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

Introduction to machine learning

In this section, we will introduce ML by using a simple analogy that might serve as common ground to establish our explanation. We will also see why and how ML works.

We will start the section by using an information transfer system as a simple analogy for ML.

A simple analogy of a machine learning system

I remember a time I was in a Twitter Space involving a discussion about ML and some other cool topics. I was told to give a brief introduction to ML for those who were interested but didn't fully get the gist.

The majority of people in this Twitter Space were software engineers with no previous knowledge of math, statistics, or any topic related to ML, and I came across instances where people failed to understand the terminology of the topic due to the addition of some technical terms.

This section aims to explain ML by avoiding too many technical terms and finding a common ground through which ML can be explained.

Using an information...