"Develop a predictive analytics framework for identifying and mitigating customer churn, detailing the process from data collection to deployment. Outline the steps involved,** including:** ## 1.** Data Collection:** Specify data sources and types, and discuss data quality and preprocessing techniques. ## 2.** Feature Engineering:** Describe the process of creating relevant features from collected data, including feature selection and dimensionality reduction. ## 3.** Model Selection:** Discuss the choice of machine learning algorithms for churn prediction, including model evaluation metrics and hyperparameter tuning. ## 4.** Deployment:** Outline strategies for integrating the predictive model into the organization's operations, ensuring scalability and adaptability. Include relevant examples, metrics, and technical details to support the approach, assuming an advanced data science and analytics audience. Address how the framework can be used to forecast customer churn accurately and suggest retention strategies."