Principal Components Analysis (PCA) is a commonly used unsupervised machine learning technique. In this presentation, I describe the PCA method with a general description and geometric interpretation using simulated two and three-dimensional data. The description of the PCA methodology is followed by an application of PCA to analyze Latino stress by agricultural season and occupation in a majority-minority agricultural area of eastern Washington State.