How to identify clusters and name them
Unsupervised algorithms for clustering
Selecting the number of clusters
Learn how to identify clusters and name them
In this course we will study in details the machine learning aspect of our Geographic clustering sample project.
The aim of this project is to segment neighborhoods of Manhattan and Paris based on the type of locations and events that are present. Based on data from Open Street Map and Foursquare, we aggregate points of interest by type and count how many venues are present in each neighborhood.
This data serves as the basis for a clustering algorithm that will classify neighborhoods by type.
If you want to see how we achieve this, especially regarding data ingestion and preparation you can find details in the project’s description.
Another interesting aspect of this project is data visualization. You can see on the dashboard a map that uses the built-in chart engine, as well as a custom built web app. If you are interested in building web apps featuring maps, refer to our tutorial using SFPD data to build a crime map of the city of San Francisco.