Book Image

Machine Learning with Qlik Sense

By : Hannu Ranta
Book Image

Machine Learning with Qlik Sense

By: Hannu Ranta

Overview of this book

The ability to forecast future trends through data prediction, coupled with the integration of ML and AI, has become indispensable to global enterprises. Qlik, with its extensive machine learning capabilities, stands out as a leading analytics platform enabling businesses to achieve exhaustive comprehension of their data. This book helps you maximize these capabilities by using hands-on illustrations to improve your ability to make data-driven decisions. You’ll begin by cultivating an understanding of machine learning concepts and algorithms, and build a foundation that paves the way for subsequent chapters. The book then helps you navigate through the process of framing machine learning challenges and validating model performance. Through the lens of Qlik Sense, you'll explore data preprocessing and analysis techniques, as well as find out how to translate these techniques into pragmatic machine learning solutions. The concluding chapters will help you get to grips with advanced data visualization methods to facilitate a clearer presentation of findings, complemented by an array of real-world instances to bolster your skillset. By the end of this book, you’ll have mastered the art of machine learning using Qlik tools and be able to take your data analytics journey to new heights.
Table of Contents (17 chapters)
1
Part 1:Concepts of Machine Learning
6
Part 2: Machine learning algorithms and models with Qlik
12
Part 3: Case studies and best practices

Building a model in an on-premises environment using the Advanced Analytics connection

In Chapter 5, we prepared an environment for R and Python using the Advanced Analytics connection with Qlik. In this chapter, we are going to utilize this same environment. This exercise will use R specifically.

In general, there are two ways to utilize the Advanced Analytics connection with Qlik applications. These are the following:

  • Live connection: A live connection interacts with the third-party machine learning environment from the user interface while the user interacts with the application. A live connection enables what-if scenarios, simulations, and similar use cases. It is best for light models that do not require extensive training. The idea behind live connections is explained in the following diagram:
Figure 7.1: Advanced Analytics connection

Figure 7.1: Advanced Analytics connection

  • Load time connection: A load time connection is a one-time prediction model run that takes place...