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

Why machine learning works

During our illustration of training the ML model, we talked about generating a form of representation that is used to build our ML system. One thing to note is that this information or the data used to generate the form of representation is the data representation of our future source of information. Why do we need the data representation of our future source of information? In this sub-section, we will look into this and see how it helps in creating ML models.

Let's assume we are told to carry out research about product interest in a particular community. Imagine this community consists of a large number of people and we are only able to reach a few people – let's say 50% of the community population.

The idea is that, from the information obtained from 50% of the population, we should be able to generalize for the remaining 50% of the population. We assume this because a set of people from the same community or population are assumed...