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

Questions


Here are 10 questions that will help to reinforce all of the learning material presented in this chapter:

  1.  Is the spam classification task a binary classification task?
  2. What was the significance of the hashing trick in the spam classification task?
  3. What is hashing collision and how is it minimized?
  4. What do we mean by inverse document frequency?
  5. What are stop words and why do they matter?
  6. What is the role played by the Naive Bayes algorithm in spam classification?
  7. How do you use the HashingTF class in Spark to implement the hashing trick in your spam classification process?
  8. What is meant by the vectorization of features?
  9. Is there a better algorithm that you can think of to implement the spam classification process? 
  10. What are the benefits of spam filtering, and why do they matter in business terms?