This kernel uses Electronic Arts' Fifa 18 Player Statistics dataset to predict any player's Transfer Market Value as well as his Wage.
The first part of the kernel is an Exploratory Data Analysis looking at the correlation between the characteristics of a player and its value on the transfer market and its salary. Through this Exploratory Data Analysis, I have used graphing libraries (matplotlib, seaborn and plotly) to vizualise Player value per country, Player value versus his age etc. Head over to Kaggle to see the Plotly graphs within the Notebook.
The second part is a Machine Learning Model designed to predict player value and wage based on the analysed characteristics. I used a Binary Tree. To determine the optimal hyperparameters for the library XGBoost, I used a Grid Search. After training the model on the training set, I measured its accuracy on the testing set and compared it with a Baseline Mean Average Error.