I applied unsupervised learning techniques on product spending data collected for consumers of a wholesale distributor in Lisbon, Portugal. My goal was to define how best segment their customers into distinct categories. I reviewed unstructured data to understand the patterns and natural categories that the data fits into and made predictions about the natural categories of multiple types in a dataset. Then, I checked these predictions against the result of unsupervised analysis.

I mostly used the Python libraries Pandas and Sklearn to wrangle and built the model and Seaborn to visualize the data. This project is connected to the Machine Learning Engineer Nanodegree program, from Udacity.