FIFA19 Players Data Analysis

FIFA19 is the official football game of EA Sports. Queries in the following project are based on the Player Dataset of FIFA19. This Dataset is available in Kaggle which is a hub of datasets. This dataset consists of details of players and their stats in the year 2019. This can be used to determine success ratio, ratings, top players etc.

In this project I have used Numpy, Pandas, Matplotlib and Seaborn. This project is part of the course Data Analysis with Python: Zero to Pandas.

Table of Contents

• How to run the code
• Downloading the Dataset
• Data Preparation and Cleaning
• Determining the number of attributes in the dataset
• Cleaning the dataset
• Exploratory Analysis and Visualization
• Heatmap representing relation between the properties of attributes of football players
• Nation wise list of players contributing to football
• Comparison of positions to number of players
• Preferred foot of players
• Player's Overall (Rating) Distribution
• Asking and Answering Questions
• What are the different nations that are a part of FIFA?
• What is the highest number of players for a single position?
• What is the dominant foot of players?
• What is the age distribution in FIFA19?
• Which club has the highest Overall Rating?
• Inferences and Conclusion

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally

1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade

3. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:

jovian clone notebook-owner/notebook-id

4. Enter the newly created directory using cd directory-name and start the Jupyter notebook.

jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

1. Downloading the Dataset

Datasets can be downloaded within Jupyter using the opendatasets Python Library.