  In this paper, we discuss the impact of the Covid-19 pandemic on alcohol
consumption habit changes among healthcare workers in the United States. We
utilize multiple supervised and unsupervised machine learning methods and
models such as Decision Trees, Logistic Regression, Naive Bayes classifier,
k-Nearest Neighbors, Support Vector Machines, Multilayer perceptron, XGBoost,
CatBoost, LightGBM, Chi-Squared Test and mutual information method on a mental
health survey data obtained from the University of Michigan Inter-University
Consortium for Political and Social Research to find out relationships between
COVID-19 related negative effects and alcohol consumption habit changes among
healthcare workers. Our findings suggest that COVID-19-related school closures,
COVID-19-related work schedule changes and COVID-related news exposure may lead
to an increase in alcohol use among healthcare workers in the United States.
