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

Scatter plots

One of the most useful charts for data analysis are scatter plots. These charts are heavily used in data analysis, especially in clustering techniques, classification, and so on. In this chart, we pick up data points from the data and plot them as dots on a chart. In simple terms, scatter plots are just data points plotted on x and y axes as shown below. This helps us figure out where the data is more concentrated or in which direction the data is actually flowing.

This is very useful for showing trends, clusters, or patterns, for example, we can figure out which data points lie closer to each other. As an example, let's see a scatter plot next that shows the price of houses versus their living area.

As you can see from the graph, you will generally see that prices are going in the upward direction as the area is increasing. Of course, there are other parameters for the price to consider too; however, for the sake of this graph, we only used the living area. You can also see...