Fifa-2019-EDA

Fifa 2019 Player and League EDA

Player data: https://www.kaggle.com/karangadiya/fifa19

League information: https://sofifa.com/

The Fédération Internationale de Football Association or FIFA is a non-profit organization which describes itself as an international governing body of association football, fútsal, beach soccer, and efootball. It is the highest governing body of football. (Wikipedia) Using the player stats and data, the project intends to answer some business questions and tries to predict some interesting facts.

1. Which are the major football playing nations?
2. Which are the richest clubs in terms of spending power?
3. What is the age distribution of the players?
4. Correlation between Player’s Age, Overall potential, Value and Wage.

Machine Learning model: Can we predict the Value of a player based on its attributes (like accuracy, shot power, reactions, dribbling etc.)?

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisite

To execute this project, you will need the following applications -

• Python 3.7 and Jupyter Notebook OR Anaconda distribution
• Following Python libraries: gc, pandas_profiling, numpy, pandas, matplotlib, seaborn, sklearn

Installing

This particular project does not require any installation other than the required softwares mentioned above.

Usage

This section will be updated as the project is developed.

Project Status

Project work in progress and will be constantly updated.

Versioning

Git is used for project versioning.

Authors

Anirban Pal

Acknowledgements

• Initial idea taken from https://www.kaggle.com/sujaypandit/fifa19-player-and-league-dataset