Machine Learning Regression

These are two simple codes from my collection. The first code Sklearn_Polynomial_Interpolation_from_CSV_with_GUI.py is an GUI made for polynomial regression. When running a simple window opens which gives an option to open a CSV file which then plots the points of the CSV. After you can choose the max polynomial of which you want your line to fit. Finally after the line has been fit it instantly tells you the equation of the fit line. The second Stochastic_Gradient_Descent_Linear_Regression.py start by plotting linear points with noise. Then goes onto to using stochastic gradient descent to find the minimum error of the fit line.

Using Sklearn Polynomial Interpolation

Here we can see a cubic function being fit to a cubic data set. We can see the equation of the unknown data set in the bottom right.

Here we can see a linear function being fit to the same cubic data set.

Using Stochastic Methods

Here we can see the line slowly fitting it's data point by minimizing the fit error. If a user wishes they can import their own data but the Sklearn code would be a better option.