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 a list of questions for your reference:

  1. What do you understand by EDA? Why is it important?
  2. Why do we create training and test data?
  3. Why did we index the data that we pulled from the UCI Machine Learning Repository?
  4. Why is the Iris dataset so famous?
  5. Name one powerful feature of the random forest classifier.
  6. What is supervisory as opposed to unsupervised learning?
  7. Explain briefly the process of creating our model with training data.
  8. What are feature variables in relation to the Iris dataset?
  9. What is the entry point to programming with Spark?

Task: The Iris dataset problem was a statistical classification problem. Create a confusion or error matrix with the rows being predicted setosa, predicted versicolor, and predicted virginica, and the columns being actual species, such as setosa, versicolor, and virginica. Having done that, interpret this matrix.