menu
arrow_back

Creating Models with Amazon SageMaker

Creating Models with Amazon SageMaker

小时 15 积分

Creating Models with Amazon SageMaker

SPL-212 - Version 1.0.1

© 2019 Amazon Web Services, Inc. and its affiliates. All rights reserved. This work may not be reproduced or redistributed, in whole or in part, without prior written permission from Amazon Web Services, Inc. Commercial copying, lending, or selling is prohibited.

Errors or corrections? Email us at aws-course-feedback@amazon.com.

Other questions? Contact us at https://aws.amazon.com/contact-us/aws-training/

Lab Overview

Customer Churn Prediction with XGBoost

Using Gradient Boosted Trees to Predict Mobile Customer Departure

Topics Covered

  • Background
  • Setup
  • Data
  • Train
  • Host
  • Evaluate

Background

Losing customers is costly for any business. Identifying unhappy customers early on gives you a chance to offer them incentives to stay. This notebook describes using machine learning (ML) for the automated identification of unhappy customers, also known as customer churn prediction. ML models rarely give perfect predictions though, so this notebook is also about how to incorporate the relative costs of prediction mistakes when determining the financial outcome of using ML.

We use an example of churn that is familiar to all of us–leaving a mobile phone operator. Seems like I can always find fault with my provider du jour! And if my provider knows that I’m thinking of leaving, it can offer timely incentives–I can always use a phone upgrade or perhaps have a new feature activated–and I might just stick around. Incentives are often much more cost effective than losing and reacquiring a customer.

Technical Knowledge Prerequisites

To successfully complete this lab, you should be familiar with basic navigation of the AWS Management Console and with the navigation and running of a notebook on Amazon Sagemaker.

Start Lab

  1. At the top of your screen, launch your lab by clicking Start Lab

This will start the process of provisioning your lab resources. An estimated amount of time to provision your lab resources will be displayed. You must wait for your resources to be provisioned before continuing.

If you are prompted for a token, use the one distributed to you (or credits you have purchased).

  1. Open your lab by clicking Open Console

This will automatically log you into the AWS Management Console.

Please do not change the Region unless instructed.

Common login errors

Error : Federated login credentials

If you see this message:

  • Close the browser tab to return to your initial lab window
  • Wait a few seconds
  • Click Open Console again

You should now be able to access the AWS Management Console.

Error: You must first log out

If you see the message, You must first log out before logging into a different AWS account:

  • Click click here
  • Close your browser tab to return to your initial Qwiklabs window
  • Click Open Console again

Join Qwiklabs to read the rest of this lab...and more!

  • Get temporary access to the Amazon Web Services Console.
  • Over 200 labs from beginner to advanced levels.
  • Bite-sized so you can learn at your own pace.
Join to Start This Lab