How will you handle the QA process when developing a predictive model to forecast customer churn?

Data analysts require inputs from the business owners and a collaborative environment to operationalize analytics. To create and deploy predictive models in production there should be an effective, efficient and repeatable process. Without taking feedback from the business owner, the model will just be a one-and-done model.

The best way to answer this question would be to say that you would first partition the data into 3 different sets Training, Testing and Validation. You would then show the results of the validation set to the business owner by eliminating biases from the first 2 sets. The input from the business owner or the client will give you an idea on whether you model predicts customer churn with accuracy and provides desired results.