Apache Spark Machine Learning Blueprints

Summary

In this chapter, we went through a step-by-step process, from big data to a rapid development of fraud detection systems from which we processed data on Spark and then built several models to predict frauds. With this, we then developed rules and scores to help the ABC company prevent frauds.

Specifically, we first selected a supervised machine learning approach with a focus on Random forest and decision trees as per business needs, after we prepared Spark computing and loaded preprocessed data. Second, we worked on feature extraction and selection. Third, we estimated model coefficients. Fourth, we evaluated these estimated models using a confusion matrix and false positive ratios. Then, we interpreted our machine learning results. Finally, ...