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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

Exploring data with Python visuals

In addition to R, Power BI also supports Python queries and visuals. Python is a very popular language that is also frequently used by data scientists. Per the requirements at the beginning of this chapter, you’ll need to install Python on your local machine for Power BI Desktop: https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-python-visuals.

In the FAA Wildlife Strike data, Height and Speed are both fields that can be recorded for reports. Height is a measure in feet from the ground at which an incident happened, while speed is a measure of the speed the aircraft was traveling when it was struck by wildlife. You will take a look at both of these metrics using Python histograms so that you can compare the distribution of those values when selecting different filters.

You will follow these steps:

  1. Preparing the data for the Python histogram.
  2. Building the Python histogram visualization and add it to your report...
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