Summary
We first got an idea of how clustering works. We built the simplest possible model for a tiny dataset. We ran the model a few times and evaluated the performance and outcomes for each of the numbers of clusters that we chose.
We then explored the elbow technique to evaluate different clusters and saw how we might discover the point of diminishing returns, where not much improvement is achieved by adding new clusters. With that knowledge, we used the same technique for clustering countries by a metric with which most of us are familiar and got firsthand experience in how it might work on real data.
After that, we planned an interactive KMeans app and explored two techniques for preparing data before running our model. We mainly explored imputing missing values and scaling data.
This gave us enough knowledge to get our data in a suitable format for us to create our interactive app, which we did at the end of the chapter.
We next explored advanced features of Dash...
