Predict Visitor Purchases with a Classification Model in BQML
BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
BigQuery Machine Learning (BQML, product in beta) is a new feature in BigQuery where data analysts can create, train, evaluate, and predict with machine learning models with minimal coding.
There is a newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into BigQuery. In this lab you will use this data to run some typical queries that businesses would want to know about their customers' purchasing habits.
In this lab, you learn to perform the following tasks:
Use BigQuery to find public datasets
Query and explore the ecommerce dataset
Create a training and evaluation dataset to be used for batch prediction
Create a classification (logistic regression) model in BQML
Evaluate the performance of your machine learning model
Predict and rank the probability that a visitor will make a purchase
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- 获取对“Google Cloud Console”的临时访问权限。
- 200 多项实验，从入门级实验到高级实验，应有尽有。
Create a new dataset
Create a model and specify model options
Evaluate classification model performance
Improve model performance with Feature Engineering(Create second model)
Improve model performance with Feature Engineering(Better predictive power)
Predict which new visitors will come back and purchase