Book Image

Learning Jupyter 5 - Second Edition

Book Image

Learning Jupyter 5 - Second Edition

Overview of this book

The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Node.js decision-tree package


The decision-tree package is an example of a machine learning package. It is available at https://www.npmjs.com/package/decision-tree. The package is installed by using the following command:

npm install decision-tree

We need a dataset to use for training/developing our decision tree. I am using the car MPG dataset from the following web page: https://alliance.seas.upenn.edu/~cis520/wiki/index.php?n=Lectures.DecisionTrees. It did not seem to be available directly, so I copied it into Excel and saved it as a local CSV.

The logic for machine learning is very similar:

  • Load our dataset
  • Split into a training set and a testing set
  • Use the training set to develop our model
  • Test the mode on the test set

Note

Typically, you might use two-thirds of your data for training and one-third for testing.

Using the decision-tree package and the car-mpgdataset, we would have a script similar to the following:

//Import the modules 
var DecisionTree = require('decision-tree'); 
var fs = require...