A comprehensive data analysis project exploring the correlation between GDP and air transport passenger numbers across multiple countries, leveraging statistical modeling and time series analysis to forecast future trends and provide actionable insights for policy makers.
- Strong Correlation: 75.8% variance explanation in US air passenger numbers through GDP
- Market Variations: 48.5% variance explanation in Nigerian market
- Critical Events Impact: Quantified effects of 2008 financial crisis and COVID-19
- Cross-Market Analysis: Comparative study across developed and developing economies
- Time Series Analysis: Advanced forecasting models for GDP and passenger numbers
- Statistical Modeling: Regression analysis with R-squared evaluation
- Data Processing:
- Historical data cleaning and normalization
- Cross-country data harmonization
- Outlier detection and handling
- Visualization: Dynamic time series scatter plots and prediction charts
| Country | R-squared | Key Findings |
|---|---|---|
| USA | 75.8% | Strong GDP-passenger correlation |
| Nigeria | 48.5% | Moderate correlation with external factors |
- 2008 Financial Crisis: Quantified temporary decline and recovery patterns
- COVID-19: Measured sharp decline in 2020 passenger numbers
- Economic Indicators: Identified key GDP-passenger relationship patterns
- Programming: Python
- Libraries:
- Pandas (Data Analysis)
- NumPy (Numerical Computing)
- Matplotlib/Seaborn (Visualization)
- Scikit-learn (Statistical Modeling)
- World Bank Economic Indicators
- Global Air Transport Statistics
- Historical GDP Data (1980-2020)
- Economic Impact: Demonstrated strong correlation between GDP and air travel
- Policy Implications: Developed forecasting tools for infrastructure planning
- Market Insights: Identified key differences between developed and developing markets
Feel free to reach out for collaboration or inquiries!
Data sourced from World Bank Indicators