from scipy.optimize import curve_fit
def model(x, a, b):
return a * x + b
initial_guess = [a,b]
# popt contains the optimal values for a and b
# pcov is the covariance matrix
popt, pcov = curve_fit(func, xdata, ydata, p0=initial_guess)
# Extract the optimized parameters
a_optimized, b_optimized = p
Find Zero using Newton’s Method
from scipy.optimize import newton
x_solution = newton(equation_function, initial_guess_x0, args=(parameters,))