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

Linear regression example

In this example, we will create a linear regression model to predict the value of a house in the California area. Let’s begin by getting familiar with the dataset. We will use a common California house values dataset. This is a collection of data related to residential real estate properties in various regions of California, USA. It is commonly used in machine learning and data analysis tasks for predicting house prices based on various features.

The dataset we will use contains the following fields:

  • medianIncome: The median income of households in a specific block.
  • housingMedianAge: The median age of houses in a block.
  • totalRooms: The total number of rooms in the houses in a block.
  • totalBedrooms: The total number of bedrooms in the houses in a block.
  • population: The total population of the block.
  • households: The total number of households (a group of people residing within a home unit) within a block.
  • latitude: The...