The travel industry was one of the most directly impacted sectors of the economy during the pandemic as international flights abruptly ground to a halt in March 2020 and many domestic carriers significantly reduced their flights. I was curious how the travel industry could rebound this year (2021) so I created a time series forecast using official TSA Checkpoint data, which measures how many passengers are screened per day at U.S. airports, to predict the number of passengers that will be screened this year and specifically sought to find how this number will compare to pre-pandemic levels in 2019. …

“One, two, one, two, three…”

I remember unwrapping the plastic wrap from the CD that I had just bought at the local mall, the whir of the car’s CD player as it loaded the disc and then hearing the opening words of “Waiting On The World to Change” as we pulled out of the mall parking lot. …

Hurricane Laura shortly before making landfall in Cameron Parish

In late April, while New York City was locked down due to the COVID-19 pandemic, I was moved by an article in The New York Times about the racial disparities that resulted in divergent health outcomes for COVID patients in New Orleans. Specifically, the article focused on members of the Zulu club, a Black social organization in the city, and the tremendous loss of many of their members from the virus. This same mortality pattern was unfortunately not limited to New Orleans and has been present in most metro areas across the country, including New York City. …

How did Americans feel about each candidate during their respective dueling town halls on October 15, 2020? Twitter users had plenty to say throughout the night about both Joe Biden and President Trump. I used machine learning to analyze the sentiment of every tweet about the candidates from 8pm Eastern, when both town halls began, until the conclusion of Biden’s town hall at 9:30pm Eastern. Here’s what I found:

Sentiment score on a scale of 0 (negative) to 4 (positive)

I used Tweepy’s streaming functionality to collect the tweets into separate pandas DataFrames for each candidate and preprocessed them to remove URLs, punctuation and stopwords. I also lemmatized the tweets to…

Kevin Hannon Schulman

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