Exploratory Data Analysis Project2
FIFA Football Data Analysis
Football just like Basketball is a worldwide well known sport activity that could be a profession for others, hobby or passion for some others. For me it's more than a passion its a part of my life. Watching and playing football use to take me more time in my daily life that somtimes i do have some remorse like i could have spent that time on learning how to code, it's true, but difficult to stay far away from it.
This Exploratory Data Analysis Project is more about exploring International Football team(countries) information on man team FIFA statistic from 1872 still now. In this work 4 main datasets containing informations about international Football matches, and FIFA registered countries are involve, In which we first going to analyse the data then process before going to answer several commonly asked questions concerning the topic.
Hope this work will give you superficial idea concerning International Football history
The dataset are downloaded from Kaggle which is one of the world's largest data science community with powerful tools and resources to help you achieve your data science goals.
dataset we use are :
This Analysis, through visualizing different trends, will give us an idea about international football history around the world and information about countries that participate to FIFA major tournaments.
international-football-results-from-1872-to-2017 dataset is made of more than 42363 rows(dataset get automatically update during competition) and 9 column.
FIFAWorldRanking have 63054 column, 9 rows where man football team rank are listed since 1992.
Country-code content 2 dataset
countrycode.xls for FIFA registered international team and
continent.xls where contry are group according to continent and regions.
fifa-host.xls dataset with 18 rows 3 columns and
fifa-team-performance.xls dataset having 78 rows 7 columns, the dataset was manually collected from wikipedia.
Analysis will be done using Python3, libraries such as jovian to save, make public and share the notebook, opendatasets to download the dataset files form
kaggle, pandas which is a software library written for the Python programming language for data manipulation and analysis, it offers data structures and operations for manipulating numerical tables and time series. libraries such as matplotlib, plotly and seaborn, will be use for data visualization. geopandas, folium will be helpful to watch a geographical representation. Finaly the ineluctable numpy which is suitable when working with array.
Download and Import libraries
Read the dataset
Selecting useful columns
Adding more detailed columns
Exploratory Analysis and visualization
(investigate on what we can get out of these datasets):
Relation between columns of the main Dataset
Top 20 teams that have highest number of international matches.
Number of FIFA major tournaments matches played by each country national team
The evolution of number teams participating in each FIFA major tounament.
Regroup Team registered in FIFA or continental federation, accordingly to region and continent.
History Rank for each country in FIFA Ranking
Statistic of top 20 teams(Win,draw,lost)
continent where most of the friendly game happened
Team best performance in FIFA World cup
Asking and Answering Questions
- which cities(top10) hosted the most international football matches ?
- Which team(top10) base on highest number of matches played,have score most goals and have less conceded in all international matches including friendly?
- What is the probability of hosting and wining a FIFA World Cup?
-what is the ratio of scored goal per total matches in each different Edition Fifa major tournament?
-Does score high number of goals means large number of victories?
- Which countries Host The FIFA World Cup from 1930 till 2022?
- What countries have already Won the FIFA World Cup in History? When and where and Which Edition?
- Which country won the last FIFA world Cup? Show their Statistic during the entired competition.
Summary and Conclusion