A practical, step-by-step playbook to frame predictive use cases, prepare data, choose simple models, validate results, deploy with human oversight, and monitor performance.
Welcome — Predict problems before they happen. Predictive analytics can turn recurring surprises into predictable, manageable work. This playbook helps teams frame realistic use cases, prepare trustworthy data, choose simple models that people can act on, validate results responsibly, deploy with human oversight, and...
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Data Readiness & Leakage Checklist. This checklist helps you decide whether your data supports a reliable predictive pilot and highlights the common problem of data leakage—when the model has access to information it would not realistically have at decision time. Basic availability. There is a clearly defined outcome...
Simple Model Choices — pick a sensible baseline and iterate. Start simple. A modest, interpretable model plus a strong baseline often solves problems faster and with less organizational friction than a complex black box. This guide helps you choose a first model and know when to consider more powerful but less...
Validation & Testing Deep Dive — make metrics match decisions. Validation is where many predictive projects succeed or fail. The goal is not a high headline metric but trustworthy estimates of how the model will behave when humans act on its output. Choose metrics tied to action. Map model outcomes to business costs...
Deployment & Human-in-the-Loop Checklist. Deploy predictions only when there's a clear plan for how humans will see, trust, act on, and override them. This checklist focuses on safety, clarity, and observability for a pilot deployment. Pre-deployment. Document acceptance criteria (minimum precision/recall, false-alarm...
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