Exploration of COVID19 Vaccination Data on the basis of Presidential Election Results

Group Members and Introduction:

• Arda Gündüz

• Ege Bağırsakçı

• Erdem Karataş

• Eylül Öykü Şen

• Kaan Karabacakoğlu

• Özgür Yılmaz Beker

The project aims to investigate the relation between vaccination rate and political preference based on U.S. with three discrete datasets. If a significant difference could be observed for this sample, machine learning models, such as a classifier model for countries/states/counties, could be created to infer the ruling ideology in a given area. The labels will be predicted by this model according to the vaccination rate (defined below) of the given area. This label may be used to raise awareness about the shortcomings of the ruling party in preventing the spread of the pandemic. These parties may then be called to encourage the people about vaccination.

Notes on reproducability

The dataset found used in our workflows was last updated on November 24, 2021 (January 13, 2021 for Election Dataset). Newer/older versions of the data may results in different outcomes.

Latest versions available at: COVID Vaccine Dataset: https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-County/8xkx-amqh US Presidential Elections Dataset: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/42MVDX

All code used can be found in the notebook Project.ipynb. To reproduce the data, lines that are used to access cleaned and raw data (also available in this repository) need to be configured accordingly.

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