Claude Prompts for Data Science in Data & Analytics

Frame a churn prediction problem clearly: target definition, prediction horizon, label edge cases, and the tradeoffs between competing formulations.

Generate a structured set of feature engineering candidates for an ML problem, organized by feature family with leakage and stability flags.

Produce a model card that documents what an ML model does, how it was built, where it works, and where it does not — for stakeholders, auditors, and the team that will inherit it.

Produce a structured ML experiment plan: problem framing, hypotheses, baselines, success criteria, and the path to a deployable model — before writing any training code.

Explain what a propensity model does, what its score means, and how to use it — for a non-technical executive who will sponsor or rely on the output.