"Develop a comprehensive, step-by-step guide for creating a customer lifetime value (CLV) prediction model using machine learning algorithms, tailored for advanced users.** Address the following components:** ## 1. **Data Preprocessing**: Outline the necessary steps for data cleaning, handling missing values, and normalization. Provide Python code snippets to demonstrate data preprocessing techniques. ## 2. **Feature Engineering**: Explain the process of selecting and transforming relevant features that impact CLV. Include examples of feature engineering techniques and their implementation in Python. ## 3. **Model Selection and Training**: Discuss suitable machine learning algorithms for CLV prediction and describe how to train the models using the preprocessed data. Offer Python code examples for model implementation. ## 4. **Model Evaluation**: Describe metrics for evaluating the performance of CLV prediction models. Provide guidance on interpreting results and selecting the best model. Ensure the guide is technically detailed, includes mathematical explanations where relevant, and reflects an advanced understanding of predictive modeling and machine learning."