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We incorporate external data on zip 5 level to aggregate with citizen bank's data and run an allocation model to predict homebuyers
Using a graph neural network and external data from the Census Bureau and Zillow, we predict the number of home buyers for a given zip code
We created a simple SIR model with migration term and stochastics based on the immigration data in the US.
What does the COVID-19 coronavirus outbreak mean for tourism to the United States and where is the next growth?
We hypothesize that public awareness of disease outbreaks correlates negatively to the number of arrivals from China into the US.
Based on Chinese tourism levels to the United States during the SARS outbreak, we created an ARIMA model to predict when tourism to the US will rebound to pre-COVID-19 levels.
Calculate the similarity between input wikipiece and all base sentences. Find out the most similar one and the relevant insertion. Predict the possible part of speech combination accordindly.
We modelled the impact of the SARS outbreak in 2003 on arrivals to the USA and used this model to predict the impact of the Coronavirus outbreak in the first half of 2020.
For each region in a given ZIP code area, predict whether there will be homebuyers or not.
Using the WikiAtomicEdits dataset, we have two goals - 1. Categorize the type of edits made . 2. Finding practical use - cases of the data set and building them.
Visualizing who could be home buyers within a zip-9
A pipeline to predict Additive Manufacturing Parameters to overcome the problems of physics based simulations
Using the Citizen's Bank dataset, we analyzed how geography affects home buyers, integrating demographic and credit data for various consumer use cases.
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