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Power BI Machine Learning and OpenAI

You're reading from   Power BI Machine Learning and OpenAI Explore data through business intelligence, predictive analytics, and text generation

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781837636150
Length 308 pages
Edition 1st Edition
Languages
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Author (1):
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Greg Beaumont Greg Beaumont
Author Profile Icon Greg Beaumont
Greg Beaumont
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Data Exploration and Preparation
2. Chapter 1: Requirements, Data Modeling, and Planning FREE CHAPTER 3. Chapter 2: Preparing and Ingesting Data with Power Query 4. Chapter 3: Exploring Data Using Power BI and Creating a Semantic Model 5. Chapter 4: Model Data for Machine Learning in Power BI 6. Part 2: Artificial Intelligence and Machine Learning Visuals and Publishing to the Power BI Service
7. Chapter 5: Discovering Features Using Analytics and AI Visuals 8. Chapter 6: Discovering New Features Using R and Python Visuals 9. Chapter 7: Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service 10. Part 3: Machine Learning in Power BI
11. Chapter 8: Building Machine Learning Models with Power BI 12. Chapter 9: Evaluating Trained and Tested ML Models 13. Chapter 10: Iterating Power BI ML models 14. Chapter 11: Applying Power BI ML Models 15. Part 4: Integrating OpenAI with Power BI
16. Chapter 12: Use Cases for OpenAI 17. Chapter 13: Using OpenAI and Azure OpenAI in Power BI Dataflows 18. Chapter 14: Project Review and Looking Forward 19. Index 20. Other Books You May Enjoy

Building and training a binary prediction ML model in Power BI

You’re finally ready to build and train your first ML model in Power BI! We will start with a binary prediction model to predict whether damage happened when wildlife struck an airplane. As discussed in Chapter 1, a binary prediction model will make a yes/no prediction for a given row of data containing columns that are the predictive features. The query that you built in Chapter 7 is in your ML Queries dataflow and is named Predict Damage.

You'll build your prediction model as follows:

  1. Create a new dataflow in your Power BI workspace by selecting New | Dataflow.
  2. Select Link tables from other dataflows.
  3. Ensure you are signed in to your organizational account and select Next.
  4. Expand your Power BI workspace folder, expand the ML Queries dataflow, select Predict Damage, and click Transform data.
  5. Save and close the new dataflow.
  6. Name the new dataflow Predict Damage ML.
  7. Refresh...
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