I like taking messy, real-world datasets and finding the story inside them — through exploratory analysis, predictive models, and the occasional applied AI experiment. Take a look at a few fun projects of mine:
Every project below follows the same arc: understand the data, clean it honestly, engineer what matters, then build something you can actually interact with.
Cleaning, imputing, and interrogating data until it tells a trustworthy story — with visuals that make the finding obvious.
Feature engineering, clustering, and model tuning — regression and classification pipelines built and evaluated end-to-end.
Using embeddings and pretrained models (like CLIP) to build multimodal tools, then shipping them as real, usable apps.
Three projects, three different muscles: applied ML, multimodal AI, and analytical storytelling.
An end-to-end pipeline predicting salaries across ~90,000 AI & data roles worldwide (2020–2026). Cleaned outliers with the IQR method, engineered K-Means "career archetype" clusters and polynomial features, then trained and tuned Random Forest models for both salary regression and a 3-tier salary classifier — deployed as a live interactive app.
A multimodal career-matching tool: describe or photograph your ideal workspace, and it's converted into a CLIP embedding and compared against 1,500 indoor-scene images mapped to O*NET's 923 occupations via the RIASEC personality framework. Clustered the embedding space (K=5) and built a real-time cosine-similarity recommendation engine.
A full exploratory analysis of 172,000 historical race records to find what actually predicts a win. Handled missing data with targeted imputation, engineered an "actual odds" feature from raw betting probabilities, and used correlation analysis to isolate the strongest signals — distilled into a clear, evidence-backed profile of a winning horse.