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

Naive Bayes algorithm

Have you ever wondered how your Gmail application automatically figures out that a certain message that you have received is spam and automatically puts it in the spam folder? Behind the email spam detector, a powerful machine learning algorithm is running, that automatically detects whether a particular email that you have received is spam or useful. This useful algorithm that runs behind the scenes and saves you wasted hours on deleting or checking these spam emails is Naive Bayes. As the name suggests, the algorithm is based on the bayes theorem. The algorithm is simple yet powerful, from the perspective of classification the algorithm figures out the probability of occurrence of each discrete class and it picks the value with the highest probability.

You might have wondered why the algorithm carries the word Naive in its name. It's because the algorithm makes some Naive assumptions that the features that are present in a dataset are independent of each other. Suppose...