Step 1: Initialize the basic class functions including initialization of regularization parameter C and number of iterations.

Step 2: Implement the Objective function i.e; Hinge Loss Function.

Step 3: After implementing the Hinge Loss Objective function, we will implement the Subgradient calculation function. Now as we know the Hinge loss function is not differentiable, but it is convex.

Step 4: Now we can implement the fit function, which will take in input X and y and learn the w and b parameters using objective function and Subgradient descent algorithm.

Step 5: Lastly, we can implement the predict function.