We grouped 8,950 credit‑card customers into clear, actionable personas based on real spending and payment behavior. The write‑up explains the data we used, how we formed the groups at a high level, what makes each persona distinct (e.g., cash‑advance heavy vs. everyday spenders), and how teams can activate them to tailor offers, credit limits, and messaging.
Unsupervised topic modeling on 1.1M ABC News headlines with LDA, LSA, LSI, and HDP; compare scikit‑learn vs Gensim/NLTK preprocessing and visualize separability with t‑SNE.
