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Padrões de referência
Nesta página, fornecemos links para casos de uso comerciais, exemplos de código e guias de referência técnica para casos de uso do BigQuery ML. Use esses recursos para identificar práticas recomendadas e acelerar o desenvolvimento de aplicativos.
Regressão logística
Esse padrão mostra como usar a regressão logística para realizar
para aplicativos de jogos.
Aprenda a usar o BigQuery ML para treinar, avaliar e receber previsões de vários tipos diferentes de modelos de propensão.
Eles ajudam a determinar a probabilidade de usuários específicos voltarem ao seu app. Assim, você poderá usar essas informações em decisões de marketing.
Previsão do app Planilhas Google usando o BigQuery ML
Saiba como operacionalizar o machine learning com seus processos de
negócios combinando
Páginas conectadas com um modelo de previsão
no BigQuery ML. Esse padrão orienta você no processo de criação de um modelo de previsão para o tráfego do site usando dados do Google Analytics. Estenda esse padrão para trabalhar com outros tipos de dados e modelos de machine learning.
Este padrão mostra como usar a detecção de anomalias para encontrar fraudes em cartão de crédito em tempo real.
Saiba como usar dados de transações e do cliente para treinar modelos de machine learning no BigQuery ML que podem ser usados em um
pipeline de dados em tempo real para identificar, analisar e acionar alertas de
potencial fraude de cartão de crédito.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-08-17 UTC."],[[["\u003cp\u003eThis page provides resources such as business use cases, sample code, and technical guides for various BigQuery ML applications.\u003c/p\u003e\n"],["\u003cp\u003eLearn to create propensity models with logistic regression, which can determine the likelihood of user engagement, such as returning to your app.\u003c/p\u003e\n"],["\u003cp\u003eExplore time-series forecasting patterns to build models for predicting retail demand for products.\u003c/p\u003e\n"],["\u003cp\u003eDiscover how to combine Connected Sheets with a forecasting model in BigQuery ML to operationalize machine learning for business tasks, like forecasting website traffic.\u003c/p\u003e\n"],["\u003cp\u003eUtilize anomaly detection patterns to identify and analyze potential credit card fraud in real-time using machine learning models trained in BigQuery ML.\u003c/p\u003e\n"]]],[],null,["# Reference patterns\n==================\n\nThis page provides links to business use cases, sample code, and technical\nreference guides for BigQuery ML use cases. Use these resources to\nidentify best practices and speed up your application development.\n\nLogistic regression\n-------------------\n\nThis pattern shows how to use logistic regression to perform propensity\nmodeling for gaming applications.\n\nLearn how to use BigQuery ML to train, evaluate, and get\npredictions from several different types of propensity models.\nPropensity models can help you to determine the likelihood of specific\nusers returning to your app, so you can use that information in\nmarketing decisions.\n\n- Blog post: [Churn prediction for game developers using Google Analytics 4 and BigQuery ML](/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml)\n- Notebook: [Churn prediction solution notebook](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/gaming/propensity-model/bqml)\n\nTime-series forecasting\n-----------------------\n\nThese patterns show how to create time-series forecasting solutions.\n\n### Build a demand forecasting model\n\nLearn how to build a time series model that you can use to forecast retail\ndemand for multiple products.\n\n- Blog post: [How to build demand forecasting models with BigQuery ML](/blog/topics/developers-practitioners/how-build-demand-forecasting-models-bigquery-ml)\n- Notebook: [Demand forecasting solution notebook](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/time-series/bqml-demand-forecasting/bqml_retail_demand_forecasting.ipynb)\n\n### Forecast from Google Sheets using BigQuery ML\n\nLearn how to operationalize machine learning with your business\nprocesses by combining\n[Connected Sheets](/bigquery/docs/connected-sheets) with a forecasting\nmodel in BigQuery ML. This pattern walks you through\nthe process for building a forecasting model for website traffic using\nGoogle Analytics data. You can extend this pattern to work\nwith other data types and other machine learning models.\n\n- Blog post: [How to use a machine learning model from Google Sheets using BigQuery ML](/blog/topics/developers-practitioners/how-use-machine-learning-model-google-sheet-using-bigquery-ml)\n- Sample code: [BigQuery ML forecasting with Sheets](https://github.com/googleworkspace/ml-integration-samples/tree/master/apps-script/BQMLForecasting)\n- Template: [BigQuery ML forecasting with Sheets](https://docs.google.com/spreadsheets/d/1njedwGjBOkUbTS_HYD0wIPuQIDHgobp1D80qO-OsNH0/copy)\n\nAnomaly detection\n-----------------\n\nThis pattern shows how to use anomaly detection to find real-time credit\ncard fraud.\n\nLearn how to use transactions and customer data to train machine\nlearning models in BigQuery ML that can be used in a\nreal-time data pipeline to identify, analyze, and trigger alerts for\npotential credit card fraud.\n\n- Sample code: [Real-time credit card fraud detection](https://github.com/googlecloudplatform/fraudfinder)\n- Overview video: [Fraudfinder: A comprehensive solution for real data science problems](https://io.google/2022/program/9a759b60-9a9b-4744-bd22-6e21a4a864cd/)"]]