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Scala Data Analysis Cookbook (new)

Scala Data Analysis Cookbook (new)

By : Manivannan
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Scala Data Analysis Cookbook (new)

Scala Data Analysis Cookbook (new)

4 (3)
By: 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 (9 chapters)
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8
Index

Supervised and unsupervised learning


If you are reading this book, you probably already know what supervised and unsupervised learning are, but for the sake of completion, let's briefly summarize what they mean. In supervised learning, we train the algorithms with labeled data. Labeled data is nothing but input data along with the outcome variable. For example, if our intention is to predict whether a website is about news, we would be preparing a sample dataset of website content with "news" and "not news" as labels. This dataset is called the training dataset.

With supervised learning, our end goal is to use the training dataset and come up with a function that maps our input variables to an output variable with least margin of error. We call input variables (or x variables) features or explanatory variables, and the output variable (also known as the y variable or label) the target or dependent variable. In the news website example, the text content in the website would be the input variable...

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