In The Kitchen Sink, I will be blogging about the applications of data science and AI. These consist of side projects, essays on machine learning, and general thoughts. Please email me with any questions.
6/20/2020 – A $15 minimum wage isn’t all that radical.
In this article, I investigate the $15 federal minimum wage from a historical perspective. Specifically, I look at how the minimum wage has changed as a function of time to see if we can gain insights about the future of American wages.
3/20/2020 – COVID-19 is winning the partisan battle
In this article, I investigate the relationship between Google searches of coronavirus pandemic-related topics and political partisanship. I found that the redder the state, the less likely that state is to look up important information about coronavirus.
TL;DR: Stop making coronavirus partisan and go to cdc.gov for accurate information.
12/18/2019 – To Which Tribe Do You Ascribe?
In this article, I investigate political tribalism in America. I engineered a dataset consisting of features extracted from the congressional voting record as well as recent election results. I then applied a machine learning algorithm to this set of data to predict how Congress will vote. I found that party affiliation is the most indicative predictor for how a congressperson will vote. I found that there is less evidence for political tribalism among Democrats than among Republicans.
11/10/2019 – Risky Business
In this article, I investigate the effect one’s undergraduate major might have on financial success. Specifically, I look at the risk associated with a given college major. I found that a high-risk degree can just as easily launch a graduate to financial success as a low-risk degree. In fact, the data might even imply that the riskiness of a college major is just a proxy for whether graduate education is necessary.
In this article, I delve into some of the technical aspects of the algorithms underlying facial recognition that make me support the ACLU’s direction on facial recognition.