Anomaly Detection with Z-Score: Pick The Low Hanging Fruits

This article introduces an unsupervised anomaly detection method which based on z-score computation to find the anomalies in a credit card transaction dataset using Python step-by-step. Anomaly detection techniques can be applied to resolve various challenging business problems. For example, detecting the frauds in insurance claims, travel expenses, purchases/deposits, cyber intrusions, bots that generate fake reviews, energy consumptions, and so on. Unsupervised learning is the key to the imperfect world because in which the majority of data is unlabeled. It is also much harder to evaluate an unsupervised learning solution than supervised learning method which we will discuss in details later on…....