This is a UCI data set from 2014 about clients' spending at a wholesale distrubutor. Every row represents how many monetary units within a category of item the given client bought in a year.440 clients' spendings on each category were recorded. There are 8 attributes: channel (e.g. the client type: "1" for hotel/restaurant and "2" for retail such as a supermarket), region (Lisbon, Oporto, or other, numbered 1-3), fresh products, milk products, grocery products, frozen products, detergents/paper products, and delicatessen (deli products like cold cuts). The numbers under the product types represent monetary units (m.u.), which is a substitute for measuring in regular currency. Wikipedia defines it as "the change in the utility from an increase in the consumption of that good or service". I will be working with this data by observing relationships between certain features, performing clustering, and doing a PCA analysis.

Problem Statement

As I mentioned before, this data set is from the UCI site, and it was donated in 2014. Based on the region names, this seems to be data collected in Portugal. The main questions I seek to answer are, in general terms, "Is there a relationship between the kind of client and the type of goods of which the most were purchased?", "Is there a relationship between certain product types, whether it be positive or negative correlation?", and "Can we cluster the data into groups based on similar number of products purchased in certain categories?"


describe the visualization and analysis tools/methods you used

Linear regression


Maybe naive bayes and knn if I'm feeling masochistic


show the visualizations and analysis results


highlights the important results and concludes the writeup


Thank you to my high school buddy John for explaining some economics things