#data available at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6C3JR1
#published by Rieth, Cory A.; Amsel, Ben D.; Tran, Randy; Cook, Maia B., 2017, #"Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation", #https://doi.org/10.7910/DVN/6C3JR1, Harvard Dataverse, V1
This repo includes my testing code for the application of neural networks and other supervised learning approaches to classify fault states in a simulated chemical process problem - the Tennessee Eastman process.
The data was featherized (https://github.com/wesm/feather) to import it into Python for use with sci-kit learn's MLP Classifier.