A comprehensive data analytics project that explores global happiness trends from 2015 to 2017 using socio-economic indicators. This analysis helps uncover the key drivers behind happiness scores and provides insightful visualizations for data-driven storytelling.
This project analyzes the World Happiness Report data across three consecutive years (2015, 2016, and 2017). It leverages Python and data visualization libraries to:
- Understand the distribution and trends in happiness scores.
- Identify influential factors like GDP, health, trust, and social support.
- Compare performance across countries and continents.
- Highlight correlations between various indicators.
- Clean and consolidate happiness datasets from 2015–2017.
- Perform exploratory data analysis (EDA) on global happiness scores.
- Visualize patterns and insights using advanced plots.
- Compare trends year-over-year and across regions.
- Lay the foundation for future predictive modeling.
- Language: Python
- Libraries:
pandas– Data preprocessingmatplotlib,seaborn– Visualization
- Platform: Jupyter Notebook
- Data Source: World Happiness Report
- Countries with higher GDP, better health services, and social freedom tend to have higher happiness scores.
- Trust in government and generosity also show significant correlation with well-being.
- Consistent performers: Norway, Denmark, and Switzerland.
- Declining trends observed in regions facing political or economic instability.
- Expand analysis to include reports up to 2024.
- Build an interactive dashboard using Streamlit or Power BI.
- Apply machine learning models to predict happiness scores.
- Integrate geospatial mapping for location-based insights.
This project is licensed under the MIT License.
- World Happiness Report
- Community inspiration from Kaggle, GitHub, and open data contributors.