A data scientist is working on a machine learning project using Amazon SageMaker to build a predictive model for customer churn. The dataset contains various features, including customer demographics, transaction history, and customer service interactions. The data scientist decides to use SageMaker\'s built-in algorithms for training the model. After training, they want to evaluate the model\'s performance using a confusion matrix. If the model predicts that 70 out of 100 customers who churned were correctly identified, and it incorrectly predicted that 30 customers who did not churn would churn, what is the model\'s precision?