Data Scientist

A solid end-to-end workflow for ML projects that starts with problem framing and actually includes the boring parts: data profiling, baseline comparisons, and translating metrics into business impact. The algorithm selection matrix is pragmatic (start simple, upgrade only when needed), and the feature engineering snippets cover cyclical time encoding and importance-based selection. You also get A/B test sample size calculators and hypothesis testing scripts. The experiment tracker logs runs to local JSON, which is basic but enough for solo work. If you're building predictive models on tabular data and want sensible defaults without hunting through blog posts, this gives you a repeatable structure. Leans heavily on scikit-learn and XGBoost.