Exploratory Data Analysis of the FIFA 19 Dataset in Python

💡 What is Exploratory Data Analysis?

Exploratory Data Analysis refers to the fundamental process of conducting initial assessment on a dataset in order to uncover patterns, spot anomalies, test various machine learning models, and validate assumptions using statistical results and mathematical visualisations.

OUTLINE

• Download and Import libraries

• Data Preparation and Cleaning

• Selecting specific columns

• Finding and Replacing The Null Values In Our Dataset

• Adding more detailed columns

• Exploratory Analysis and visualization
  (investigate on what we can get out of these datasets):

• Wage

• Country

• Position

• Weight

• Height

• Factors effecting the players

• Asking and Answering Questions

• Who are the top 10 most paid players?

• Who are the most rated players?

• How many differrent country players are playing and which country contributes most players?

• Which are the top most played position?

• Does every position players are paid equally?

• Compare left foot and right foot players.

• Is there any age difference between different country players?

• Does age effect on wages?

• Summary and Conclusion

• Future Works

• References

Download and Import libraries