bundesliga-analysis/
├── data/                    # Datasets organized by club
│   ├── bayern/              # Specific datasets for FC Bayern Munich
│   ├── dortmund/            # Specific datasets for Borussia Dortmund
│   ├── leverkusen/          # Specific datasets for Bayer Leverkusen
│   ├── stuttgart/           # Specific datasets for VfB Stuttgart
│   └── leipzig/             # Specific datasets for RB Leipzig
│
├── notebooks/               # Jupyter notebooks for Python analysis
│   ├── Bayern.ipynb         # Analysis for Bayern Munich
│   ├── Dortmund.ipynb       # Analysis for Dortmund
│   ├── Leverkusen.ipynb     # Analysis for Leverkusen
│   ├── Stuttgart.ipynb      # Analysis for Stuttgart
│   └── Leipzig.ipynb        # Analysis for Leipzig
│
├── src/                     # Source code for Python utilities and SQL scripts
│   ├── analysis_utils.py    # Python utilities
│   └── sql_queries.sql      # SQL script files
│
├── R/                       # R scripts for statistical analysis
│   ├── Bayern.R             # R analysis for Bayern Munich
│   ├── Dortmund.R           # R analysis for Dortmund
│   ├── Leverkusen.R         # R analysis for Leverkusen
│   ├── Stuttgart.R          # R analysis for Stuttgart
│   └── Leipzig.R            # R analysis for Leipzig
│
├── outputs/                 # Generated reports, figures, and models
│   ├── bayern/              # Outputs for Bayern Munich
│   ├── dortmund/            # Outputs for Dortmund
│   ├── leverkusen/          # Outputs for Leverkusen
│   ├── stuttgart/           # Outputs for Stuttgart
│   └── leipzig/             # Outputs for Leipzig
│
└── README.md                # Overview and documentation


Getting Started
Prerequisites
Ensure you have the following installed:

Python: Along with libraries like pandas, numpy, matplotlib, seaborn, plotly, and scipy.
R: Along with packages like ggplot2, dplyr, and tidyr.
Database Access: Setup to run SQL queries, with access to a SQL server or a local database setup like SQLite.
Installation
Clone the repository to your local machine:

git clone https://github.com/itzmore-mph/itzmore-mph-portfolio.git
cd bundesliga-analysis

Running the Analyses
Python
Navigate to the notebooks/ directory and run the Jupyter Notebooks:

jupyter notebook Portfolio-Project_itzmore-mph_Performance-Analysis_Top-5-Clubs_Bundesliga_Germany_Bayer-04-Leverkusen.ipynb
jupyter notebook Portfolio-Project_itzmore-mph_Performance-Analysis_Top-5-Clubs_Bundesliga_Germany_FC-Bayern-Munich.ipynb
jupyter notebook Portfolio-Project_itzmore-mph_Performance-Analysis_Top-5-Clubs_Bundesliga_Germany_VfB-Stuttgart.ipynb
jupyter notebook Portfolio-Project_itzmore-mph_Performance-Analysis_Top-5-Clubs_Bundesliga_Germany_Borussia-Dortmund.ipynb
jupyter notebook Portfolio-Project_itzmore-mph_Performance-Analysis_Top-5-Clubs_Bundesliga_Germany_RB-Leipzig.ipynb

Repeat for other clubs as needed.

SQL
Ensure your database connection details are configured correctly in the SQL scripts or within the Python notebooks where SQL queries are executed.

R
Navigate to the R/ directory and run the R markdown for Expected Goal (xG) Analysis:

Rmarkdown Top5_Bundesliga-Germany_xG_Analysis.Rmd



Data Sources
Data was sourced from official Transfermarkt.de websites, Kaggle datasets, and also other open Github Projects. Specific data files and their sources are listed within each notebook and script.

Contributing
Contributions are welcome. Please fork the repository and submit a pull request with your changes.

License
This project is licensed under the Apache License - see the LICENSE.md file for details.

Contact
For queries or collaboration, please open an issue in the GitHub repository.