Welcome to my BPL Data Analysis/Data Visualization/ML Modeling Project! This project is a comprehensive exploration of football match data, aimed at gaining valuable insights into the beautiful game. Leveraging a rich dataset containing match statistics, scores, attendance, and other details, I employ a variety of data analysis, visualization, and machine learning techniques to uncover hidden patterns and trends.
The primary objectives of this project are as follows:
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Exploratory Analysis: Conduct exploratory data analysis (EDA) to understand the characteristics of football matches, including match outcomes, goal distribution, and attendance trends.
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Predictive Modeling: Build machine learning models to predict match outcomes and attendance based on historical data, providing valuable insights for teams, venues, and fans.
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Data Cleaning: Addressed missing values, ensuring that my analyses was based on complete and accurate information. Identifying and handling outliers to prevent them from skewing my results. Data preprocessing techniques were applied to maintain the integrity of my analyses of the data.
Our dataset encompasses a wide range of information related to football matches, including but not limited to:
- Match date and time
- Teams and referees
- Expected goals (xG) for home and away teams
- Final scores
- Attendance figures
- Venue details
This rich dataset serves as the foundation for our analyses and modeling efforts.
To replicate my analyses and run this project, you need the following Python libraries and packages:
pandasnumpymatplotlibseabornscikit-learnplotly.expressscipy.stats(for sentiment analysis)statsmodels.api(for graph analysis)
You can install these libraries using pip:
pip install pandas .... + as many as you need to downlaodThis project is licensed under the MIT License.
- Acknowledgments and credits, if applicable.
Feel free to provide feedback, report issues, or contribute to this project by submitting pull requests. Your contributions are valuable and help improve the project.