There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.
This is an Udacity Nanodegree project. I chose FIFA 19 complete player dataset.
As a football fan, it would be quite interesting to apply data analysis skills here.
My key interests would be:
- What's the ratio of total wages/ total potential for clubs. Which clubs are the most economical ？
- How is nation team player total market value distributed? Probably show a distribution plot in a world map?
- How is a player's skilsl set influence his potential/wage? Can we predict a player's potential based on his skills' set?
Data.csv contains lastest edition FIFA 2019 players attributes including age, potential, wage, etc.
There is also a notebook available here to showcsae all my work related to my three questions.
The main findings of the code can be found at the post available
Must give credit to Udacity courses for some of code ideas, and to kaggle/AirBnb for the data. You can find the Licensing for the data and other descriptive information at the Kaggle link available here. Otherwise, feel free to use the code here as you would like!