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

Chapter 8. Ensembling on Big Data

Have you used a Kinect while playing video games on Microsoft Xbox? It's so smooth how it detects your motion while you are playing games. It enables users to control and interact with their game without using any external device like a game controller. But how does it do that? How does the device detect the user's motion from the camera and predict the command that the motion suggested? Some users on different forums have claimed that a powerful random forest machine learning algorithm runs behind it and the link for the same is Though I am myself not sure how true this claim is, this example at least demonstrates at what scale and level this powerful machine learning algorithm has the potential to be used. Random forests are perhaps one of the best machine learning algorithms because of the accuracy they bring in the predicted results and because of their implicit feature...