Multi-Dimensional Customer Lifetime Value Predictive Model

Design a comprehensive predictive model for Customer Lifetime Value (CLV) using machine learning techniques in Python. The model should incorporate non-linear features including purchase frequency, average transaction value, customer tenure, product category interactions, and seasonal spending patterns. Implement cross-validation with stratified K-fold, feature importance ranking using SHAP values, and create a deployment-ready scoring mechanism that can handle real-time feature updates and provide confidence intervals for predictions.