loyce0323-cpu/east-africa-economic-indicators

# East Africa Economic Indicators — Multi-Tool Data Analysis Project A full-stack data analysis project tracking GDP Growth, Inflation, and Unemployment across 5 Sub-Saharan African economies (2000–2023), progressing through four tools: **Excel → SQL → R → Python**. ## 📊 Project Overview **Data Source:** World Bank — World Development Indicators **Countries:** Ethiopia, Kenya, Tanzania, Uganda, Nigeria **Period:** 2000–2023 (24 years) **Indicators:** GDP Growth (%), Inflation (%), Unemployment (%) ## 🔑 Key Findings - **Fastest average GDP growth:** Ethiopia (8.43%), driven by sustained infrastructure investment - **Most volatile inflation:** Uganda (std dev 16.81%), reflecting early-2000s hyperinflation episodes - **Hardest hit by COVID-19 (2020):** Nigeria, consistent with the oil price collapse + pandemic shock - **GDP growth vs Inflation:** No statistically significant correlation found (r = 0.07, p = 0.45) - **Forecasting:** A linear regression on Kenya's GDP growth shows weak predictive power (R² = 0.07), confirming growth is driven by multiple factors beyond time trend alone ## 🛠️ Tools & Skills by Stage ### 1. Excel - Data cleaning and reshaping from World Bank raw export - Pivot tables and pivot charts - INDEX/MATCH formulas for Best Year / Worst Year analysis - Interactive dashboard with KPI summary cards - File: `East_Africa_Economic_Indicators.xlsx` ### 2. SQL (SQL Server) - Database design and table creation - Data import and troubleshooting - Window functions: `LAG()` for year-on-year change, `RANK()` for country comparison - Manual pivoting using `CASE WHEN` - Aggregate analysis: AVG, MIN, MAX, STDEV - File: `EastAfrica_Analysis_Queries.sql` ### 3. R - Data wrangling with `dplyr` - Visualization with `ggplot2` - Pearson correlation test (GDP Growth vs Inflation) - File: `analysis.R` ### 4. Python - Data analysis with `pandas` - Visualization with `seaborn` / `matplotlib` - Linear regression forecasting with `scikit-learn` - File: `analysis.ip …