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

Building a simple regression model with TensorFlow.js

In the previous chapter, Chapter 9, Basics of Machine Learning, you were introduced to the basics of ML, especially the theoretical aspect of regression and classification models. In this section, we'll show you how to create and train a regression model using tfjs LayerAPI. Specifically, by the end of this section, you'll have a regression model that can predict sales prices from supermarket data.

Setting up your environment locally

Before building the regression model, you have to set up your environment locally. In this section, we'll be working in a Node.js environment. This means that we'll be using the node version of TensorFlow.js and Danfo.js.

Follow the steps here to set up your environment:

  1. In a new work directory, create a folder for your project. We will create one called sales_predictor, as demonstrated in the following code snippet:
    mkdir sales_predictor
    cd sales_predictor
  2. Next...