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DS-SF-30 | Dataset for the Unit Project, Parts 1-4

We'll be using the 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:

  • admit is a binary variable. It indicates whether or not a candidate was admitted into UCLA (admit = 1) or not (admit = 0).
  • gre is the GRE score. GRE stands for Graduate Record Examination.
  • gpa is the GPA score. GPA stands for Grade Point Average.
  • prestige is the prestige of an applicant alta mater, with 1 as highest tier (most prestigeous) and 4 as the lowest tier (least prestigeous).

Dataset: dataset-ucla-admissions.csv