BigQuery ML의 연결된 시트와 예측 모델을 결합하여 비즈니스 프로세스에서 머신러닝을 운용하는 방법을 알아보세요. 이 패턴은 Google 애널리틱스 데이터를 사용하여 웹사이트 트래픽에 대한 예측 모델을 빌드하는 과정을 설명합니다. 이 패턴을 확장하여 다른 데이터 유형 및 다른 머신러닝 모델에서 작업할 수 있습니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(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/)"]]
