Machine learning and NLP Models to Predict COVID-19 Cases in US
| dc.contributor.author | Alshabana, Ghadah | |
| dc.contributor.author | Chitimalla, Ashritha | |
| dc.contributor.author | Tran, Thao | |
| dc.contributor.author | Thompson, Michael | |
| dc.date.accessioned | 2022-01-19T12:52:08Z | |
| dc.date.available | 2022-01-19T12:52:08Z | |
| dc.date.issued | 2021-04-28 | |
| dc.description.abstract | Air travel is an important factor to spread of the coronavirus from more infected regions to those with limited or no prior infections. The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. In our analysis, we intend to use Machine learning and NLP models based on CDC data to determine if the number of flights into or out of the Washington DC metro area may have impacted the number of coronavirus deaths reported in those counties. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12188 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.subject | NLP | |
| dc.subject | Social Media | |
| dc.subject | Pandemic | |
| dc.subject | COVID-19 | |
| dc.title | Machine learning and NLP Models to Predict COVID-19 Cases in US | |
| dc.type | Working Paper |