What this book covers
Chapter 1, Spark for Machine Learning, introduces Apache Spark from a machine learning perspective. We will discuss Spark dataframes and R, Spark pipelines, RM4Es data science framework, as well as the Spark notebook and implementation models.
Chapter 2, Data Preparation for Spark ML, focuses on data preparation for machine learning on Apache Spark with tools such as Spark SQL. We will discuss data cleaning, identity matching, data merging, and feature development.
Chapter 3, A Holistic View on Spark, clearly explains the RM4E machine learning framework and processes with a real-life example and also demonstrates the benefits of obtaining holistic views for businesses easily with Spark.
Chapter 4, Fraud Detection on Spark, discusses how Spark makes machine learning for fraud detection easy and fast. At the same time, we will illustrate a step-by-step process of obtaining fraud insights from big data.
Chapter 5, Risk Scoring on Spark, reviews machine learning methods and processes for a risk scoring project and implements them using R notebooks on Apache Spark in a special DataScientistWorkbench environment. Our focus for this chapter is the notebook.
Chapter 6, Churn Prediction on Spark, further illustrates our special step-by-step machine learning process on Spark with a focus on using MLlib to develop customer churn predictions to improve customer retention.
Chapter 7, Recommendations on Spark, describes how to develop recommendations with big data on Spark by utilizing SPSS on the Spark system.
Chapter 8, Learning Analytics on Spark, extends our application to serve learning organizations like universities and training institutions, for which we will apply machine learning to improve learning analytics for a real case of predicting student attrition.
Chapter 9, City Analytics on Spark, helps the readers to gain a better understanding about how Apache Spark could be utilized not only for commercial use, but also for public use as to serve cities with a real use case of predicting service requests on Spark.
Chapter 10, Learning Telco Data on Spark, further extends what was studied in the previous chapters and allows readers to combine what was learned for a dynamic machine learning with a huge amount of Telco Data on Spark.
Chapter 11, Modeling Open Data on Spark, presents dynamic machine learning with open data on Spark from which users can take a data-driven approach and utilize all the technologies available for optimal results. This chapter is an extension of Chapter 9, City Analytics on Spark, and Chapter 10, Learning Telco Data on Spark, as well as a good review of all the previous chapters with a real-life project.
