Predicting Graduate Admission

I conducted a comprehensive analysis of University of Berkeley graduate admissions, examining the dataset to understand the trends and patterns across gender and departments.

I created plots illustrating the acceptance rates by gender within each department. This allowed for a clear comparison of acceptance rates across different genders and departments, highlighting any disparities that may exist.

Furthermore, I employed a logistic regression model to delve deeper into the data and investigate whether there is evidence of gender bias in the university admissions process. By analyzing various factors and their impact on admissions outcomes, the logistic regression model provided valuable insights into the presence and extent of any gender-related disparities in admissions decisions at the University of Berkeley.