6 representative projects across energy forecasting, banking risk, NLP, and policy analytics. All built to production or research publication standards.
SPOTLIGHT
Wattzo Energy Forecasting System
Machine learning for solar & grid demand · Feb–Jul 2026
Problem: Independent solar operators need 12-48 hour forecasts of PV output and grid demand to balance supply safely. Uncertainty quantification critical for confidence in routing decisions.
Solution: Dual-regime LSTM ensemble + conformal prediction. Separate models for clear-sky and cloud-covered periods. SHAP-based feature attribution for operator interpretation.
Results: 18% error reduction vs. baseline; reduced forecast uncertainty bands by 12%. Deployed across 12 sites in Southern Africa.
Tech: PyTorch · TensorFlow · Conformal prediction · SHAP · Production inference API
SPOTLIGHT
Afriland Credit Risk Scorecard
Enterprise risk modeling for retail lending · Mar–Jul 2025
Problem: Afriland First Bank needed systematic credit risk scoring for 15-branch retail loan portfolio (50K+ outstanding loans). Manual decision-making inconsistent and slow.
Solution: Logistic regression scorecard with elastic net feature selection. Threshold optimization for precision/recall tradeoff. 40+ engineered features from transaction history, applicant demographics, repayment patterns.
Results: 82% precision on high-risk flagging. Backtested model identified 80% of future defaults 6 months ahead. Reduced approval-time 4× (40 min → 10 min per application).
Tech: scikit-learn · Excel VBA dashboard · SQL · Shap explainability
SPOTLIGHT
SME Survival Policy Dashboard
Econometric + ML early-warning system for enterprise failure · Apr–Jun 2025
Problem: Cameroon's Ministry of Small & Medium Enterprises needed to identify high-failure-risk firms to target support programs. No systematic data on business survival patterns.
Solution: Hybrid econometric-ML pipeline: Lasso feature selection + ensemble models (CatBoost, Random Survival Forest). Integrated firm-level survival curves with risk scores. Interactive Shiny dashboard for policy makers.
Results: AUC = 0.884 on hold-out test set. Model identified 80% of firms failing 12–24 months ahead. Dashboard adopted by Ministry; now used to guide enterprise credit and training grants.
Tech: CatBoost · scikit-survival · Shiny (R) · PostgreSQL · survival analysis