#### 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.
Big Data Analytics with Java
Credits
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Big Data Analytics with Java
First Steps in Data Analysis
Data Visualization
Basics of Machine Learning
Regression on Big Data
Naive Bayes and Sentiment Analysis
Decision Trees
Ensembling on Big Data
Recommendation Systems
Clustering and Customer Segmentation on Big Data
Massive Graphs on Big Data
Real-Time Analytics on Big Data
Deep Learning Using Big Data
Index

## Box plots

Another very useful type of charts is box chart. Before looking into box charts, let's revise some simple mathematical concepts next. You can skip this page and directly go to the chart as well.

Suppose you have an array of numbers as shown here:

`int[] numbersArr = { 5, 6, 8, 9, 2 };`

Now, from this array, we have to find the following simple math stats:

• Min: This is just the minimum value from the array and as you can see it is `2`

• Max: This is the maximum value from the array and this as you can see, is `9`

• Mean: This is the mean value of the array elements. Mean is nothing but the average value. Hence in this case it is the sum of array elements divided by the number of elements in the array.

`	(5 + 6 + 8 + 9 + 2) / 5 = 6`
• Median: If we sort the preceding array in ascending order, the values would be:

`int[ ] numbersArr = ( 2, 5, 6, 8, 9 ),`

The value located at the middle of the dataset array depicts the median. As such, the median depicts a value in the array such that 50% of the values...