Feature preparation
In section, Feature extraction, of Chapter 2, Data Preparation for Spark ML, we reviewed a few methods for feature extraction and discussed their implementation on Apache Spark. All the techniques discussed there can be applied to our data here, especially the ones utilizing time series and feature comparison to create new features.
For this project, feature extraction is one of the most important tasks because all the fraud happens online and the web log is the most important and most recent data to predict frauds, which needs extraction to produce features ready for modeling.
Also, as we have features for transactions, users, bank accounts, and computer devices, a lot of work is needed to merge all these features together to form a complete data file ready for machine learning.
Feature extraction from LogFile
Log files are always unstructured, similarly to a collection of random symbols and numbers. One example of this is as follows:
May 23 12:19:11 elcap siu: 'siu...
