Case study analysis of 12K transaction dataset to uncover suspicious money laundering activity using advanced data wrangling and visualizations.
Built time-series XGBoost and RandomForest classification models to predict individual customer churn in a bank's wealth management business unit.
Build and deploy ML models to predict Lending Club loan defaults and optimize a high-IRR portfolio, with interactive EDA, Flask API, and Dash apps.
Build stacked models on the Ames Housing dataset to predict SalePrice with rigorous cleaning, feature engineering, RFE, and stacking.
I scraped 22k WSJ articles to run statistical analyses on their article text content to determine if WSJ sentiment could be predictive of S\&P 500 returns. Includes an R Shiny app displaying final takeaways.
