Walkthrough: Develop a predictive analytics solution for credit risk assessment in Azure Machine Learning

In this walkthrough, we take an extended look at the process of developing a predictive analytics solution in Machine Learning Studio. We develop a simple model in Machine Learning Studio, and then deploy it as an Azure Machine Learning web service where the model can make predictions using new data.

This walkthrough assumes that you've used Machine Learning Studio at least once before, and that you have some understanding of machine learning concepts. But it doesn't assume you're an expert in either.

If you've never used Azure Machine Learning Studio before, you might want to start with the tutorial, Create your first data science experiment in Azure Machine Learning Studio. That tutorial takes you through Machine Learning Studio for the first time. It shows you the basics of how to drag-and-drop modules onto your experiment, connect them together, run the experiment, and look at the results. Another tool that may be helpful for getting started is a diagram that gives an overview of the capabilities of Machine Learning Studio. You can download and print it from here: Overview diagram of Azure Machine Learning Studio capabilities.

If you're new to the field of machine learning in general, there's a video series that might be helpful to you. It's called Data Science for Beginners and it can give you a great introduction to machine learning using everyday language and concepts.


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The problem

Suppose you need to predict an individual's credit risk based on the information they gave on a credit application.

Credit risk assessment is a complex problem, but we can simplify it a bit for this walkthrough. We'll use it as an example of how you can create a predictive analytics solution using Microsoft Azure Machine Learning. To do this, we use Azure Machine Learning Studio and a Machine Learning web service.

The solution

In this detailed walkthrough, we start with publicly available credit risk data and develop and train a predictive model based on that data. Then we deploy the model as a web service so it can be used by others for credit risk assessment.

To create this credit risk assessment solution, we follow these steps:

  1. Create a Machine Learning workspace
  2. Upload existing data
  3. Create an experiment
  4. Train and evaluate the models
  5. Deploy the web service
  6. Access the web service

You can find a working copy of the experiment that we develop in this walkthrough in the Cortana Intelligence Gallery. Go to Walkthrough - Credit risk prediction and click Open in Studio to download a copy of the experiment into your Machine Learning Studio workspace.

This walkthrough is based on a simplified version of the sample experiment, Binary Classification: Credit risk prediction, also available in the Gallery.