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)
Part 1:Concepts of Machine Learning
Part 2: Machine learning algorithms and models with Qlik
Part 3: Case studies and best practices

Preparing and validating a model

In Chapter 1, we discovered some of the concepts for model validation and preparation. Qlik AutoML handles model selection automatically and provides us with comprehensive information to support the validation. We will consider model selection and validation in more detail in Chapter 7 and Qlik AutoML in Chapter 8. In this section, we will prepare for these chapters by summarizing the most important steps of model preparation and validation in Qlik. The following steps are written on Qlik AutoML point of view. When using the Advanced Analytics integration there might be small differences based on the selected technology (ie. R, Python, Azure ML Studio, AWS SageMaker, etc.).

General validation and preparation steps include the following:

  • Data preparation: Start by preparing your data for machine learning. Load your data into Qlik Sense, clean and preprocess it, handle missing values, and perform feature engineering if necessary. Qlik AutoML...