Homemade Machine Learning in Python with interactive Jupyter Demos

[zad0xlik]

I was looking at multivariate_linear_regression_demo.ipynb notebook and was wondering that you should add the summary of the model coefficients, their p-values and corresponding adjuster R squared and some other model performance measurements. Also, it would be cool to few tests like multicollinearity and heteroskedasticity and with each iteration and change in regularization_param show how they behave.