Ilango gurusamy - Modern Scala Projects: Leverage the power of Scala for building data-driven and high-performant projects

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Ilango Gurusamy holds an MS degree in computer science from California State University. He has lead Java projects at Northrop Grumman, AT&T, and such. He moved into Scala and Functional Programming. His current interests are IoT, navigational applications, and all things Scala related. A strategic thinker, speaker, and writer, he also loves yoga, skydiving, cars, dogs, and fishing. You can know more about his achievements in his blog, titled scalanirvana. His LinkedIn user name is ilangogurusamy.

Adithya Selvaprithiviraj is a Scala developer in the Innovation Centre Network at SAP Labs. Currently, he is involved in the development of a modern typesafe framework to ease enterprise application development in the SAP landscape. Previously, Adithya was part of several machine learning projects. You can find out more about his achievements in his blog, titled adithyaselv.

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Scala, along with the Spark Framework, forms a rich and powerful data processing ecosystem. This book is a journey into the depths of this ecosystem. The machine learning (ML) projects presented in this book enable you to create practical, robust, data analytics solutions, with an emphasis on automating data workflows with the Spark ML pipeline API. This book showcases, or carefully cherry-picks from, Scalas functional libraries and other constructs to help readers roll out their own scalable data processing frameworks. The projects in this book enable data practitioners across all industries to gain insights into data that will help organizations to obtain a strategic and competitive advantage. Modern Scala Projects focuses on the application of supervisory learning ML techniques that classify data and make predictions. You'll begin with working on a project to predict a class of flower by implementing a simple machine learning model. Next, you'll create a cancer diagnosis classification pipeline, followed by projects delving into stock price prediction, spam filtering, fraud detection, and a recommendation engine.

By the end of this book, you will be able to build efficient data science projects that fulfill your software requirements.

This book is for Scala developers who would like to gain some hands-on experience with some interesting real-world projects. Prior programming experience with Scala is necessary.

, Predict the Class of a Flower from the Iris Dataset , focuses on building a machine learning model leveraging a time-tested statistical method based on regression. The chapter draws the reader into data processing, all the way to training and testing a relatively simple machine learning model.

, Build a Breast Cancer Prognosis Pipeline with the Power of Spark and Scala , taps into a publicly available breast cancer dataset. It evaluates various feature selection algorithms, transforms data, and builds a classification model.

, Stock Price Predictions , says that stock price prediction can be an impossible task. In this chapter, we take a new approach. Accordingly, we build and train a neural network model with training data to solve the apparently intractable problem of stock price prediction. A data pipeline, with Spark at its core, distributes training of the model across multiple machines in a cluster. A real-life dataset is fed into the pipeline. Training data goes through preprocessing and normalization steps before a model is trained to fit the data. We may also provide a means to visualize the results of our prediction and evaluate our model after training.