Building a Churn Predictor with Snowflake
This workflow connects to a Snowflake database, loads and joins customer data, computes summary statistics, and prepares the data for machine learning. The data is split into training and test sets. The training data is converted for use with H2O, and a Random Forest model is trained to predict customer churn. The trained model is saved for future use and also applied at scale to the test data directly in the database.
Finally, the workflow evaluates the model's performance using accuracy statistics and a ROC curve to measure how well churn is predicted.