An infectious disease that causes respiratory illness like flu, the novel coronavirus (COVID-19) has deeply affected our society all over the world. Despite the efforts of social distancing and increased personal hygiene, the countries have been struggling to “flatten the curve”. In other to better combat the pandemic, many experts have been developing various models to predict the number of infections.
According to NYC Health, there are approximately 27k restaurants in NYC, of which about 11k (40%) are located in Manhattan. In other words, it would take a New Yorker almost 10 years to try every single restaurant in Manhattan, assuming he or she eats out 3 meals everyday and the list of restaurants stay the same. If we consider more realistic scenario where a myriad of restauarnts newly open or close every year, it would be virtually impossible to try out every single restaurants in Manhattan.
On March 20, 2019, Fortune reported that the U.S. is the unhappiest it’s ever been.
In this blog post, I will discuss how I analyzed zipcode-level median housing sales data from Zillow. I used ARIMA modeling to identify 5 zipcodes with highest predicted rate of returns in the three biggest cities (by number of zipcodes).
In this blog post, I will discuss how I analyzed the Northwind Database using Monte Carlo Simulation.