We'll be using for all Unit Projects (1-4) the same dataset as UCLA's Logit Regression in R tutorial to explore logistic regression in Python. Our goal will be to identify the various factors that may influence admission into graduate school.
The dataset contains four variables: admit, gre, gpa, and prestige:
admitis a binary variable. It indicates whether or not a candidate was admitted into UCLA (admit = 1) or not (admit = 0).greis the GRE score. GRE stands for Graduate Record Examination.gpais the GPA score. GPA stands for Grade Point Average.prestigeis the prestige of an applicant alta mater, with 1 as highest tier (most prestigeous) and 4 as the lowest tier (least prestigeous).
Dataset: ucla-admissions.csv