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El sistema consta de una API hecha con Java para manipular data cruda de, principalmente, jugadores. Utilizo Python para esto, perfilando y tratando de predecir el comportamiento de jugadores. \n\n## Tecnologías utilizadas\n\n|                      |                                     |\n| -------------------- | ----------------------------------- |\n| **Backend/API**      | Java - Spring Boot, Hibernate, JPA  |\n| **Base de datos**    | PostgreSQL con Docker               |\n| **Machine learning** | Python - Pandas, Scikit-learn       |\n| **Visualización**    | Plotly, Kaleido.                    |\n| **Infra**            | Docker                              |\n\n## Prerequisitos\n\nPara ejecutar el proyecto es necesario tener preinstalado lo siguiente:\n- [Java (JDK)](https://openjdk.org/)\n- [Maven](https://maven.apache.org/)\n- [Docker](https://www.docker.com/)\n\n### 1. Levantar el proyecto\n\nEjecutar en la raiz:\n\n```bash\nmake infra-up\n```\n\n### 2. Levantar la API\nEjecutar en la raiz:\n\n```bash\nmake api-run\n```\n\n### 3.\nPara ejecutar algunos de los análisis de datos, podés ver los disponibles en la carpeta [analisis_py](https://github.com/blatth/EPL2425/tree/master/analisis_py) y ejecutar en la raiz:\n\n```bash\nmake *nombre del analisis*\n```\n\nCada código genera una imagen estática y un .html interactivo para poder ver los gráficos con mayor detalle.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblatth%2Fepl2425","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblatth%2Fepl2425","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblatth%2Fepl2425/lists"}