tseon-designs/climate-challenge-week0

Data-driven climate analysis for Ethiopia’s COP32 preparation. This project performs exploratory analysis on historical climate data across 5 African nations to surface trends, extreme event impacts, and policy-relevant evidence for climate finance and adaptation strategy. # 🌍 African Climate Trend Analysis — COP32 Edition **EthioClimate Analytics** | Exploratory analysis of historical climate data (2015–2026) across five African nations to support Ethiopia's data-driven position at COP32 in Addis Ababa, 2027. --- ## 🎯 The "So What?" (Project Impact) This project moves beyond academic research to produce **negotiation-grade insights**. By analyzing 11 years of daily NASA POWER satellite data, we demonstrate: - **Fastest Warming**: Sudan is warming at a statistically significant rate, demanding urgent adaptation finance. - **Extreme Volatility**: Tanzania and Kenya face the most erratic rainfall patterns, justifying early warning system investments. - **Policy Support**: We provide a data-backed foundation for Ethiopia's leadership at COP32 via a **Composite Vulnerability Ranking**. --- ## 🚀 Live Dashboard Experience the data interactively: **View Streamlit Dashboard** --- ## 🛠️ Technical Breadth - **Data Engineering**: Modular Python pipeline (`src/clean.py`) with Z-score outlier detection and ffill imputation. - **Exploratory Data Analysis**: 5 country-level EDA notebooks + 1 cross-country synthesis. - **Statistical Rigor**: Kruskal-Wallis significance testing and linear regression trends. - **DevOps**: GitHub Actions CI pipeline for environment verification and automated unit testing. - **UI/UX**: Interactive Streamlit dashboard with Plotly integration and professional dark-mode styling. --- ## 🗂️ Project Structure ``` climate-challenge-week0/ ├── .github/workflows/ │ └── ci.yml # CI pipeline (Linting + Testing) ├── app/ │ ├── main.py # Streamlit dashboard UI │ └── utils.py # Dashboard logic & caching ├── notebooks/ │ ├── eda_ethiopia.ipynb # Individual country EDA │ └── compare_countries.ipynb # Statistical synthesis & ranking ├── src/ │ ├── clean.py # Reusable cleaning logic │ └── visualize.py # Professional plotting utilities ├── tests/ │ └── test_ …