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

Customer churn example

In our second example, we will create a binary model to predict customer churn for a bank. We are going to use a dataset that contains the following fields:

  • customer_id: A unique identifier for each customer.
  • credit_score: A numerical representation of a customer’s creditworthiness.
  • country: The country where the customer resides.
  • gender: The gender of the customer.
  • age: The age of the customer.
  • tenure: The duration of the customer’s relationship with the company.
  • balance: The current balance in the customer’s account.
  • products_number: The number of products the customer has brought from the company.
  • credit_card: A binary indicator showing whether the customer holds a credit card with the company.
  • active_member: A binary indicator indicating whether the customer is currently an active member of the company.
  • estimated_salary: An approximate estimation of the customer’s salary.
  • churn...