{"id":28386737,"url":"https://github.com/rociobenitez/happiness-index-data-processing","last_synced_at":"2026-05-15T23:04:24.849Z","repository":{"id":204851805,"uuid":"712810924","full_name":"rociobenitez/happiness-index-data-processing","owner":"rociobenitez","description":"Repository for Big Data Processing - Contains Jupyter Notebooks and Datasets for data analysis and processing tasks related to Big Data.","archived":false,"fork":false,"pushed_at":"2025-05-28T05:41:22.000Z","size":8615,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-07T01:05:47.172Z","etag":null,"topics":["big-data","big-data-processing","data-analysis","data-processing","happiness-index","happiness-report","jupyter-notebook","matplotlib","pandas","seaborn"],"latest_commit_sha":null,"homepage":"https://medium.com/@rociobenitez2403/explorando-el-mundo-del-big-data-4f0b55e50bfc","language":"Jupyter 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felicidad mundial, combinando técnicas de preprocesamiento, análisis exploratorio y visualización utilizando Jupyter Notebooks.\n\n**El objetivo es explorar la relación entre el índice de felicidad, el GDP per cápita, la expectativa de vida y otros indicadores a lo largo de los años**.\n\n## Objetivos del Proyecto\n\n- Explorar la felicidad mundial utilizando conjuntos de datos reales (2020-2021).\n- Comparar el índice de felicidad por país, región y año.\n- Relacionar variables socioeconómicas (GDP, expectativa de vida) con la percepción de bienestar.\n- Aplicar técnicas básicas de análisis de datos usando Python (pandas, matplotlib, seaborn).\n\n## Estructura del Repositorio\n\n```\nhappiness-index-data-processing/\n├── data/\n│   ├── world-happiness-report-2021.csv\n│   └── world-happiness-report.csv\n├── docs/\n│   └── questions-analysis.md\n├── notebooks/\n│   ├── data-analysis.ipynb\n│   └── data-visualization.ipynb\n```\n\n## Ejercicios y Análisis\n\nEl cuaderno Jupyter analiza los siguientes puntos clave:\n\n1. **País más feliz del mundo en 2021**  \n   Se determina mediante el mayor `Ladder score`.\n\n2. **País más feliz por continente**  \n   A partir del mapeo entre regiones y continentes se identifica el país líder en cada uno.\n\n3. **País que más veces ocupó el primer lugar entre 2005 y 2021**  \n   Se evalúa con varias estrategias (`idxmax()`, `apply()`, rankings).\n\n4. **Relación entre GDP y felicidad**  \n   Se analiza la posición en el ranking de felicidad del país con mayor GDP en 2020.\n\n5. **Variación del GDP promedio mundial entre 2020 y 2021**  \n   Se calcula la variación porcentual y se analizan sus implicaciones.\n\n6. **País con mayor expectativa de vida**  \n   Se compara el indicador en los años 2019, 2020 y 2021.\n\n\u003e Ver detalles completos en [`docs/questions-analysis.md`](docs/questions-analysis.md)\n\n## Uso del repositorio\n\n1. Clona o descarga este repositorio:\n\n```bash\ngit clone git@github.com:rociobenitez/happiness-index-data-processing.git\ncd happiness-index-data-processing\n```\n\n2. Instala las dependencias:\n\n```bash\npip install -r requirements.txt\n```\n\n3. Abre los notebooks utilizando [Jupyter Notebook](https://jupyter.org/) o [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/).\n\n4. Explora y analiza los datos ejecutando los cuadernos disponibles, especialmente `data-visualization.ipynb` para visualizaciones y `questions-analysis.ipynb` para insights.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frociobenitez%2Fhappiness-index-data-processing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frociobenitez%2Fhappiness-index-data-processing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frociobenitez%2Fhappiness-index-data-processing/lists"}