# Tag Info

You can try fitting a multi-dimensional polynomial regression. It seems that for your data, a two dimensional regression model should be fine: $$(a)x^2 + (b)x + (c)y^2 + (d)y + (e)xy + (f)$$ In python for example, you can fit a proper model using: import numpy as np from sklearn.preprocessing import PolynomialFeatures X = np.array([ [x0,y0], [x1,y1], ....