FIFA-player's-value-analysis

Introduction

For all the FIFA players, they have their own values which have been decided by wage, skill, body type, position, and country. In this project, I am going to utilize data mining to analyze how their value has been built and which feature is the most important to their value.

Data Source

Dataset (.csv files) came from Kaggle: FIFA 19 complete player dataset: https://www.kaggle.com/karangadiya/fifa19

FIFA World Cup: https://www.kaggle.com/abecklas/fifa-world-cup

Requirements

• Python 2.7/3.7
• Seaborn
• Numpy 1.17.2
• Pandas
• matplotlib

Conclusion

1. The demographic factors do not effect the players’ value much.
2. The peak of a player’s wage is 3 years later than the peak of value.
3. The reactions in the skill factors is the most influential factors to a player’s value.
4. The right foot preferred players are much more than left foot preferred, but the values do not have big differences.
5. The country which has the higher rank does have the higher point at each capacity.