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

Chapter 5. Build a Fraud Detection System

In this chapter, we are going to develop an algorithm based on the Gaussian Distribution function using Spark ML. We will apply the algorithm to detect fraud in transactions data. This kind of algorithm can be applied toward building robust fraud detection solutions for financial institutions, such as banks, which handle great quantities of online transactions.

At the heart of the Gaussian Distribution, the function is the notion of an anomaly. The fraud detection problem is only a classification task but in a very narrow sense. It is a balanced supervised learning problem. The term balanced refers to the fact that the positives in the dataset are of a small number in relation to the negatives. On the other hand, an anomaly detection problem is typically not balanced. The dataset contains a significantly small number of anomalies (positives) in relation to the negatives. The fraud detection problem is a prime example of an anomaly detection problem...