Python least squares optimization : I am trying to solve the problem below:
Given the input data (25 columns), find the optimal coefficients and powers to return the least squared sum of errors based on the target value. Coefficients must be bound between [0, inf) and powers are bound between [0,3], both inclusive.

I am having trouble figuring out the best way to do this in Python. I currently have the data in a csv that I imported as a Dataframe. I was thinking scipy.optimize.lsq_linear might work, but I am unsure how this would look because of the large number of input variables. 