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Predict Visitor Purchases with a Classification Model in BQML

Predict Visitor Purchases with a Classification Model in BQML

1시간 15분 크레딧 7개

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

이 실습의 나머지 부분과 기타 사항에 대해 알아보려면 Qwiklabs에 가입하세요.

  • Google Cloud Console에 대한 임시 액세스 권한을 얻습니다.
  • 초급부터 고급 수준까지 200여 개의 실습이 준비되어 있습니다.
  • 자신의 학습 속도에 맞춰 학습할 수 있도록 적은 분량으로 나누어져 있습니다.
이 실습을 시작하려면 가입하세요
점수

—/100

Create a new dataset

단계 진행

/ 20

Create a model and specify model options

단계 진행

/ 20

Evaluate classification model performance

단계 진행

/ 15

Improve model performance with Feature Engineering(Create second model)

단계 진행

/ 15

Improve model performance with Feature Engineering(Better predictive power)

단계 진행

/ 15

Predict which new visitors will come back and purchase

단계 진행

/ 15