Predictive Modeling and Forecasting with SwiftUI

Learn how to use SwiftUI for Predictive Modeling and Forecasting

ML Musings


Photo by Caspar Camille Rubin on Unsplash

Predictive modeling and forecasting are crucial components of decision-making in various industries such as finance, marketing, and healthcare. With the advent of machine learning and artificial intelligence, it is now possible to make accurate predictions about future events and trends. In this article, we will explore how to use SwiftUI to build predictive models and make forecasts, including code snippets.

SwiftUI is Apple’s modern framework for building user interfaces for iOS, iPadOS, and macOS. It provides a clean and intuitive way of building and designing user interfaces, and it also integrates well with machine learning libraries such as Core ML and Create ML.

Create ML is a framework that makes it easy to build machine learning models without the need for extensive coding knowledge. It provides a simple drag-and-drop interface that can be used to train and evaluate machine learning models. Create ML supports a variety of machine learning algorithms, including linear regression, decision trees, and random forests, making it a great choice for building predictive models.

This is how to train a linear regression model using Create ML:

import CreateML

let data = try MLDataTable(contentsOf: URL(fileURLWithPath: "data.csv"))
let (trainingData, testingData) = data.randomSplit(by: 0.8, seed: 5)

let model = try MLRegressor(trainingData: trainingData, targetColumn: "Target")
let evaluation = model.evaluation(on: testingData)

let metrics = [
"Mean Absolute Error": evaluation.meanAbsoluteError,
"Root Mean Squared Error": evaluation.rootMeanSquaredError,
"R^2": evaluation.rSquared,

for (name, value) in metrics {
print("\(name): \(value)")

try model.write(to: URL(fileURLWithPath: "model.mlmodel"))

Once the model is trained, it can be used to make predictions by passing new data to it. In SwiftUI, we can easily integrate the trained model into our user interface by using the Core ML framework. This allows us to provide real-time predictions and forecasts to our users, making it easier for them to make informed decisions.



ML Musings

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