Anomaly detection is a huge field of research focused on the task of finding weird or outlying points in data. This task is useful in all fields that handle large amounts of data and is therefore a big topic of research. The focus of research often lies in finding novel approaches for finding anomalies in already labeled and well-understood data. This thesis will not focus on a novel algorithm but instead display and discuss the power of an anomaly detection process that focuses on feature engineering and feature exploration. The thesis will also compare two unsupervised anomaly classification algorithms, namely k-nearest neighbours and principal component analysis, in terms of explainability and scalability. The results concludes that sometimes feature engineering can display anomalies just as well as novel and complex anomaly detection algorithms.