FIFA World Cup Analysis

Project Title - FIFA World Cup 2022

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.

Sources of Data

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

Modifications

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.

Output Data out of this analysis

1. Previous world cup winners and the number of goals scored

2. FIFA World Cup 2022 Teams

Contents of the Folders

1. bootswatch_cerulean: Javascript libraries minified files
2. css: Style sheets
3. Data: raw csvs and data cleaning pandas notebook
4. fifa_Analysis_api: flask api
5. Images: Images related to readme files
6. Maps: Map visualisation and its logic
7. static: js files
8. templates: templates
9. table_creation_scripts: scripts to create tables
10. Project_3_Presentation: Presentation file
11. Project_3_Report: Project Report

Execution of the code:

1. Create .env files with the content: db_UserName= db_Password=
2. Create tables using 'table_creation_scripts'
3. Execute the 'data_cleaning' jupyter notebook from 'Data' folder
4. Install flask-cors using 'pip install -U flask-cors'
5. Execute the 'app.py' from 'fifa_Analysis_api' folder
6. Execute 'index.html' from 'templates' folder to run the website.

Team Members

Carlos Soda, Bharat Guturi, Balvinder Rajbans

Dataset Tables - Raw & Modified

goals.csv - Raw

new_goals.csv - Modified

tournament_standings - Raw

Applications used:

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

Process:

Extract :

Open python file and Import csv files into Pandas

Transform:

Transform tables to formal specification

Data Modelling - Tables :

Create the tables using www.quickdatabase.com

Load:

Connect to postgres SQL database -> load data.

Formal specification to be created that defines the tables format can be imported into postgres SQL database.

new goals table

Visualisations:

Responsive visualisations

Project Report:

To access the detailed process of Extract, Transform,Load and visualisations follow the steps shown in the Project Report.