Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Modern Scala Projects

You're reading from   Modern Scala Projects Leverage the power of Scala for building data-driven and high performance projects

Arrow left icon
Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788624114
Length 334 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Ilango gurusamy Ilango gurusamy
Author Profile Icon Ilango gurusamy
Ilango gurusamy
Arrow right icon
View More author details
Toc

What this book covers

Chapter 1, Predict the Class of a Flower from the Iris Dataset, focuses on building a machine learning model leveraging a time-tested statistical method based on regression. The chapter draws the reader into data processing, all the way to training and testing a relatively simple machine learning model.

Chapter 2, Build a Breast Cancer Prognosis Pipeline with the Power of Spark and Scala, taps into a publicly available breast cancer dataset. It evaluates various feature selection algorithms, transforms data, and builds a classification model.

Chapter 3, Stock Price Predictions, says that stock price prediction can be an impossible task. In this chapter, we take a new approach. Accordingly, we build and train a neural network model with training data to solve the apparently intractable problem of stock price prediction. A data pipeline, with Spark at its core, distributes training of the model across multiple machines in a cluster. A real-life dataset is fed into the pipeline. Training data goes through preprocessing and normalization steps before a model is trained to fit the data. We may also provide a means to visualize the results of our prediction and evaluate our model after training.

Chapter 4, Building a Spam Classification Pipeline, informs the reader that the overarching learning objective of this chapter is to implement a spam filtering data analysis pipeline. We will rely on the Spark ML library's machine learning APIs and its supporting libraries to build a spam classification pipeline.

Chapter 5, Build a Fraud Detection System, applies machine learning techniques and algorithms to build a practical ML pipeline that helps find questionable charges on consumers’ credit cards. The data is drawn from a publicly accessible Consumer Complaints Database. The chapter demonstrates the tools contained in Spark ML for building, evaluating, and tuning a pipeline. Feature extraction is one function served by Spark ML that is covered here.

Chapter 6, Build Flights Performance Prediction Model, makes us able to leverage flight departure and arrival data to predict for the user if their flight is delayed or canceled. Here, we will build a decisions trees-based model to derive useful predictors, such as what time of the day is best to have a seat on a flight, with a minimum chance of delay.

Chapter 7, Building a Recommendation Engine, draws the reader into the implementation of a scalable recommendations engine. The collaborative-filtering approach is laid out as the reader walks through a phased recommendations-generating process based on users’ past preferences.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image