High School Student Alcohol Consumption Study


Analyzed, sanitized, cleaned, and pre-processed a publicly available dataset of over 500 students' alcohol consumption habits.

Subsequently implemented several unsupervised Machine Learning algorithms to yield correlations within the dataset and discovered that students who consume more alcohol have a positive correlation with more free time on their hands, spend less time studying, and lack guardians whose occupations reside within health care.

Utilized various data visualization techniques such as bar graphs, line graphs, and scatter plots to analyze the data and provide meaningful insight.