Dataset:
A high school has a dataset representing 40 students who were admitted to college and 40 students who were not admitted. Each (x(i); y(i)) training example contains a student's score on two standardized exams and a label of whether the student was admitted. In training data,The first column of x array represents all Test 1 scores, and the second column represents all Test 2 scores. The y vector uses '1' to label a student who was admitted and '0' to label a student who was not admitted.
Task:
The task is to build a binary classification model that estimates college admission chances based on a student's scores on two exams. Our goal is to use Newton's method to minimize the cost function. Plotting the decision boundary.