How to code Logistic Regression from scratch with NumPy

Let's first think of the underlying math that we want to use. In the above equations, X is the input matrix that contains observations on the row axis and features on the column axis; y is a column vector that contains the classification labels (0 or 1); f is the sum of squared errors loss function; h is the loss function for the MLE method. So, this is our goal: translate the above equations into code. We plan to use an object-oriented approach for implementation. We'll create a LogisticRegression class with 3 public methods: fit(), predict(), and accuracy().