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Predict Visitor Purchases with a Classification Model in BQML

75m access · 60m completion
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7 Credits

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01:15:00

Predict Visitor Purchases with a Classification Model in BQML

GSP229

Google Cloud Self-Paced Labs

Overview

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.

Objectives

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

What you'll need

  • A Google Cloud Platform Project

  • A Browser, such as Google Chrome or Mozilla Firefox

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Score

—/30

Create a new dataset

Run Step

/ 5

Create a model and specify model options

Run Step

/ 5

Evaluate classification model performance

Run Step

/ 5

Improve model performance with Feature Engineering(Create second model)

Run Step

/ 5

Improve model performance with Feature Engineering(Better predictive power)

Run Step

/ 5

Predict which new visitors will come back and purchase

Run Step

/ 5

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