Book Image

Jupyter for Data Science

By : Dan Toomey
Book Image

Jupyter for Data Science

By: Dan Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Nearest neighbor estimator


Using nearest neighbor, we have an unclassified object and a set of objects that are classified. We then take the attributes of the unclassified object, compare against the known classifications in place, and select the class that is closest to our unknown. The comparison distances resolve to Euclidean geometry computing the distances between two points (where known attributes fall in comparison to the unknown's attributes).

Nearest neighbor using R

For this example, we are using the housing data from ics.edu. First, we load the data and assign column names:

housing <- read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data") 
colnames(housing) <- c("CRIM", "ZN", "INDUS", "CHAS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PRATIO", "B", "LSTAT", "MDEV") 
summary(housing)

We reorder the data so the key (the housing price MDEV) is in ascending order:

housing <- housing[order(housing$MDEV),] 

Now, we can split the data into a training...