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


The following are questions that will consolidate and deepen your knowledge of fraud detection:

  1. What is a Gaussian Distribution?
  2. The algorithm in our fraud detection system requires something really important to be fed into it, before generating probabilities? What is that?
  3. Why is the selection of an error term (Epsilon) such a big deal in detecting outliers and identifying the correct false positives and false negatives?
  4. Why is fraud detection not exactly a classification problem? 
  5. Fraud detection is essentially an anomaly identification problem. Can you name two properties that define anomaly identification?
  6. Can you think of other applications that can leverage anomaly identification or outlier detection?
  7. Why is cross-validation so important?
  1. Why is our fraud detection problem not a supervised learning problem?
  2. Can you name a couple of ways to optimize the Gaussian Distribution algorithm?
  3. Sometimes, our results may not be satisfactory because the algorithm failed to identify certain samples...