For my passion project at Metis, I wanted to do a project that falls under “data science for social good” umbrella. Don’t get me wrong – I’m very interested in using data science to solve business problems – but I figured this was a good opportunity to focus all my energy towards a social problem. With this in mind, my goal was to build a tool that can help shelters identify animals that are less likely to be adopted.

I usually browse reddit’s unfiltered front page in my free time, but I always find myself cracking up at the posts from /r/showerthoughts. Highly upvoted submissions to /r/showerthoughts have that je ne sais quoi that other subreddits lack. A couple weeks ago, I decided that it’d be fun to try to distill that undefinable quality that makes showerthoughts so good using NLP and machine learning.

Exoplanets discoveries are the hot new science on the block. Chances are, you’ve heard something about exoplanets in the past few years. In case you haven’t, let me give you a quick overview.

The third week of Metis is over, and with it, project two has been completed. This project was much more free form in that the only constraints imposed on me were the expectation that I would collect all my data via web scraping and predict something using linear regression. Everything else was on me, including subject matter, methodology, and tech stack used.

I just finished my first week of the Metis data science bootcamp. About eighteen hours ago my group and I were presenting our first project, wherein we were tasked with recommending the optimal New York City (NYC) subway stations to canvas for an upcoming Gala supporting women in tech.