Soccer and machine learning tutorial
How good is a certain soccer player? Let’s find out applying Machine Learning to Fifa 18!
I’m sure you’ve probably heard about the 2018 FIFA Football World Cup in Russia everywhere during the last few months. And, if you are a techy, I guess you also have realized that Machine Learning and Artificial Intelligence are buzzwords too. So, what better way to start off this 2018 than by writing a post that combines these two hot topics in a machine learning tutorial! In order to do that, I’m going to leverage a dataset of the Fifa 2018 video game.
My goal is to show you how to create a predictive model that is able to forecast how good a soccer player is based on their game statistics (using Python in a Jupyter Notebook). Fifa is one of the most well known video games around the world. You’ve probably played it at least once, right? Although I’m not a fan of video games, when I saw this dataset collected by Aman Srivastava, I immediately thought that it was great for practicing some of the basics of any Machine Learning Project. The Fifa 18 dataset was scraped from the website sofifa.com containing statistics and more than 70 attributes for each player in the Full version of FIFA 18. In this Github Project you can access the csv files that compose the dataset and some jupyter notebooks with the python code used to collect the data. Having said this, now let’s start!
Getting started with the machine learning tutorial
In our recently published Machine Learning e-book we explained most of the basic concepts related to smart systems and how machine learning techniques could add smart capabilities to many kinds of systems in almost any domain that you can imagine. Among other things, we learned that a typical workflow for a Machine Learning Project usually looks like the one shown in the image below:
In this post we’ll go through a simplified view of this whole process, with a practical implementation of each phase. The main objective is to show most of the common steps performed during any machine learning project. Therefore, you could use it as a start point in case you need to address a machine learning project from scratch.
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