Why is numpy.linalg.pinv() preferred over numpy.linalg.inv() for creating inverse of a matrix in linear regression
If the determinant of the matrix is zero it will not have an inverse and your inv function will not work. This usually happens if your matrix is singular.
But pinv will. This is because pinv returns the inverse of your matrix when it is available and the pseudo inverse when it isn't.
The different results of the functions are because of rounding errors in floating point arithmetic
You can read more about how pseudo inverse works here
inv and pinv are used to compute the (pseudo)-inverse as a standalone matrix. Not to actually use them in the computations.
For such linear system solutions the proper tool to use is numpy.linalg.lstsq (or from scipy) if you have a non invertible coefficient matrix or numpy.linalg.solve (or from scipy) for invertible matrices.