This data challenge contained a .csv file with credit card user data and the goal was to simply segment the users so that a credit card company could develop more targeted marketing for each type of user. I took an RFM modeling approach to classify users with unsupervised learning: Recency, Frequency, Monetary Value.
The notebook contains metadata from the .csv file, exploratory data analysis (EDA), k-means clustering with descriptions of analytical decisions, and some recommendations for the credit card company.
The google slides provide a summary of the decisions made in this challenge.
This challenge was completed while a Data Science Fellow at Insight Data Science, Boston