Logistic Regression
- This project intented to create a model that predicts whether a university applicant is going to be accepted or not. This was done by taking into account his/her exams results. In cases like this one, classification machine learning algorithms are utilised.
Initially, input data were plotted in order to decide which decision boundary can be developed.
I saw that most points regarding admitted and not admitted applicants are linearly seperable, therefore a linear decision boundary could be applied. Thus, I implemented Logistic Regression, a known classification algorithm that generates a model with linear decision boundary. The produced decision boundary is illustrated in Figure 2.
However, we can see that there are some data points classified incorrectly. So, let’s see which is the train accuracy of the generated model. The accuracy of the trained model was 89%, and the acceptance probability of a student with exams results of 45 and 85, is 77.62%.
A more detailed description of this project’s implentation in Matlab can be seen in this github repository: Link to Github repository