Welcome to the Football League Analysis project! This repository contains the code and documentation for analyzing a dataset sourced from Kaggle, focusing on the world's major football leagues. The project utilizes Python, primarily relying on Pandas and NumPy for data preparation and analysis, and Matplotlib and Seaborn for creating insightful visualizations.
Football is a passion shared globally, and understanding the dynamics of major leagues can provide valuable insights. This project aims to perform a comprehensive analysis using Python tools to unravel interesting patterns and trends within the football dataset.
The dataset, obtained from Kaggle, includes detailed information about player details such as nation, wage, position and age.
The analysis involves data cleaning, exploration, and visualization using Pandas and NumPy for data manipulation and Matplotlib along with Seaborn for creating insightful charts and graphs. We delve into key aspects such as correlation between age and salary, team best payer and countries whose export most players.
To explore the analysis, follow these steps:
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Clone the repository:
git clone https://github.com/campospluiza/Football-League-Analysis.git
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Navigate to the project directory:
cd Football-League-Analysis -
Install Jupyter and nbconvert (if not installed):
pip install jupyter nbconvert
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Convert the Jupyter Notebook to a Python script:
jupyter nbconvert --to script MainAnalysis.ipynb
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Run the generated Python script:
python MainAnalysis.py
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Explore the generated visualizations and insights.
Contributions are encouraged! Whether it's fixing a bug, enhancing visualizations, or suggesting new analyses, feel free to contribute.
This project is licensed under the MIT License.