Challenge: Analyzing Clustered Data

Explore how to use ML.NET to analyze clustered data in housing market examples. Learn to train clustering models, interpret cluster IDs, assign human-readable category labels such as high-end and cheap, and improve model output clarity by customizing cluster descriptions.

We'll cover the following...

Problem statement

Using the coding playground at the end of this lesson, perform the tasks highlighted in the “Problem statement” section.

Problem statement

In the playground below, we have a full end-to-end process of training and using a clustering model against housing market data. We tell the model to create three distinct clusters and then feed the data to it.

Once the model is trained, we pass an example of a house to it to predict what cluster it would belong in and print this information in the console. We also print distance measures. We then print the distributions of the training data ...

1.Introduction to ML.NET

2.Machine Learning Fundamentals

3.Selecting a Problem for Machine Learning

4.Built-In Supervised Learning Tasks in ML.NET

5.Built-In Unsupervised Learning Tasks in ML.NET

6.Deep Learning and Neural Networks

7.Automating Machine Learning Tasks with AutoML

8.Saving and Consuming Machine Learning Models

Mini Project

9.Wrapping Up

Project

10.Appendix

Challenge: Analyzing Clustered Data

Problem statement