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

Creating and monitoring a machine learning model with Qlik AutoML

In this section, we will create an actual implementation using Qlik AutoML. We will utilize the famous Iris dataset that we have already used in this book. The data preparation part for Iris dataset is already done, so we can jump into the model training and experiment part directly.

Note

You can find the datasets used in this example in the GitHub repository for this book.

Note

Only users with Professional entitlement can create experiments.This is a limitation at the license level.

Let’s assume that we have already uploaded the iris dataset into our cloud tenant. Now, we will start to define a business question. This question defines what we would like to achieve from our machine learning model.

As we know, the Iris dataset consists of measurements of four features of three different species of Iris flowers. These features are as follows:

  • Sepal length: The length of the sepal, which...