This is the first in a series of articles on implementing Machine Learning scenarios in UWP apps. All of these are cross platform Open Source technologies, all of these are written in C#, all of these are free, and all of these can be used on the UWP platform, albeit with some -hopefully temporary- restrictions. Currently the large majority of the online samples on ML.NET are straightforward console apps. That's fine if want to learn the API, but we want to figure out how ML.NET behaves in a more hostile enterprise-ish environment – where calculations should not block the UI, data should be visualized in sexy graphs, and architectural constraints may apply. Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends.

In the previous post "Beginners Guide: Apache Spark Machine Learning Scenario With A Large Input Dataset" we discussed the process of creating predictive model with 34 gigabytes of input data using Apache Spark. I received a request for the Python code as a solution instead of Scala. This is exactly what I will do in this post. Python solution looks similar to the last Scala solution because when you look "under the hood" you have the same Spark library and engine. Because of this fact, I don't anticipate any significant performance change.