Anomaly Detection Tutorial for Data Scientists
Anomaly detection is widely used by Data Scientists and Machine Learning Engineers to detect data that is the most different from the main, generalized part of your data. It is beneficial when setting up alerts to see if a record of data is going beyond a certain threshold designated by the clustering model. You can also look at event data from the product website of your business, for example, such as clicks and views that could possibly see some anomalies. Some other use cases include fraud detection and cybersecurity. Other fields like the healthcare field can benefit greatly from anomaly detection, and Machine Learning algorithms only make it easier to detect these anomalies automatically.