merci93 / analysis_of_european_top_leagues

This is a data analysis project aimed at analyzing the top European Leeagues in the following selected countries: England, Germany, Poland, Belgium, Italy, Spain and lots more.

Analysis_of_European_Top_Leagues

This is a data analysis project aimed at analyzing the top European Leeagues in the following selected countries: England, Germany, Poland, Belgium, Italy, Spain and lots more. It contains data for soccer matches, players, and teams from these European countries from 2008 to 2016. The features includes but not limited to:

• Player name: shows the name of players
• Country: Countries whose leagues were listed
• League: Leagues played in the listed countries
• Clubs: Name of clubs participating in the leagues
• Penalties: Number of penalties played by a player
• Home team goal: Goals scored by the home team per match
• Away team goal: Goals scored by the away team per match . . . and lots more. The colmns are enormous to be listed here.

Contained in this database are the following csv files:

• Countries: Names of different countries whose with their ID(England, Germany, Italy, etc, the rest will be seen during analysis.),
• Leagues: The leagues of the countries listed in 1 above (English Premier League in England, Germany 1 Bundesliga in Germany, Italy Serie A in Italy, etc),
• Player: Contains players details such as name, date of birth, height and weight.
• Player_attributes: Shows the characteristics of different players in the leagues. These characteristics includes if the player is left or right footed, players overall rating, defence and attacking rate and so much more.
• Match: Gives details about matches played between 2008 and 2016 (grouped in seasons), and also the number of goals scored, either scored by the home team or the away team.
• Team: Contains selected names of clubs participating in each of the Europian leagues listed in 1, with their short names and club ID.
• Team_attributes: This file shows the different characteristics of each team in general. Their build up play pattern, team composure in general, defence mindset, chances created and passing too.

Exploratory analysis will be carried out on these provided dataset and visuals used to communicate results obtained during this process. The following research questions will be answered during the course of exploring these data to obtain insight on the Europian league:

• Which team(s) had the best build up play?
• which team(s) had the worst build up play?
• Player(s) with most penalties
• Player(s) with least number of penalties

The project is organized under the following categories:

• Introduction
• Data Wrangling
• Exploratory Data Analysis
• Conclusions

Required Packages:

• pandas
• numpy
• seaborn
• matplotlib.pyplot
• datetime