Modern Scala Projects: Leverage the power of Scala for building data-driven and high performance projects

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Build a Breast Cancer Prognosis Pipeline with the Power of Spark and Scala

Breast cancer is the leading cause of death among women each year, leaving others in various stages of the disease. Lately, machine learning (ML) has shown great promise for physicians and researchers working towards better outcomes and lowering the cost of treatment. With that in mind, the Wisconsin Breast Cancer Data Set represents a combination of suitable features that are useful enough to generate ML models, models that are able to predict a future diagnostic outcome by learning from predetermined or historical breast mass tissue sample data.

Here is the dataset we refer to:

UCI Machine Learning Repository: Breast Cancer Wisconsin (Original) Data Set
UCI Machine Learning Repository: Breast Cancer Wisconsin (Diagnostic) Data Set
Accessed July 13, 2018
Website URL: https:/...

Key benefits

Gain hands-on experience in building data science projects with Scala

Exploit the powerful functionalities of machine learning libraries

Use machine learning algorithms and decision tree models for enterprise apps

Description

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.

What you will learn

Create pipelines to extract data for analytics and visualizations

Automate your process pipeline with jobs that are reproducible

Extract intelligent data efficiently from large, disparate datasets

Automate the extraction, transformation, and loading of data

Develop tools that collate, model, and analyze data

Maintain data integrity as data flows become more complex

Develop tools that predict outcomes based on pattern discovery

Build fast and accurate machine learning models in Scala

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