Global Happiness Trends Project

Project overview

This project explores what drives happiness across countries using the World Happiness Report. Analysis in Python (Kaggle) identifies patterns and correlations in economic, social, and health-related factors; an interactive Tableau dashboard then makes those insights explorable for a broad audience.

The work spans two linked deliverables: a reproducible notebook that examines how GDP, social support, life expectancy, freedom, generosity, and corruption perception relate to national happiness, and a Tableau dashboard that visualizes global and regional trends, comparisons over time, and factor-level views. Together they show how rigorous analysis can be translated into accessible visualization.

Dataset / source

World Happiness Report data (country-level happiness scores and contributing indicators), used consistently across the Python analysis and the Tableau build.

Tools used

Python, pandas, matplotlib/seaborn (in Kaggle); Tableau Public for the dashboard.

What problem you solved

Raw happiness and socio-economic data needed to be turned into clear, evidence-based answers about what drives national well-being, and those answers needed to be communicated without requiring statistical expertise—first through a documented notebook, then through an interactive dashboard for exploration and education.

Key insights

• Drivers of happiness
  Economic and social factors (e.g., GDP per capita, social support, health, freedom) show interpretable relationships with happiness scores in line with the World Happiness framework.
• Regional and temporal patterns
  Country and regional comparisons and time-based views highlight where scores cluster and how they shift across years.
• Notebook to dashboard
  Analysis steps in Python support and validate the story told in Tableau (maps, trends, factor breakdowns).
• Dashboard interactivity
  Users can filter by country, region, and year and use tooltips for detail without rebuilding the analysis.