Implementing-Machine-Learning-Linear-Regression-by-Scratch

In this python file I am implementing the machine learning algorithms for linear regression from scratch without any ML liibraries like scikit-learn. Algorithms include the normal equation, gradient descent and stochastic gradient descent to a polynomial feature set of x. This is an input matrix of X and have multiple features with different exponent k values. So thus, we must perform our algorithms depending on the fact that X is an input vector matrix and not a linear-one featured vector. The plot class is not shown as it is not mine, as well as the data set. Only the functions from scratch implemented using numpy are shown.