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

Customer segmentation

Customers for any store either offline or online (that is, e-commerce) all exhibit different behaviors in terms of buying patterns. Some might buy in bulk, while others might buy lesser quantities of stuff but the transactions might be spread out throughout the year. Some might buy big items during festival times like Christmas and so on. Figuring out the buying patterns of the customers and grouping or segmenting the customers based on their buying patterns is of the utmost importance for the business owners, simply because it lays out the customers' needs in front of them and their importance. They could selectively market to the more important customers, thereby giving prime care and importance to the customers that generate maximum revenue for the stores.

Figuring out the buying patterns of the customers from historical data (of their purchase transactions) is easy for an online store as all the transaction data is readily available. Some approaches that people use...