You're reading from Serverless Machine Learning with Amazon Redshift ML Create, train, and deploy machine learning models using familiar SQL commands

Product type Paperback

Published in Aug 2023

Publisher Packt

ISBN-13 9781804619285

Length 290 pages

Edition 1st Edition

Languages

Python

Tools

Amazon Redshift

Concepts

Machine Learning

Authors (4):

Phil Bates

Sumeet Joshi

Debu Panda

Bhanu Pittampally

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Table of Contents (19) Chapters

Preface

1. Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning

2. Chapter 1: Introduction to Amazon Redshift Serverless FREE CHAPTER

3. Chapter 2: Data Loading and Analytics on Redshift Serverless

4. Chapter 3: Applying Machine Learning in Your Data Warehouse

5. Part 2:Getting Started with Redshift ML

6. Chapter 4: Leveraging Amazon Redshift ML

7. Chapter 5: Building Your First Machine Learning Model

8. Chapter 6: Building Classification Models

9. Chapter 7: Building Regression Models

10. Chapter 8: Building Unsupervised Models with K-Means Clustering

11. Part 3:Deploying Models with Redshift ML

12. Chapter 9: Deep Learning with Redshift ML

13. Chapter 10: Creating a Custom ML Model with XGBoost

14. Chapter 11: Bringing Your Own Models for Database Inference

15. Chapter 12: Time-Series Forecasting in Your Data Warehouse

16. Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models

17. Index

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Creating a simple linear regression model using XGBoost

To build our simple linear regression problem, we are going to take a look at a dataset that includes predicting weight based on height. This dataset has only one independent variable, which is height in inches, and is used to predict weight in pounds. Since there is only one independent variable, we call this the simple linear regression model.

In this section, we will upload the data, analyze it, prepare it for training the model, and then lastly, we will create the model and run prediction queries using the function created by the model.

Uploading and analyzing the data

We are going to work on a height and weight dataset in this section.

The dataset contains 25,000 synthetic records of human heights and weights of 18-year-old participants. This data was generated based on a 1993 Growth Survey, which was conducted on 25,000 children from their birth to 18 years of age. The participants were recruited from Maternal...

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You're reading from Serverless Machine Learning with Amazon Redshift ML Create, train, and deploy machine learning models using familiar SQL commands

Table of Contents (19) Chapters

Creating a simple linear regression model using XGBoost

Uploading and analyzing the data

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Authors (4)

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