write a Python program which can make Nth order polynomial fit with an example

Here is an example Python program that can make an Nth order polynomial fit using the NumPy and Matplotlib libraries:

import numpy as np
import matplotlib.pyplot as plt

# Generate some random data
x = np.linspace(0, 10, 100)
y = 2*x**3 - 5*x**2 + 3*x + np.random.randn(100)*5

# Fit an Nth order polynomial
N = 3
p = np.polyfit(x, y, N)

# Evaluate the polynomial at some new points
x_new = np.linspace(0, 10, 1000)
y_new = np.polyval(p, x_new)

# Plot the original data and the fitted polynomial
plt.scatter(x, y, label='Data')
plt.plot(x_new, y_new, label='Fit')
plt.legend()
plt.show()

In this example, we generate some random data and then fit a 3rd order polynomial to it using the `np.polyfit()` function from the NumPy library. We then evaluate the polynomial at some new points using the `np.polyval()` function and plot both the original data and the fitted polynomial using the Matplotlib library.

Note that the order of the polynomial (`N`) is a parameter that can be changed depending on the complexity of the data and the desired level of accuracy in the fit.