write a simple code that generates 10 random (y,x) data that are around a simple eponential and a least square fit of these data with aa y=a*exp(b*x)+c target function. in python

main.py
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt

# Generate random data around an exponential function
np.random.seed(0)
x_data = np.random.uniform(0, 5, 10)
y_data = 2 * np.exp(0.5 * x_data) + 1 + np.random.normal(0, 0.5, 10)

# Define the target function
def func(x, a, b, c):
    return a * np.exp(b * x) + c

# Perform a least squares fit
popt, pcov = curve_fit(func, x_data, y_data)

# Print the fitted parameters
print('Fitted parameters: a = %.2f, b = %.2f, c = %.2f' % tuple(popt))

# Generate x values for plotting the fitted function
x_fit = np.linspace(0, 5, 100)

# Generate the fitted function values
y_fit = func(x_fit, *popt)

# Plot the data and the fitted function
plt.plot(x_data, y_data, 'ko', label='Data')
plt.plot(x_fit, y_fit, 'r-', label='Fit')
plt.legend()
plt.show()
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