In this project we look into the history of FIFA World cup to gather and store the data to study and perform various types of data visualisation. Visualisation screens are developed along with responsive visualisation.
The data is sourced from ("Joshua C. Fjelstul, Ph.D."), a notice that the database is copyrighted ("© 2022 Joshua C. Fjelstul, Ph.D."), a link to the CC-BY-SA 4.0 license (https://creativecommons.org/licenses/by-sa/4.0/legalcode), and a link to this repository (https://www.github.com/jfjelstul/worldcup)
No | Source | Link | File |
---|---|---|---|
1 | Git Hub | https://github.com/jfjelstul/worldcup/tree/master/data-csv | goals.csv |
2 | Git Hub | https://github.com/jfjelstul/worldcup/tree/master/data-csv | tournament_standings.csv |
3 | The Sporting News | https://www.sportingnews.com/uk/soccer/news/fifa-world-cup-which-teams-have-qualified/86nbyru9dkh41ii7s800gjwav |
The csv file goals.csv was modified to the requirements of the project and the following columns were dropped: 'match_id','stage_name','group_name','shirt_number','player_team_id','player_team_code','minute_label','minute_stoppage','own_goal','penalty'
The csv file tournament_standings.csv was NOT modified as it met the requirements of the project.
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Previous world cup winners and the number of goals scored
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FIFA World Cup 2022 Teams
- bootswatch_cerulean: Javascript libraries minified files
- css: Style sheets
- Data: raw csvs and data cleaning pandas notebook
- fifa_Analysis_api: flask api
- Images: Images related to readme files
- Maps: Map visualisation and its logic
- static: js files
- templates: templates
- table_creation_scripts: scripts to create tables
- Project_3_Presentation: Presentation file
- Project_3_Report: Project Report
- Create .env files with the content: db_UserName= db_Password=
- Create tables using 'table_creation_scripts'
- Execute the 'data_cleaning' jupyter notebook from 'Data' folder
- Install flask-cors using 'pip install -U flask-cors'
- Execute the 'app.py' from 'fifa_Analysis_api' folder
- Execute 'index.html' from 'templates' folder to run the website.
Carlos Soda, Bharat Guturi, Balvinder Rajbans
Python - Libraries: Pandas, SQLAlchemy
Jupyter Notebook
Database - PGAdmin (PostgresSQL)
Java - Libraries: d3, plotly, leaflet, chart
Installation of Flask Cors is required - Use pip install -U flask-cors
Open python file and Import csv files into Pandas
Transform tables to formal specification
Create the tables using www.quickdatabase.com
Connect to postgres SQL database -> load data.
Formal specification to be created that defines the tables format can be imported into postgres SQL database.
To access the detailed process of Extract, Transform,Load and visualisations follow the steps shown in the Project Report.