Objective

The objective of this project is to perform data analytics on FIFA (Fédération Internationale de Football Association) data to gain insights into player performance, team statistics, and overall trends in the game. The project aims to provide in-depth analysis and visualization of the FIFA dataset, enabling users to explore and understand various aspects of the game.

Development Time

This project took approximately 1 week to complete. The development time may vary depending on the complexity of the analysis, data processing, and visualization tasks involved.

Pricing

The cost for creating a similar data analytics project for FIFA or any other sports domain can vary depending on the specific requirements, data sources, and analysis scope. For pricing inquiries and project consultations, please contact us at mohammedkayser143@gmail.com

Tools Used

The FIFA Data Analytics project was built using the following tools and technologies:

• Python: A versatile programming language used for data processing, statistical analysis, and visualization.
• Jupyter Notebook: An interactive development environment used for data exploration, analysis, and code documentation.
• Pandas: A powerful data manipulation library used for data preprocessing, cleaning, and transformation.
• Matplotlib: A plotting library used for creating visualizations, such as bar charts, scatter plots, and histograms.
• Seaborn: A statistical data visualization library built on top of Matplotlib, providing enhanced visualizations and statistical analysis capabilities.

Project Ownership

This project was developed by Mohammed Kayser. It was  completed as an individual project , if needed source code or any other information feel free to contact For more information or collaboration opportunities, please feel free to reach out to us