Supervised Machine Learning: Regression and Classification

First Jupyter File (Linear Regression):

In this file, I wrote code that predicts which city in our dataset yields the highest profit.
The dataset contains data from various cities along with their corresponding profits. Our main goal is to predict the true profit value based on a city's population.
I implemented gradient descent to find the optimal values of w and b that minimize the error.

Second Jupyter File (Logistic Regression):

In this file, I wrote code that predicts which applicants can apply to a specific university and get accepted.
For each training example, we have the applicant’s scores on two exams and the admission decision.
Our task is to build a classification model that estimates an applicant’s probability of admission based on the scores from those two exams.
I used the sigmoid function, which is suitable for logistic regression due to its mathematical properties. Then, I applied gradient descent to find the best w and b for my model.