This project analyzes global happiness data using the United Nations World Happiness Report (2008–2018). It examines socio-economic factors such as Life Ladder, GDP per capita, social support, and perceptions of corruption to assess their impact on happiness levels across 165 countries.
- Top and Bottom Rankings: Identifies countries with the highest and lowest happiness scores.
- Correlation Analysis: Explores relationships between happiness and factors like GDP, social support, and corruption.
- Income Inequality: Calculates the Gini Index for 2018 to assess wealth disparity.
- Country Comparisons: Compares key factors for Germany, China, and Hong Kong.
- Visualizations: Includes scatter plots, bar charts, and box plots for data insights.
- Programming Language: Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn
- Clone the repository:
git clone https://github.com/Aundrila/World-Happiness-Analysis.git- Install required Python libraries:
pip install pandas numpy matplotlib seaborn- Run the analysis script using Jupyter Notebook or a Python IDE.
The analysis reveals factors like GDP per capita, social support, and perceptions of corruption as major influencers of happiness levels. Countries like Denmark and Finland rank highest, while South Sudan and Togo score the lowest. Gini Index highlights economic disparities.
- Aundrila Acharjee
TU Dortmund