisticos.git\ncd analisis-datos-logisticos\n```\n2️⃣ Crea un entorno virtual (opcional, pero recomendado):\n\n```\npython -m venv venv\nsource venv/bin/activate  # En Windows: venv\\Scripts\\activate\n```\n\n3️⃣ Instala las dependencias:\n\n```\npip install -r requirements.txt\n```\n\n4️⃣ Ejecuta el script para empezar el análisis:\n\n```\npython entregas.py\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaudefclasspy%2Fanalisis-de-datos-logisticos","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpaudefclasspy%2Fanalisis-de-datos-logisticos","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaudefclasspy%2Fanalisis-de-datos-logisticos/lists"}