This project is part of the deliverables for Ironhack's Data Analytics Bootcamp. The primary objectives are to consolidate Python and data visualization skills, perform hypothesis testing, and optionally explore machine learning techniques.

FIFA 2023 is a football simulation video game developed by EA Sports, renowned for its unparalleled statistical realism. Designed as a sophisticated simulation of real-world football, FIFA 2023 leverages statistics rooted in actual player performances, team dynamics, and league data, making it one of the most authentic representations of the sport.

In this project, you'll find an in-depth analysis of the FIFA 2023 dataset. The goal is to uncover patterns related player's values, statistics, salary differences release clauses, correlations within metrics, and addressing questions such as: "Is a player's value in € related to the position they play?"

The dataset used for this analysis can be downloaded from the following link: FIFA 2023 dataset.

• Size: The original dataset consists of 18,539 rows and 89 columns (71 numeric columns and 18 categorical).
• Data Cleaning: After initial preprocessing, the dataset was streamlined to 26 columns to focus on the most relevant attributes for the analysis.

After cleaning and preparing the data in a Python notebook, I created a Tableau dashboard to provide insights into the players' values in €. The analysis focuses on differences across various positions, identifies the teams with the highest release clauses, and examines player ratings in relation to their positions and values in €.

Presentation 🎬

• Follow the project through the Google Slides