Book Image

Modern Scala Projects

By : Ilango gurusamy
Book Image

Modern Scala Projects

By: Ilango gurusamy

Overview of this book

Scala is both a functional programming and object-oriented programming language designed to express common programming patterns in a concise, readable, and type-safe way. Complete with step-by-step instructions, Modern Scala Projects will guide you in exploring Scala capabilities and learning best practices. Along the way, you'll build applications for professional contexts while understanding the core tasks and components. You’ll begin with a project for predicting the class of a flower by implementing a simple machine learning model. Next, you'll create a cancer diagnosis classification pipeline, followed by tackling projects delving into stock price prediction, spam filtering, fraud detection, and a recommendation engine. The focus will be on application of ML techniques that classify data and make predictions, with an emphasis on automating data workflows with the Spark ML pipeline API. The book also showcases the best of Scala’s functional libraries and other constructs to help you roll out your own scalable data processing frameworks. By the end of this Scala book, you’ll have a firm foundation in Scala programming and have built some interesting real-world projects to add to your portfolio.
Table of Contents (14 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Summary


We learned how to build an explicit feedback type recommendation system. We implemented a predictions model with the Spark MLlib collaborative filtering algorithm that learns from past sales data and makes ratings-based recommendations about products to customers. The algorithm, as we have come to know, made its tailored product predictions on unknown customer-product interactions. 

We used Spark's support for recommendations to build a prediction model that generated recommendations for unknown customer-product interactions in terms of sales leads and past weapons sales data. We leveraged Spark's alternating least squares algorithm to implement our collaborative filtering recommendation system.