Book Image

Big Data Analytics with Java

Book Image

Big Data Analytics with Java


Overview of this book

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
Table of Contents (21 chapters)
Big Data Analytics with Java
About the Author
About the Reviewers
Customer Feedback
Free Chapter
Big Data Analytics with Java
Ensembling on Big Data
Real-Time Analytics on Big Data


Imagine that a group of friends are deciding which movie they want to see together. For this, they select their movie of choice from a set of, say, five or six movies. At the end, all their votes are collected and read. The movie with the maximum votes is picked and watched. What just happened is a real-life example of the ensembling approach. Basically, multiple entities act on a problem and give their selection out of a collection of discrete choices (in the case of a classification problem). The selection that was suggested by the maximum number of entities is chosen as the predicted choice.

This explanation was a general approach to ensembling. From the perspective of machine learning, it just means that multiple machine learning programs act on a problem that can be either of type classification or regression. The output from each machine learning algorithm is collected. The results from all the algorithms are then analyzed with different approaches like voting, averaging...