I would like to explore the data related to the World Happiness Report through visualisations, on the following areas:

1. Is there an evolution of the Happiness Score over the period of 2015 – 2019 ?
2. Is “Economy” the most decisive factor of the Happiness Score ?
3. Is the World Bank's "Income Share" index correlated with Happiness Score ?

1. Create a database from the available data
2. Exploit the database to produce visualisations and obtain answers to our queries.

• Data: Kaggle, World Bank
• Database: MySQL
• Database editor: MySQL Workbench 8.0
• Python: Jupyter Notebook, Pandas, Pymysql, SQLAlchemy
• Data visualisation: Plotly
• Web: Flask, Heroku

• Data fetch, cleaning & merge
• Create a database from the results
• Optimize the database:
  • Optimize table data types.
  • Set up the relations between the tables, and verify the constraints.
  • Secure the database.
  • Optimize the SQL queries.

Now that the data are ready, I can try to get some directions to the questions. The steps are:

• Injecting the tables into the dataframes (Pymysql &SQLAlchemy).
• Produce the charts with Plotly.

Note: GitHub performs a static render of the notebooks and doesn't include the embedded HTML/JavaScript that makes up graphs. Please use this link to see the WHR_Exploit file with the interactive plotly charts.

• Is there an evolution of the Happiness Score over the period of 2015 – 2019 ?

  • There have been no remarkable developments over the period in question

• Is “Economy” the most decisive factor of the Happiness Score ?

  • The most decisive factors are Economy & Health

• Is the World Bank's "Income Share" index correlated with Happiness Score ?

  • The index has no correlation with any of the factors making up the Happiness Score
  • The happiest countries do not significantly redistribute their wealth to the less fortunate.