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

Defining a machine learning problem

Defining a machine learning problem involves identifying a specific business challenge or analytical objective that can be addressed using machine learning techniques. The process of defining a problem can be divided into a few key steps:

  1. Understand the business objective: Start by gaining a clear understanding of the overall business objective or problem that needs to be solved. This could be improving customer retention, optimizing pricing strategies, predicting equipment failures, or identifying potential fraud cases, among others.
  2. Identify the key problem or challenge: Once the business objective is defined, identify the specific problem or challenge that needs to be addressed using machine learning. This involves understanding the key pain points, limitations, or gaps in the current process or system that can be improved through machine learning.
  3. Define the scope and boundaries: Clearly define the scope and boundaries of the...