Machine Learning for Everyone - Part 2: Spotting anomalous data

We're going to analyze data that contain cases flagged as abnormal. So we'll build a predictive model in order to spot cases that are not currently flagged as abnormal, but behaving like ones that are. This post contains R code and some machine learning explanations, which can be extrapolated to other languages such as Python. The idea is to create a case study giving the reader the opportunity to recreate results. Note: There are some points oversimplified in the analysis, but hopefully you'll become curious to learn more about this topic, in case you've never done a project like this.