Customer-Segments

In this project I applied unsupervised learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data. I first explored the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, I preprocessed the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, I applied PCA transformations to the data and implemented clustering algorithms to segment the transformed customer data. Finally, I compared the segmentation found with an additional labeling and considered ways this information could assist the wholesale distributor with future service changes.