Sophie's Data Orchard

I built a six-stage machine learning pipeline on 400,000+ consumer complaints from the CFPB (the federal agency that handles complaints about banks, credit bureaus, and lenders)— cleaning raw data, discovering hidden topics, extracting structured facts from free text, predicting outcomes, retrieving similar past cases, and finally, building an AI assistant that triages new complaints using everything the earlier stages learned. The part of this project I'm proudest of isn't any of that. It's the moment I caught myself being wrong. The setup The last stage of the pipeline is a triage tool. Feed it a new complaint, and it pulls up similar historical complaints, then generates a predicted category, a suggested next step, and a confidence level. To know whether an automated prompt-optimization step actually improved this tool, I needed a way to score its predictions against the true category. I wrote what looked like a reasonable check: does a meaningful word from the correct c...

By the final year of OMSA, I could clearly see how the curriculum was coming together. Spring focused on broadening my analytics toolkit—from visual analytics to classical Natural Language Processing—while Summer shifted toward modern Generative AI and an industry-sponsored practicum. Looking back, these four courses complemented one another surprisingly well, bridging traditional analytics, modern AI, and real-world data science practice. CSE 6242 – Data & Visual Analytics CSE 6242 is one of the most practical and technically diverse courses in OMSA. Rather than concentrating on a single subject, the course introduces a wide range of technologies commonly used in data analytics, including D3.js, Tableau, Spark, Docker, SQL, and machine learning workflows. Every homework felt like learning a new technology stack, making the course both exciting and challenging. The workload was among the heaviest in the program. Much of the effort was spent configuring environments, debugging dep...