Logistic Regression is a statistical technique capable of predicting a binary outcome and commonly applied in disciplines from credit and finance to medicine and other social sciences.

A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable.

For this tutorial, we will use pandas, statsmodels, and numpy to predict each student's success (admission) rate given their gre, gpa scores and the rank of her alma matter upon application.

This repo contains the dataset required for this tutorial, the Jupyter notebook, as well as HTML and Python code for this exercise.

Credits to the original tutorial on yHat, which is adapted from the R tutorial published by UCLA

The code for our cartesian function originally found on StackOverflow here