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Power BI Machine Learning and OpenAI

You're reading from  Power BI Machine Learning and OpenAI

Product type Book
Published in May 2023
Publisher Packt
ISBN-13 9781837636150
Pages 308 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Greg Beaumont Greg Beaumont
Profile icon Greg Beaumont

Table of Contents (21) Chapters

Preface Part 1: Data Exploration and Preparation
Chapter 1: Requirements, Data Modeling, and Planning Chapter 2: Preparing and Ingesting Data with Power Query Chapter 3: Exploring Data Using Power BI and Creating a Semantic Model Chapter 4: Model Data for Machine Learning in Power BI Part 2: Artificial Intelligence and Machine Learning Visuals and Publishing to the Power BI Service
Chapter 5: Discovering Features Using Analytics and AI Visuals Chapter 6: Discovering New Features Using R and Python Visuals Chapter 7: Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service Part 3: Machine Learning in Power BI
Chapter 8: Building Machine Learning Models with Power BI Chapter 9: Evaluating Trained and Tested ML Models Chapter 10: Iterating Power BI ML models Chapter 11: Applying Power BI ML Models Part 4: Integrating OpenAI with Power BI
Chapter 12: Use Cases for OpenAI Chapter 13: Using OpenAI and Azure OpenAI in Power BI Dataflows Chapter 14: Project Review and Looking Forward Index Other Books You May Enjoy

Identifying features in Power BI using a report

Now that you’ve built out the base queries for Predict Damage, Predict Size, and Predict Height, you can add additional features to evaluate in each of the queries that will be used for Power BI ML. Features to evaluate the ML models will be added to each ML query. Building the ML models in Power BI will allow you to narrow down the list of features to those that are most useful. In order to make the chapter easy to read, you can take a look at potential new features one by one as they pertain to the three queries for Power BI ML. When training and testing your ML models in Power BI, the predictive value of each feature will be evaluated when you build the ML models. Therefore, if a feature is in question, you’re better off keeping it in the query and then removing it later on if it turns out to provide little value.

Note that if you don’t want to build all of these Power BI report pages as part of your journey...

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