Your retail company wants to predict customer churn using historical purchase data stored in BigQuery.

Google Associate-Data-Practitioner Question Answer

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?

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

CREATE OR REPLACE MODEL churn_prediction_model options (model type=’logistic_reg’) AS select churned as label FROM customer_data;

CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ’ except(churned) FROM customer data;

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Your retail company wants to predict customer churn using historical purchase data stored in BigQuery.

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