The "Goal Analytics" project aims to provide valuable insights into soccer players' performance, particularly focusing on comparing Ecuadorian players with the best in each position. The primary audience includes soccer clubs, national teams, and betting houses interested in enhancing their knowledge of the sport. This project explores a comprehensive dataset, encompassing player information, team data, and various statistics related to the 2023 soccer season.
The key focus areas of this project include:
- Comparing Ecuadorian players to the best players in each position.
- Analyzing player attributes such as Attacking, Skills, Defense, Mentality, and Goalkeeping Skills.
- Understanding player roles in both club and national teams.
- Utilizing data mining techniques for valuable insights.
- Size: Approximately 5.6 GB
- Contents: The dataset comprises over 100 columns and 200,000 rows.
- Data Categories:
- Player positions and roles in clubs and national teams.
- Player attributes, including statistics in various skill categories.
- Player personal data like Nationality, Club, Date Of Birth, Wage, Salary, etc.
- Team data, including coaches, overall value, and tactics.
In this deliverable, detailed information about the dataset and the data preprocessing steps, including cleaning, integration, and selection, are provided. These steps are crucial in achieving the final outcome of the project.
- Textbook Reference: Han, Jiawei, et al. Data Mining: Concepts and Techniques. Netherlands, Elsevier Science, 2022.
- Dataset on Kaggle
- Poetry
- NumPy Documentation
- Pandas Documentation
- Scikit-Learn Documentation
- Seaborn Documentation
- NLTK (Natural Language Toolkit) Documentation
- Matplotlib Documentation
- Plotly Documentation
- Jupyter Documentation