Book Image

Scala Data Analysis Cookbook

By : Arun Manivannan
Book Image

Scala Data Analysis Cookbook

By: Arun Manivannan

Overview of this book

This book will introduce you to the most popular Scala tools, libraries, and frameworks through practical recipes around loading, manipulating, and preparing your data. It will also help you explore and make sense of your data using stunning and insightfulvisualizations, and machine learning toolkits. Starting with introductory recipes on utilizing the Breeze and Spark libraries, get to grips withhow to import data from a host of possible sources and how to pre-process numerical, string, and date data. Next, you’ll get an understanding of concepts that will help you visualize data using the Apache Zeppelin and Bokeh bindings in Scala, enabling exploratory data analysis. iscover how to program quintessential machine learning algorithms using Spark ML library. Work through steps to scale your machine learning models and deploy them into a standalone cluster, EC2, YARN, and Mesos. Finally dip into the powerful options presented by Spark Streaming, and machine learning for streaming data, as well as utilizing Spark GraphX.
Table of Contents (14 chapters)
Scala Data Analysis Cookbook
About the Author
About the Reviewers

Feature reduction using principal component analysis

Quoting the curse of dimensionality (, large number of features are computationally expensive. One way of reducing the number of features is by manually choosing and ignoring certain features. However, identification of the same features (represented differently) or highly correlated features is laborious when we have a huge number of features. Dimensionality reduction is aimed at reducing the number of features in the data while still retaining its variability.

Say, we have a dataset of housing prices and there are two features that represent the area of the house in feet and meters; we can always drop one of these two. Dimensionality reduction is very useful when dealing with text where the number of features easily runs into a few thousands.

In this recipe, we'll be looking into Principal Component Analysis (PCA) as a means to reduce the dimensions of data that is meant for both supervised...