Book Image

Mastering Machine Learning with Spark 2.x

By : Malohlava, Tellez, Max Pumperla
Book Image

Mastering Machine Learning with Spark 2.x

By: Malohlava, Tellez, Max Pumperla

Overview of this book

The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.
Table of Contents (9 chapters)
3
Ensemble Methods for Multi-Class Classification

Lending Club Loan Prediction

We are almost at the end of the book, but the last chapter is going to utilize all the tricks and knowledge we covered in the previous chapters. We showed you how to utilize the power of Spark for data manipulation and transformation, and we showed you the different methods for data modeling, including linear models, tree models, and model ensembles. Essentially, this chapter will be the kitchen sink of chapters, whereby we will deal with many problems all at once, ranging from data ingestion, manipulation, preprocessing, outlier handling, and modeling, all the way to model deployment.

One of our main goals is to provide a realistic picture of a data scientists' daily life--start with almost raw data, explore the data, build a few models, compare them, find the best model, and deploy into production--if only it were this easy all the time! In...