Book Image

Building Data-Driven Applications with Danfo.js

By : Rising Odegua, Stephen Oni
Book Image

Building Data-Driven Applications with Danfo.js

By: Rising Odegua, Stephen Oni

Overview of this book

Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications. Starting with an overview of modern JavaScript, you’ll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You’ll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you’ll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you’ll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js. By the end of this app development book, you’ll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser.
Table of Contents (18 chapters)
1
Section 1: The Basics
3
Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook
10
Section 3: Building Data-Driven Applications

Installing and using TensorFlow.js

As we mentioned earlier, TensorFlow.js can be installed and run in both the browser and Node.js environment. In the following paragraphs, we'll show you how to achieve this, starting off with the browser.

Setting up TensorFlow.js in the browser

There are two ways of installing TensorFlow.js in the browser. These are outlined here:

  • Via script tags
  • Using package managers such as Node Package Manager (npm) or Yarn

Installing via script tags

Installing TensorFlow.js via a script tag is easy. Just place the script tag in the header file of your HyperText Markup Language (HTML) file, as shown in the following code snippet:

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>

To confirm that TensorFlow.js is installed, open the HTML file in the browser, and check the network tabs. You should see the name tf.min.js and a status code of 200, as shown in the following...