jayy-agu/nigeria-power-crisis-analysis

End-to-end data analysis of Nigeria's power sector using Python (EDA, visualization, and ML forecasting) # Nigeria Power Crisis — Data Analysis & ML Forecasting ## Overview End-to-end analysis of Nigeria's electricity sector using Python, covering economic costs, generation capacity, distribution performance, grid quality, and state-level access data from 2019–2024. ## Key Findings - Nigeria loses $48bn/year to its energy crisis — more than the entire federal budget - Only 31% of installed generation capacity is actually used - The grid collapsed 8 times in 2024, double the 2019 figure - Private generator capacity (42 GW) is now 10x larger than the actual grid (4.2 GW) - Metering rate predicts payment rate with R²=0.95 — the single most actionable fix ## Tools & Libraries Python · pandas · matplotlib · seaborn · scikit-learn · Jupyter ## Models Applied | Model | Purpose | Result | |-------|---------|--------| | Linear Regression | Forecast crisis cost 2025–2027 | $55.4bn by 2027 | | Polynomial Regression | Test for accelerating trend | R²=0.887, confirms acceleration | | K-Means Clustering | Segment DisCos by performance | 3 tiers: Distressed / Developing / Performing | | Linear Regression | Predict payment rate from metering | R²=0.95, 75% metering → 90.7% payment | ## Data Sources Dataset independently compiled from NERC quarterly reports, TCN grid operations data, DisCo performance filings, CBN economic statistics, and World Bank energy access data.