Predictive Analytics

When to Use

Define the prediction target, unit of analysis, horizon, and success criteria before modeling
Audit leakage, label timing, and train/validation design for tabular or time-ordered data
Engineer and select features for churn, propensity, fraud, demand, or risk scoring use cases
Choose model families and baselines (linear, tree ensembles, gradient boosting) matched to data size and interpretability needs
Run validation: holdout, cross-validation, or time-based splits with metrics aligned to the decision
Tune calibration, thresholds, and cost-sensitive operating points for classification and scores
Explain models at practitioner level (importance, partial dependence, SHAP-style intuition—not full XAI research)
Plan conceptual post-deployment monitoring: drift signals, retrain triggers, and limitation language for stakeholders