algo-hr-turnoverlisted

# Employee Turnover Prediction ## Overview Turnover prediction uses classification models (logistic regression, random forest, XGBoost) to estimate the probability an employee will leave within a defined period (typically 6-12 months). Features include tenure, compensation, performance, promotion history, and engagement signals. ## When to Use **Trigger conditions:** - Identifying employees at high risk of voluntary departure - Quantifying which factors drive turnover for targeted interventions - Prioritizing retention budgets toward highest-impact employees **When NOT to use:** - For involuntary termination planning (different process and ethics) - When headcount is < 200 (insufficient data for reliable modeling) ## Algorithm ``` IRON LAW: Turnover Models Predict RISK, Not Certainty A predicted 80% turnover probability means "employees with similar profiles historically left 80% of the time." It does NOT mean this specific employee WILL leave. Never use model outputs as sole basis for employment decisions — that creates legal and ethical liability. ``` ### Phase 1: Input Validation Collect: employee demographics, tenure, compensation (relative to market), last promotion date, performance ratings, manager change history, engagement survey scores, commute distance. Outcome: voluntary departure within N months. **Gate:** Minimum 200 turnover events, features available before departure date. ### Phase 2: Core Algorithm 1. Feature engineering: tenure buckets, comp ratio