Ca Rtola

Abstract

1. Introduction

Entre no nosso [servidor do Discord][discord] para trocar experiências sobre projetos e do uso de estatísticas no Cartola FC.

Você encontra os dados raw do Cartola FC desde 2014 na pasta [data/01_raw][folder_data].

2. Objective

Extração de dados da API do CartolaFC, análise exploratória dos dados e modelos preditivos em R e Python - 2014-22. [EN] Data munging, analysis and modeling of CartolaFC - the most popular fantasy football game in Brazil. Data cover years 2014-23.

This project demonstrates how HTML, Python, Jupyter Notebook can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• A modern web browser
• VS Code or any code editor
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/henriquepgomide/caRtola.git
cd caRtola

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by henriquepgomide and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.