Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

Sessionization


I will demonstrate the use of the complex or nested structures in the example of sessionization. In sessionization, we want to find the behavior of an entity, identified by some ID over a period of time. While the original records may come in any order, we want to summarize the behavior over time to derive trends.

We already analyzed web server logs in Chapter 1, Exploratory Data Analysis. We found out how often different web pages are accessed over a period of time. We could dice and slice this information, but without analyzing the sequence of pages visited, it would be hard to understand each individual user interaction with the website. In this chapter, I would like to give this analysis more individual flavor by tracking the user navigation throughout the website. Sessionization is a common tool for website personalization and advertising, IoT tracking, telemetry, and enterprise security, in fact anything to do with entity behavior.

Let's assume the data comes as tuples...