Reference patterns

This page provides links to business use cases, sample code, and technical reference guides for BigQuery ML use cases. Use these resources to identify best practices and speed up your application development.

Logistic regression

This pattern shows how to use logistic regression to perform propensity modeling for gaming applications.

Learn how to use BigQuery ML to train, evaluate, and get predictions from several different types of propensity models. Propensity models can help you to determine the likelihood of specific users returning to your app, so you can use that information in marketing decisions.

Time-series forecasting

These patterns show how to create time-series forecasting solutions.

Build a demand forecasting model

Learn how to build a time series model that you can use to forecast retail demand for multiple products.

Forecast from Google Sheets using BigQuery ML

Learn how to operationalize machine learning with your business processes by combining Connected Sheets with a forecasting model in BigQuery ML. This pattern walks you through the process for building a forecasting model for website traffic using Google Analytics data. You can extend this pattern to work with other data types and other machine learning models.

Anomaly detection

This pattern shows how to use anomaly detection to find real-time credit card fraud.

Learn how to use transactions and customer data to train machine learning models in BigQuery ML that can be used in a real-time data pipeline to identify, analyze, and trigger alerts for potential credit card fraud.