Book Image

Haskell Data Analysis cookbook

By : Nishant Shukla
Book Image

Haskell Data Analysis cookbook

By: Nishant Shukla

Overview of this book

Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code. This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed.
Table of Contents (14 chapters)
13
Index

Implementing a k-Nearest Neighbors classifier

One simple way to classify an item is to look at only its neighboring data. The k-Nearest Neighbors algorithm looks at k items located closest to the item in question. The item is then classified as the most common classification of its k neighbors. This heuristic has been very promising for a wide variety of classification tasks.

In this recipe, we will implement the k-Nearest Neighbors algorithm using a k-d tree data structure, which is a binary tree with special properties that allow efficient representation of points in a k-dimensional space.

Imagine we have a web server for our hip new website. Every time someone requests a web page, our web server will fetch the file and present the page. However, bots can easily hammer a web server with thousands of requests, potentially causing a denial of service attack. In this recipe, we will classify whether a web request is being made by a human or a bot.

Getting ready

Install the KdTree, CSV, and...