I have experience in predictive modeling, statistical analysis, machine learning, and Image Classification. With a background in neuroscience and the biomedical sciences, I bring strong skills in data analysis and collaboration that help research groups make powerful insights and help companies utilize data to impact organizational decisions.
The purpose of this project was to analyze data regarding movie box office sales, actors, directors, ratings, etc in order to make recommendations to a new, hypothetical microsoft movie studio which wants its first movie to be a success. We chose to recommend that Microsoft hire directors, actors, and writers who had the highest return on investment (ROI) on the films that they were involved with.
The purpose of this project was to analyze features of houses in King County, Washington in order to see if we could use a linear regression model to successfully predict house sale prices. The business purpose of this project was to introduce a predictive model for a hypothetical bank which it could use to determine whether or not a house represents sufficient collateral to justify giving out a loan to a client interested in the home.
In this project, we leveraged a variety of predictive modeling tools in order to predict the likelihood of an individual getting vaccinated with the H1N1 flu vaccine from the National Flu Survey (NHFS, 2009) data, and the factors which most contributed to vaccination status. Insights gleaned from understanding past vaccination patterns can perhaps help assist factors which affect vaccination behavior for other vaccines, such as the COVID-19 vaccine.
The purpose of this project was to train a neural network to distinguish between chest x-rays which showed evidence of pneumonia and those that are normal, and then to use this model to predict disease status using a validation set.
The purpose of this project was to train a neural network to distinguish between Brain MRIs which contained a brain tumor and those that are normal, and then to use this model to predict whether or not a brain MRI contains a tumor using a validation set.