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

Best practices with Qlik AutoML

There are some general guidelines and best practices when working with Qlik AutoML. Following these practices and principles will make it easier to get accurate results and handle the machine learning project flow. The general principles include the following:

  • Define the problem: Clearly define the problem you are trying to solve with Qlik AutoML. Identify the variables you want to predict, and understand the available data. This is one of the most important best practices.
  • Prepare and clean the data: Ensure that your data is in a format suitable for analysis. This may involve cleaning missing values, handling outliers, transforming variables, cleaning duplicates, and making sure the data is well formatted. This is typically the most time-consuming part of machine learning projects.
  • Feature engineering: Explore and create meaningful features from your raw data. Qlik AutoML can automate some feature engineering tasks, but it’s still...