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

What is a decision tree?

A decision tree is a machine learning algorithm that belongs to the family of supervised learning algorithms. As such, they rely on training data to train them. From the features on the training data and the target variable, they can learn and build their knowledge base, based on which they can later take decisions on new data. Even though decision trees are mostly used in classification problems, they can be used very well in regression problems also. That is, they can be used to classify between discrete values (such as 'has disease' or 'no disease') or figure out continuous values (such as the price of a commodity based on some rules).

As mentioned earlier, there are two types of decision trees:

  • Decision trees for classification: These are the decision tree algorithms that are used in classification of categorical values, for example, figuring out whether a new customer could be a potential loan defaulter or not.

  • Decision trees for regression: These are the decision...