Predictive Analytics

Predictive analytics использует historical data для forecasting future events. Applications широки от operations до strategic planning.

Techniques

Regression models: numeric forecasts (revenue, players). Classification: binary/multi-class (churn, fraud, VIP). Time series: temporal patterns. Survival analysis: time to event (churn). Ensemble methods обычно best-performing.

iGaming applications

Player LTV prediction. Churn prediction. Fraud probability. Bonus response prediction. Revenue forecasting: 30/90-day. Player VIP probability. Session outcome prediction. Marketing ROI forecasting. Каждая сохраняет money или generates revenue.

Pipeline

Feature engineering. Model training. Validation split. Hyperparameter tuning. Deployment via API или batch. Monitoring performance. Regular retraining. MLOps practices. Business validation: predictions vs actual outcomes.

Best practices

Start simple: baseline modeling. Feature engineering — biggest lift. Avoid data leakage. Cross-validation right (temporal для time series). Explainability для business trust. Cost-sensitive learning: not all errors equal. Continuous monitoring и improvement.