This paper will present useful and reproducible feature engineering techniques utilizing R's tidyverse workflows. It will focus primarily on identifying and resolving redundant measures of underlying constructs. In addition, it will explore the use of deep learning autoencoding for non-linear dimensionality reduction and anomaly detection. Autoencoding also allows for complete reproduction of the data, unlike e.g., principal components. The objective is to achieve high quality partitions leading to more accurate predictive models for scoring.
