ex.m er normal explaination

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Certainly! This code implements a binary classification algorithm called logistic regression, which is used to predict whether a student will be admitted to a university based on their exam scores.

First, the code loads the data from a text file and separates it into two matrices: X containing the exam scores and y containing admission status (0 for not admitted, 1 for admitted). Next, the code displays a scatter plot of the data, with admitted students shown as + and non-admitted students shown as o.

After that, the code sets up options for an optimization function called fminunc, which will be used to find the optimal values of the parameters that define the decision boundary between admitted and non-admitted students. The costFunction calculates the cost associated with a particular set of parameters.

Then, the fminunc function is called to minimize the cost function and find the optimal values of the parameters theta. These parameters are then used to predict the admission probability of a hypothetical student with exam scores of 45 and 85.

Finally, the predict function is used to predict the admission status of all students in the dataset using the learned parameters theta, and the accuracy of the model on the training set is calculated as a percentage.