How to Implement Linear Regression in JavaScript

Learn how to Implement Linear Regression in JavaScript using ML.js

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Linear Regression is a machine learning technique used for modeling the relationship between a dependent variable and one or more independent variables. It is a popular algorithm for making predictions and is widely used in various fields such as finance, healthcare, and retail. In this article, we will see how to implement Linear Regression in JavaScript using the ML.js library.

ML.js is a popular open-source library for machine learning in JavaScript. It provides a simple and easy-to-use interface for building and training machine learning models. In this tutorial, we will be using ML.js version 0.25.1.

Lets’ begin.

Before we start implementing Linear Regression, let’s first install ML.js in our project using npm.

npm install ml.js

Next, we need to import the library in our project.

const ml = require('ml.js');

Once we have installed and imported the library, we can start by creating a Linear Regression model.

const model = new ml.LinearRegression();

Next, we need to provide the training data for the model. The training data is an array of objects, each object…