You're reading from Machine Learning with Amazon SageMaker Cookbook 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Product type Paperback

Published in Oct 2021

Publisher Packt

ISBN-13 9781800567030

Length 762 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Joshua Arvin Lat

View More author details

Table of Contents (11) Chapters

Preface

1. Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker

2. Chapter 2: Building and Using Your Own Algorithm Container Image FREE CHAPTER

3. Chapter 3: Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker

4. Chapter 4: Preparing, Processing, and Analyzing the Data

5. Chapter 5: Effectively Managing Machine Learning Experiments

6. Chapter 6: Automated Machine Learning in Amazon SageMaker

7. Chapter 7: Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor

8. Chapter 8: Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms

9. Chapter 9: Managing Machine Learning Workflows and Deployments

10. Other Books You May Enjoy

Performing cluster analysis with the built-in KMeans algorithm

In this recipe, we will demonstrate how to use the KMeans algorithm to perform cluster analysis with the synthetic dataset. Cluster analysis involves identifying subgroups of records within the dataset that exhibit similar properties. This helps solve different problems and requirements related to market segmentation, fraud detection, and document analysis.

Getting ready

This recipe continues from Generating a synthetic dataset for analysis and transformation.

How to do it…

The next set of steps focus on using the unlabeled dataset we generated in the Generating a synthetic dataset for analysis and transformation recipe to prepare the KMeans model we will use for cluster analysis:

Navigate to the my-experiments/chapter04 directory inside your SageMaker notebook instance. Feel free to create this directory if it does not exist yet.
Create a new notebook using the conda_python3 kernel inside...