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

Cleaning and preparing data

Data preparation is a crucial step in machine learning because the quality, relevance, and suitability of the data used for model training directly impact the accuracy, reliability, and effectiveness of the resulting machine learning models.

General data preparation steps include the following:

  • Removing null values
  • Removing columns that are not needed
  • Encoding (for example, the one-hot encoding that we used in some of the examples in Chapter 2)
  • Feature scaling
  • Splitting into test and training datasets
  • Setting correct data types
  • Removing duplicates
  • Correcting data errors
  • Removing outliers

Those steps that are automatically taken care of by Qlik AutoML are shown in bold in the preceding list. The rest of the steps can be done in Qlik Sense.

Let’s take a closer look at some of these steps using examples.

Example 1 – one-hot encoding

Let’s assume that we have the following dataset...