Project Overview
The project merges local CSV sources, builds a country-level dataset, and tests how climate variables correlate with national wealth.
Climate and Wealth Analysis
A data analysis pipeline that explores how climate variables like temperature and latitude relate to GDP per capita across 160+ countries.
Tech Stack
- → Python data pipeline
- → OLS regression analysis
- → Matplotlib and Plotly visualizations
- → World map and regional summaries
What It Does
- ✓ Loads and normalizes GDP, temperature, coordinates, and region data
- ✓ Fits three regression models to compare climate predictors
- ✓ Generates charts, diagnostics, and an interactive world map
- ✓ Highlights overperforming and underperforming countries relative to the model
Key Findings
- • Latitude is a stronger predictor of GDP than temperature.
- • Adding temperature to latitude gives only a small R² gain.
- • Residuals show that non-climate factors still explain most of the variance.
Repository Focus
The repo is structured as a reproducible analysis project with data inputs, a scripted pipeline, and generated outputs that make the statistical story easy to review.