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

Chapter 5. Regression on Big Data

Regression is a form of machine learning where we try to predict a continuous value based on some variables. It is a form of supervised learning where a model is taught using some features from existing data. From the existing data the regression model then builds its knowledge base. Based on this knowledge base the model can later make predictions for outcomes on new data.

Continuous values are numerical or quantitative values that have to be predicted and are not from an existing set of labels or categories. There are lots of examples of regression where it is heavily used on a daily basis and in many cases it has a direct business impact. Some of the use cases where regression can be used are the following:

  • To estimate the price of a product based on some criteria or variables

  • For demand forecasting, so you can predict the amount of sales of a product based on certain features such as amount spent on advertising, and so on

  • To estimate the hit count of an...