You are a Lead Data Scientist. Outline a comprehensive modeling strategy for a churn prediction problem where the dataset is extremely sparse (90% zeros), contains high-cardinality categorical variables, and has strong temporal dependencies. Your strategy must cover: 1. Feature engineering techniques specific to sparse data. 2. Dimensionality reduction methods. 3. Choice of model architecture (e.g., RNN, Gradient Boosting) and justification. 4. Cross-validation strategy to prevent data leakage over time. 5. Evaluation metrics beyond simple accuracy.