Analytics in practice
Let’s now put what we have learned into practice. We are going to build a diagnostic analytic and a predictive analytic. We will develop an anomaly detection algorithm for an airplane and a predictive algorithm for an oil and gas refinery. We want to remain as generic as possible, so we won’t make any assumptions about the system that we are going to monitor.
We will develop these two use cases with Python, SciPy, NumPy, Seaborn, and pandas. We will assume that Python 3.9+ is already installed on your system and that Jupyter is running on your PC (see the first paragraph in the Jupyter subsection earlier on how to start the notebook).
Anomaly detection in practice
For this exercise, we will use a free dataset for predictive maintenance (the dataset is based on Azure Dataset and can be found at https://learn.microsoft.com/en-us/azure/architecture/industries/manufacturing/predictive-maintenance-overview).
