Quick Summary

The purpose of this analysis is to explore the players from the top European competition in order to find useful information that could be used to compare, recommend or evaluate a player.

We have considered players who play in: Serie A (Italy), Ligue 1 (France), Premier League (England), La Liga (Spain). The datasets were taken from https://fbref.com/en/.

The project is organized in two Jupyter notebook in which are done different type of analysis:

- Clustering_portieri: In this notebook are analyzed the goalkeeper, with the aim of:

1. recognize the different types of goalkeepers on the basis of statistics

2. gather goalkeepers based on their characteristics

3. compare the characteristics of two or more goalkeepers

4. recommend a new goalkeeper to a team

5. evaluate and identify the general change in the characteristics of the goalkeepers over time

- AnalisiComplementare: In this notebook are analyzed all the outfield players with the following purpose:

1. recognize players who could perform better in a role other than the one assigned to them
2. compare the statistics of different role

Models used

1. Kmeans
2. Agglomerative Clustering
3. Decision Tree classifier
4. Principal Component Analysis

Libraries used

1. NumPy
2. Pandas
3. Plotly
4. Sklearn
5. Matplotlib