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Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?
Correct Answer: B
In BigQuery ML, when creating a logistic regression model to predict customer churn, the correct query should: Exclude the target label column (in this case, churned) from the feature columns, as it is used for training and not as a feature input. Rename the target label column to label, as BigQuery ML requires the target column to be named label. The chosen query satisfies these requirements: SELECT * EXCEPT(churned), churned AS label: Excludes churned from features and renames it to label. The OPTIONS(model_type='logistic_reg') specifies that a logistic regression model is being trained. This setup ensures the model is correctly trained using the features in the dataset while targeting the churned column for predictions.
