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Extending Power BI with Python and R

You're reading from   Extending Power BI with Python and R Ingest, transform, enrich, and visualize data using the power of analytical languages

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801078207
Length 558 pages
Edition 1st Edition
Languages
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Author (1):
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Luca Zavarella Luca Zavarella
Author Profile Icon Luca Zavarella
Luca Zavarella
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Table of Contents (22) Chapters Close

Preface 1. Section 1: Best Practices for Using R and Python in Power BI
2. Chapter 1: Where and How to Use R and Python Scripts in Power BI FREE CHAPTER 3. Chapter 2: Configuring R with Power BI 4. Chapter 3: Configuring Python with Power BI 5. Section 2: Data Ingestion and Transformation with R and Python in Power BI
6. Chapter 4: Importing Unhandled Data Objects 7. Chapter 5: Using Regular Expressions in Power BI 8. Chapter 6: Anonymizing and Pseudonymizing Your Data in Power BI 9. Chapter 7: Logging Data from Power BI to External Sources 10. Chapter 8: Loading Large Datasets beyond the Available RAM in Power BI 11. Section 3: Data Enrichment with R and Python in Power BI
12. Chapter 9: Calling External APIs to Enrich Your Data 13. Chapter 10: Calculating Columns Using Complex Algorithms 14. Chapter 11: Adding Statistics Insights: Associations 15. Chapter 12: Adding Statistics Insights: Outliers and Missing Values 16. Chapter 13: Using Machine Learning without Premium or Embedded Capacity 17. Section 3: Data Visualization with R in Power BI
18. Chapter 14: Exploratory Data Analysis 19. Chapter 15: Advanced Visualizations 20. Chapter 16: Interactive R Custom Visuals 21. Other Books You May Enjoy

Embedding training code in Power Query

One of the easiest solutions to train a machine learning model is to write the code needed to do so directly in Power Query, right after importing a dataset on which you will build the model.

Training a model on a fairly large dataset typically takes quite a bit of time to complete. As you embed the code in Power Query, it will run every time the data is refreshed, and this may result in a non-negligible delay in getting the data online. Hence, the following applies:

Important Note

This solution is recommended when you are certain that the time required to complete the model training is acceptable.

Let's now look at an example of how to write some training code using PyCaret.

Training and using ML models with PyCaret

Let's take the Titanic disaster dataset to train a machine learning model. Specifically, we want to create a model that predicts whether a passenger survives (the Survived column) based on their attributes...

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