AIODBOSS/africa-fiscal-sustainability-analysis

Data-driven fiscal sustainability assessment framework for African economies using machine learning and predictive analytics # 🌍 Africa Fiscal Sustainability Analysis **Data-driven framework for assessing fiscal health in African economies using machine learning and predictive analytics** --- ## 📋 Overview This project develops an integrated analytical framework to assess fiscal sustainability across African economies, addressing critical challenges of persistent deficits, accelerating debt, and revenue volatility. **Key Features:** - Multi-country fiscal data analysis (40+ African nations) - Anomaly detection using Z-score and Isolation Forest - Composite risk scoring framework - Time series forecasting with scenario analysis - Policy recommendations aligned with SDGs --- ## 🎯 Problem Statement African economies face structural fiscal challenges: - **Persistent deficits** exceeding -3% of GDP - **Accelerating debt** surpassing sustainability thresholds - **Revenue volatility** 3-5x higher in commodity-dependent economies Traditional analysis lacks predictive capability for proactive intervention. --- ## 🔬 Methodology ### Data Sources - World Bank Open Data - IMF World Economic Outlook - African Development Bank ### Key Indicators - Fiscal Balance Ratio = (Revenue - Expenditure) / GDP - Debt-to-GDP Ratio - Revenue Volatility (5-year rolling std) - Expenditure Growth Rate ### Analysis Modules **1. Exploratory Data Analysis** - Time series trends - Cross-country heatmaps - Correlation analysis **2. Anomaly Detection** - Statistical: Z-score method - Machine Learning: Isolation Forest **3. Risk Assessment** - Composite Risk Score = 0.35×Deficit + 0.30×Debt + 0.25×Volatility + 0.10×Growth - Risk categories: Low / Moderate / High / Critical **4. Forecasting** - 5-year projections - Scenario analysis (optimistic, baseline, pessimistic) --- ## 📊 Key Findings 1. **Structural Deficit Crisis**: Majority of countries exceed -3% threshold 2. **Debt Acceleration**: Sharp increase post-2015 3. **Revenue Volatility**: Root cause identified - commodity dependence 4. **Risk Assess …