For my final project at Metis, I built a lineup creation tool for daily fantasy sports. The first step when playing fantasy sports is to build a model to project how many fantasy points they will score, and to do that you need historical statistics to train the model on.

Our latest project involved creating a predictive classification model from a real life dataset. Our team used a dataset relating to heart disease from the excellent UCI Machine Learning Database.

For our second project, we used linear regression to investigate predictions relating to the movie industry. My initial idea centered around trying to predict the quality of a movie. In some ways, predicting the IMDB Rating or Metacritic score of a movie isn't entirely useful, as those ratings will nearly always be available as soon as the movie is released, but if we could make a model without using any time sensitive inputs, we could use it to estimate the hypothetical rating of any combination of director/actors.

As an exercise in exploratory data analysis, we broke up into groups to obtain, digest, and analyze data in pursuit of our stated objective. The goal was to use MTA subway data, and other sources, to determine where, and when, to optimally place workers in order to recruit potential donors to an event supporting increasing the participation of women in technology.