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Tutorial – Generating and visualizing prediction data

This tutorial teaches you how to use Qlik AutoML to train and deploy a machine learning model for making predictions. It also teaches you how you can visualize the prediction data in a Qlik Sense app.

We will consider the scenario of customer churn, a classic example of a binary classification problem. The goal is to be able to reliably predict whether a customer will cancel their subscription or remain a subscriber to a service. There are only two outcomes in this type of problem: true or false (churned, or not churned).

To approach this machine learning problem, we will start by processing a set of data for which we already know the outcome, then apply statistical modeling created from that data to new data we would like to predict the outcomes for.

You will begin this tutorial by creating an experiment. From there, you will refine and deploy the experiment into a machine learning model. This model will be used to create predictions, which can be shown in the form of visualizations in a Qlik Sense app.

What you will learn

Once you have completed this tutorial, you will understand the different steps involved in creating and configuring an experiment. You will also learn how to interpret model scores. Finally, you will be able to deploy a machine learning model and will understand how your predictions data can be used to generate compelling Qlik Sense visualizations in Qlik Cloud Analytics.

Who should complete this tutorial

This tutorial is designed for users who want an introduction to automated machine learning and data visualization in Qlik Cloud Analytics. Some basic knowledge of machine learning and Qlik Sense is helpful, but not required.

To complete this tutorial, you need the following:

If you are not able to view or create ML resources, this likely means that you do not have the required roles, entitlements, or permissions. Contact your tenant admin for further information.

For more information, see Who can work with Qlik AutoML.

What you need to do before you start

Download this package and unzip it on your desktop:

AutoML tutorial

The package contains:

  • The two data files needed to complete the tutorial.

  • A sample copy of the Qlik Sense app that you will build during the tutorial. This is provided in case you would like to get quicker hands-on experience with predictive app development. For more information, see Alternative workflow: Upload pre-configured app.

The training dataset contains information about customers whose deadline for renewal has passed, and have made the decision to churn or remain subscribed to the service.

The apply dataset contains details about a new set of customers whose renewal date has not yet passed. It has not yet been determined whether or not these customers will cancel their service. The goal, with this tutorial, is to predict what this set of customers will do, with the hope that we can decrease the likelihood that they will churn.

  1. Open the Qlik Cloud Analytics hub.

  2. Click Add new > Dataset, and then select Upload data file.

  3. Drag the Customer churn data - training.csv file to the upload dialog.

  4. Next, drag the Customer churn data - apply.csv file to the upload dialog.

  5. Select a space. It can be your personal space or a shared space if you want other users to be able to access this data.

  6. Click Upload.

Now that your datasets are uploaded, you can proceed to creating an experiment.

Lessons in this tutorial

The topics in this tutorial are designed to be completed in sequence. However, you can step away and return at any time.

Further reading and resources

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