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print('beta_1 = %f, beta_2 = %f' % (popt[0],popt[1])) >> beta_1 = 690.453017, beta_2 = 0.997207 So now that we have the ideal parameters, thanks to the curve_fit() method, we shall use them to fit our model, in other to minimize the sum of squared differences between each prediction and its