Automated Customer Churn Prediction Feature Engineering Sheet

This feature-engineering workbook organizes common predictors used for churn modeling and prepares a training dataset suitable for machine learning. It computes temporal features (recency, frequency, tenure), monetary features (lifetime value, average order value), behavioral signals (session counts, feature usage), and derived aggregations (rolling averages, trend slopes). The sheet includes automated bucketing (cohort-based quantiles), one-hot encoding helpers (category flags), and a missing-value imputation policy with audit tracking for imputed rows. It exports a normalized CSV for modeling and includes a target construction template (e.g., churn in next 30/90 days). There are also instructions for backtesting and holdout splits (time-based), plus a column naming convention to support repeatable pipelines. Use this workbook to bridge analysts and data scientists, enabling fast iteration on feature sets while preserving provenance and reproducibility.