You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781801070157

Length 326 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Michael Hsieh

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Table of Contents (16) Chapters

Preface

1. Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio

2. Chapter 1: Machine Learning and Its Life Cycle in the Cloud FREE CHAPTER

3. Chapter 2: Introducing Amazon SageMaker Studio

4. Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio

5. Chapter 3: Data Preparation with SageMaker Data Wrangler

6. Chapter 4: Building a Feature Repository with SageMaker Feature Store

7. Chapter 5: Building and Training ML Models with SageMaker Studio IDE

8. Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify

9. Chapter 7: Hosting ML Models in the Cloud: Best Practices

10. Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot

11. Part 3 – The Production and Operation of Machine Learning with SageMaker Studio

12. Chapter 9: Training ML Models at Scale in SageMaker Studio

13. Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor

14. Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry

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Getting started with SageMaker Data Wrangler for customer churn prediction

Customer churn is a serious problem for businesses. Losing a customer is definitely not something you want to see if you are a business owner. You want to your customers to be happy with your product or service and continue to use them for, well, forever. Customer churn is always going to happen but being able to understand how and why a customer leaves the service or why a customer is not buying your product anymore is critical for your business. Being able to predict ahead of time would be even better.

In this chapter, we will perform exploratory data analysis and data transformation with SageMaker Data Wrangler, and at the end of the chapter, we will be training an ML model using the XGBoost algorithm on the wrangled data.

Preparing the use case

We are going to take a synthetic telecommunication (telco) customer churn dataset for this chapter to demonstrate what it takes to prepare a dataset for...

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You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Table of Contents (16) Chapters

Getting started with SageMaker Data Wrangler for customer churn prediction

Preparing the use case

Authors (1)

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You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Table of Contents (16) Chapters

Getting started with SageMaker Data Wrangler for customer churn prediction

Preparing the use case

Unlock this book and the full library FREE for 7 days

Authors (1)

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