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.
Regression and classification
These patterns show how to create regression and classification solutions.
Build new audiences based on current customer lifetime value
Learn how to identify your most valuable current customers, and then use them to develop similar audiences in Google Ads.
- Technical reference guide: Building new audiences based on existing customer lifetime value
- Sample code: Activate on LTV predictions
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.
- Blog post: Churn prediction for game developers using Google Analytics 4 and BigQuery ML
- Notebook: Churn prediction solution notebook
- Technical overview: Propensity modeling for gaming applications
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.
- Blog post: How to build demand forecasting models with BigQuery ML
- Notebook: Demand forecasting solution notebook
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.
- Blog post: How to use a machine learning model from Google Sheets using BigQuery ML
- Sample code: BigQuery ML forecasting with Sheets
- Template: BigQuery ML forecasting with Sheets
These patterns show how to create anomaly detection solutions.
Real-time credit card fraud detection
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.
- Sample code: Real-time credit card fraud detection
- Overview video: Fraudfinder: A comprehensive solution for real data science problems