| title | description | keywords | services | documentationcenter | author | manager | editor | ms.assetid | ms.service | ms.workload | ms.tgt_pltfrm | ms.devlang | ms.topic | ms.date | ms.author |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A predictive solution for credit risk with Machine Learning | Microsoft Docs |
A detailed walkthrough showing how to create a predictive analytics solution for credit risk assessment in Azure Machine Learning Studio. |
credit risk, predictive analytics solution,risk assessment |
machine-learning |
garyericson |
jhubbard |
cgronlun |
43300854-a14e-4cd2-9bb1-c55c779e0e93 |
machine-learning |
data-services |
na |
na |
get-started-article |
09/16/2016 |
garye |
Walkthrough: Develop a predictive analytics solution for credit risk assessment in Azure Machine Learning
Suppose you need to predict an individual's credit risk based on the information they give on a credit application.
Credit risk assessment is a complex problem, of course, but let's simplify the parameters of the question a bit. Then, we can use it as an example of how you can use Microsoft Azure Machine Learning with Machine Learning Studio and the Machine Learning web service to create such a predictive analytics solution.
In this detailed walkthrough, we'll follow the process of developing a predictive analytics model in Machine Learning Studio and then deploying it as an Azure Machine Learning web service. We'll start with publicly available credit risk data, develop and train a predictive model based on that data, and then deploy the model as a web service that can be used by others for credit risk assessment.
[!INCLUDE machine-learning-free-trial]
Tip
To download and print a diagram that gives an overview of the capabilities of Machine Learning Studio, see Overview diagram of Azure Machine Learning Studio capabilities.
To create a credit risk assessment solution, we'll follow these steps:
- Create a Machine Learning workspace
- Upload existing data
- Create a new experiment
- Train and evaluate the models
- Deploy the web service
- Access the web service
This walkthrough is based on a simplified version of the Binary Classfication: Credit risk prediction sample experiment in the Cortana Intelligence Gallery.