Use hidden Markov models to label regimes on S&P 500 data, train three classifiers (naive Bayes, logistic regression, and support vector machines) to predict regimes with the intent of improving an existing trading strategy

Use nonlinear dimension reduction, clustering, nonparametric smoothing, and natural language processing to analyze Form D data from SEC's website

Use object-oriented design in Java to build an application to computerize the processes of making reservations, recording of orders and displaying of sale records for a restaurant

Built an optimizer using SciPy to optimize an objective subject to custom-defined constraints

Ran an asset market simulation consisting of an exchange and dark pool, analyzed experimental data

Running Monte Carlo simulations to get win-rates for different Teen Patti (three-card brag) hands

Python package for common quantitative finance functions

Scraping data from LinkedIn profiles and running logistic regression on it

Using various time series forecasting methods in R to forecast Singapore's Retail Sales Index

Using credit card customer data to predict churning by customers

Implementation of the Battleship game on Python using Pygame.