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Machine Learning with Qlik Sense

You're reading from   Machine Learning with Qlik Sense Utilize different machine learning models in practical use cases by leveraging Qlik Sense

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781805126157
Length 242 pages
Edition 1st Edition
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Author (1):
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Hannu Ranta Hannu Ranta
Author Profile Icon Hannu Ranta
Hannu Ranta
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Table of Contents (17) Chapters Close

Preface 1. Part 1:Concepts of Machine Learning
2. Chapter 1: Introduction to Machine Learning with Qlik FREE CHAPTER 3. Chapter 2: Machine Learning Algorithms and Models with Qlik 4. Chapter 3: Data Literacy in a Machine Learning Context 5. Chapter 4: Creating a Good Machine Learning Solution with the Qlik Platform 6. Part 2: Machine learning algorithms and models with Qlik
7. Chapter 5: Setting Up the Environments 8. Chapter 6: Preprocessing and Exploring Data with Qlik Sense 9. Chapter 7: Deploying and Monitoring Machine Learning Models 10. Chapter 8: Utilizing Qlik AutoML 11. Chapter 9: Advanced Data Visualization Techniques for Machine Learning Solutions 12. Part 3: Case studies and best practices
13. Chapter 10: Examples and Case Studies 14. Chapter 11: Future Direction 15. Index 16. Other Books You May Enjoy

Preprocessing and Exploring Data with Qlik Sense

Data processing and exploration are essential steps in the data analysis process. They involve transforming raw data into meaningful insights, identifying patterns, and gaining a deeper understanding of the data. Qlik Sense is a powerful data analytics tool that allows users to easily process and explore data. With its data cleansing, transformation, and integration capabilities, Qlik Sense makes it easy to prepare data for analysis and produce a good dataset to be used for machine learning models.

This chapter will introduce data preprocessing and exploration with Qlik Sense. We will cover the following main topics:

  • Creating a data model using the data manager
  • Creating a data model and altering data with Qlik script
  • Validating data
  • Utilizing data lineage and data catalogs
  • Exploring data and finding insights using Insight Advisor
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