Outlier Detection in Python

Outlier detection plays a pivotal role in many fields. Its applications include fraud detection, network security, financial auditing, regulatory oversight of financial markets, medical diagnosis, and the development of autonomous vehicles. Although outlier detection often doesn't garner the same attention as many other machine learning disciplines, such as prediction, generative AI, forecasting, or reinforcement learning, it holds a place of significant importance. It's important to note that not all outliers are necessarily problematic, and in fact, many are not even interesting. But outliers often can be indicative of errors, hold special interest, or at least warrant further investigation. And it is a very common theme that, while many outliers may not be of interest they are simply items that are statistically unusual—very often the converse is true: what we are looking for in data, possible fraud, errors, scientific discoveries, hardware failures, criminal activity, genetic variants, and so on, are outliers, and consequently specifically looking for outliers in data can be highly fruitful.