Classical ML, NLP and forecasting, each built around the mistake that makes its answer wrong. 5 projects, each with its code on GitHub.
Twenty machine-learning projects on public data. Each built around the mistake that makes its answer wrong: search bias, target leakage, overlapping windows, imbalance, confounding.
BKT, PFA, IRT and DKT written from scratch, and how much of deep knowledge tracing's lead on ASSISTments was duplicated rows.
BM25 lands within 8 points of a pretrained neural embedding for 1/270th of the indexing cost.
A credit scorecard that explains every decline in points: WoE/IV binning with leakage detection, PDO point allocation, adverse-action reason codes, calibration, and a fairness audit that reports all four incompatible measures.
Hierarchical demand forecasting where the numbers add up: reconciliation with coherence asserted, Croston for intermittent demand, and a rolling-origin backtest that proves it cannot leak.