Ja-Akins/G7-vs-Africa-Economic-Analysis

End-to-end economic data analysis project featuring Python ETL pipelines, SQL-based analytical metrics, Jupyter notebooks, and Tableau Dashboard to compare G7 and African economic performance. # 🌍 G7 vs. Emerging Africa: Economic Volatility & Recovery Analysis (2000-2024) ## Project Overview This project performs an end-to-end Data Engineering and Analytics analysis comparing the economic resilience of G7 Nations against Africa's Top 5 Emerging Economies. The goal was to move beyond static datasets by building a live ETL pipeline to extract World Bank API data, apply statistical methods to detect economic "shock events," and leverage SQL to uncover the "Efficiency Gap" in debt utilization. Data Source: World Bank Open Data API (Live Fetch) Tools Used: Python (Pandas, Concurrent Futures), SQL (MySQL), Tableau, Seaborn ## 🛠️ Data Engineering Process (Python Implementation) Unlike standard CSV analyses, this project required building a robust pipeline to handle 25 years of data across 8 indicators for 12 countries. * **Concurrent Extraction:** * Implemented `concurrent.futures.ThreadPoolExecutor` to multi-thread API requests, reducing data ingestion time by **70%**. * Handled pagination logic to ensure zero data loss across thousands of records. * **Statistical Outlier Detection (The IQR Method):** * Instead of blindly deleting outliers, I calculated the **Interquartile Range (IQR)** for every country-indicator pair. * Data points falling outside `1.5 * IQR` were flagged as **"Shock Events"** in a dedicated column (`is_outlier`). This allowed for the analysis of economic volatility without destroying data integrity. * **Secure Loading:** * Utilized environment variables (`.env`) to secure database credentials and `SQLAlchemy` for optimized batch loading into MySQL. ## 📈 Exploratory Data Analysis (SQL Implementation) Following the engineering phase, advanced SQL techniques were used to derive actionable economic insights: * **Volatility Indexing:** Calculated the frequency of economic shocks using `COUNT(CASE WHEN is_outlier = 1)` to rank countries by stability. * **Inflation Momentum:** Tracked post-pand …