Selected work
A product in progress and four technical case studies, each separating the question, method, reported result and what the evidence cannot prove.
My evaluation lens
I use the same five questions to connect technical quality with what a person can safely decide.
Verdict: research prototype, not a clinical product.
Inspect method and code ↓
An evidence-first B2B SaaS designed to help UK fire-safety contractors identify relevant public procurement opportunities and likely contract-renewal windows.
Published opportunities are never relabelled from a renewal forecast. Evidence remains attached to the signal.
Profiles, deterministic matching and renewal intelligence focus the product on UK fire-safety procurement.
The local build and browser journey have been validated; hosted CI, target VPS acceptance and production readiness are not claimed.
01 · RAG and evaluation
A research retrieval-and-answering prototype in which every response stays connected to evidence and a specific patient in a synthetic dataset.
Build an auditable RAG workflow for NHS-style records without using real patient data.
TF-IDF retrieval, qrels, citation-aware answers, safe responses when evidence is insufficient, pytest checks and documented assumptions.
Reported retrieval results: Precision@3 0.583, Recall@3 1.000 and MRR 1.000 within the project’s defined evaluation.
The dataset is synthetic and the system is an academic prototype, not a clinical product or medical device.
02 · Computer vision
A NumPy implementation of perceptron and feed-forward networks focused on label quality, error analysis and model comparison.
Understand the mechanics of neural-network learning by implementing the essential parts without a high-level framework.
Backpropagation, regularisation, early stopping, linear-versus-nonlinear comparison and checks for inconsistent labels.
Aligned multiclass accuracy of 0.8862 after a documented correction to the label mapping.
The score belongs to the Sign Language MNIST benchmark; it is not the performance of a real-world camera-based sign-language system.
03 · Machine learning
A reproducible regression and classification pipeline with baselines, feature engineering, validation and automated tests.
Separate predictive signal from an appealing but weakly generalising story about music data.
CatBoost regression, Linear SVC classification, model comparisons, diagnostic figures and data-quality tests.
Reported benchmark RMSE of 6.52657 and classification accuracy of 0.57142.
Results depend on the dataset, split and target scale. The work documents overfitting risk and is not a production recommender.
04 · Optimisation
A chronological evaluation of a 15-parameter strategy with transaction costs and several comparison baselines.
Reduce the risk of fitting a strategy to history and evaluate it on unseen, later data.
Time-respecting train/test split, random-search, fixed-rule and buy-and-hold baselines, plus explicit transaction costs.
Reported out-of-sample return of 7.50% with maximum drawdown of 9.99%.
A research project, not investment advice. Historical performance does not predict future returns and needs its period and benchmark for full interpretation.