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

Machine learning problems/tasks

ML problems or tasks such as classification problems can be categorized into different groups based on how the model learns.

In this section, we will look into two of the most popular categories of ML problems:

  • Supervised learning
  • Unsupervised learning

First, we will look into supervised learning.

Supervised learning

In this category, the model learns under supervision. By supervision, we mean the model knows whether it is doing well based on the provided label. While training, we provide the model with a dataset containing a set of labels, which are used to correct and improve the model. With this, we can measure how well the model performs.

The following ML problems/tasks belong to this category:

  • Classification problems: In this type of problem, the model is made to classify an input to a set of discrete categories, such as classifying whether the image is a dog or a cat.
  • Regression problems: This involves...