Anomaly Detection using Autoencoders
Track: Data Science
Room: Boundary Room
Time: Oct 11 (Fri), 09:00
Finding anomalous behaviour can be similar to finding a needle in a haystack. This information can be very useful for fraud detection or identifying unusual behavior. Machine Learning techniques such as autoencoders can assist in this process.
We will present a jupyter notebook followed by a visualisation which indicates anomalous activity using an open source credit card dataset. The anomalous activity will be compared to known fraudulent activity within the dataset. The technologies used for visualisation is Qliksense and the python implementation of autoencoders is the h2o deeplearning estimator package.