Implementing K-Means Clustering in SwiftUI

Learn how to implement ML algorithms such as K-Means in your SwiftUI Apps

Photo by Pierre Bamin on Unsplash

Want to add powerful machine learning algorithms to your SwiftUI App? Follow along!

SwiftUI is a framework for building user interfaces on Apple platforms. With its easy-to-use syntax, developers can create powerful and interactive user interfaces with less code. One area where SwiftUI excels is in data visualization. In this article, we will explore the concept of clustering and unsupervised learning in SwiftUI and demonstrate how to implement it in code.

Clustering is a technique used in unsupervised machine learning to group similar data points into clusters. This is useful when dealing with large amounts of data where it’s difficult to identify patterns or relationships. In SwiftUI, we can use the KMeans algorithm to perform clustering. This algorithm is widely used for clustering and is relatively simple to understand and implement.

In this article, we will learn how to implement K-means clustering in SwiftUI. Lets begin.

Import necessary libraries

import SwiftUI
import CoreML
import CreateML

Create a data set