Least Square Method, Curve Fitting, Python, Residual, Data.
np.exp(-1.3 * x_data) y_noise = 0.2 * np.random.normal(size=x_data.size) y_data = y_true + y_noise # Fit the model to the data popt, pcov = curve_fit(model_func, x_data, y_data) # Generate points for the fitted curve x_fit = np.linspace(0, 5, 100) y_fit = model_func(x_fit, *popt) # Plot the