As mentioned earlier, the application we will be creating is a login anomaly detector. Given a set of attributes relating to the login, the application will use that data to find anomalies such as unusual login times. As with other applications, this is not meant to power the next ML login anomaly detection product; however, it will show you how to use anomaly detection in ML.NET.
As with previous chapters, the completed project code, sample dataset, and project files can be downloaded here: https://github.com/PacktPublishing/Hands-On-Machine-Learning-With-ML.NET/tree/master/chapter06.
Exploring the project architecture
Building on the project architecture and code we created in previous chapters, the bulk of the changes in this example are in the training of the model.
In the following screenshot, you will find the Visual Studio Solution Explorer view of the project. The new additions to the solution are the LoginHistory and LoginPrediction files...
