Book Image

Scala Machine Learning Projects

By : Md. Rezaul Karim
Book Image

Scala Machine Learning Projects

By: Md. Rezaul Karim

Overview of this book

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.
Table of Contents (17 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Chapter 1. Analyzing Insurance Severity Claims

Predicting the cost, and hence the severity, of claims in an insurance company is a real-life problem that needs to be solved in an accurate way. In this chapter, we will show you how to develop a predictive model for analyzing insurance severity claims using some of the most widely used regression algorithms.

We will start with simple linear regression (LR) and we will see how to improve the performance using some ensemble techniques, such as gradient boosted tree (GBT) regressors. Then we will look at how to boost the performance with Random Forest regressors. Finally, we will show you how to choose the best model and deploy it for a production-ready environment. Also, we will provide some background studies on machine learning workflow, hyperparameter tuning, and cross-validation.

For the implementation, we will use Spark ML API for faster computation and massive scalability. In a nutshell, we will learn the following topics throughout this end-to-end project:

  • Machine learning and learning workflow
  • Hyperparameter tuning and cross-validation of ML models
  • LR for analyzing insurance severity claims
  • Improving performance with gradient boosted regressors
  • Boosting the performance with random forest regressors
  • Model deployment