Projects I have worked on

Predicting Procrastination

Using machine learning to enhance productivity

The motivation for this project came from my own personal struggles with procrastination. There exist many productivity apps out there to help people like me stay focused despite the seemingly infinite number of distractions that exist on the current state of the Internet (cough Reddit cough). The problem is, most of these productivity apps block website purely off URL. In many cases this is good enough, but often times blocking an entire URL creates more problems than it solves. Youtube, for example, is one of the largest sources of distraction the Internet has to offer. Naturally, it is a website one would block when they would like to be productive. However, Youtube also provides a vast wealth of incredibly informative videos. There have been numerous times when watching a five minute video on matrix multiplication to refresh my memory would lead to hours of increased productivity down the line. But alas, the site is blocked by my supposedly helpful productivity app. So I find myself in at a crossroads: I can disable the app to watch the video I want to see, but run the risk of spiraling down a never ending path of cat videos; or I leave the app running and search for a less effective refresher. I should not have to choose. So I set out to find a better way.

Westnile Virus Prediction

A Kaggle comptetion

This project was inspired by a Kaggle competition on predicting West Nile Virus in Chicago. We worked as a team to develop a model that would accurately predict the occurrence of West Nile Virus in various locations and at various times across the city.

We achieved this goal by developing a complex set of features that were largely dependent on weather conditions in Chicago. We used these features in an xgb boosted tree to maximize our ROC-AUC score. The resulting model allowed us to score in the top third of Kaggle submissions.

From here we crafted a presentation based on the findings from our model that discussed effective measures that city of Chicago could take to combat the spread of West Nile Virus. Our recommendations focused largely on localized preemptive spraying utilizing our models ability to effectively predict where the next outbreak will occur.