This project explores the World Happiness Report 2025 dataset to understand how social, economic, and personal factors contribute to happiness across nations.
Using Python libraries like pandas, numpy, matplotlib, and seaborn, we visualize relationships between GDP, social support, health, freedom, generosity, and corruption perception.
- Python 3
- pandas, numpy
- matplotlib, seaborn
- Google Colab
The dataset used is the official World Happiness Report 2025 (CSV format) containing:
- Country name
- Regional indicator
- Ladder score (Happiness Score)
- Log GDP per capita
- Social support
- Healthy life expectancy
- Freedom to make life choices
- Generosity
- Perceptions of corruption
- GDP vs. Happiness — explores how economic prosperity affects happiness.
- Regional GDP Contribution — shows GDP share by region (pie chart).
- Correlation Heatmap — displays relationships among happiness factors.
- Perception of Corruption — compares corruption perceptions across regions.
- Life Expectancy Comparison — contrasts top and bottom 10 happiest countries.
- Freedom vs. Happiness — highlights the role of freedom in well-being.
- Most & Least Corrupt Countries — identifies global corruption extremes.
- Corruption vs. Happiness — visualizes their inverse relationship.
- Happiness Score vs GDP per Capita
- Correlation Heatmap of Key Factors
- Life Expectancy of Top vs Bottom 10 Countries
- Top 10 Most & Least Corrupt Countries
- Open the notebook in Google Colab:
- Mount your Google Drive (if using drive path).
- Install required libraries:
pip install pandas numpy matplotlib seaborn
- Run all cells to reproduce the results and visualizations.
- Higher GDP, social support, and freedom strongly correlate with greater happiness.
- Corruption perception shows a negative correlation — nations with lower corruption enjoy higher happiness.
- Healthy life expectancy is a major determinant separating top and bottom-ranked countries.
This project is open source and available under the MIT License.
Ali Husnain Shah
