Relevant data for building a churn prediction model may include customer demographics, purchase history, usage patterns, customer service interactions, product preferences, and any other data that can provide insights into customer behavior and their likelihood to churn.

Feature selection techniques such as statistical analysis, correlation analysis, or machine learning algorithms like decision trees or L1 regularization can help identify the key features that have the most significant influence on customer churn. These techniques measure the importance or relevance of each feature in predicting churn.

Commonly used algorithms for churn prediction include logistic regression, decision trees, random forests, gradient boosting, and neural networks. The choice of algorithm depends on factors such as dataset size, complexity, interpretability, and the trade-off between accuracy and computational resources.