Mean Squared Error (MSE ) is one of the metrices that is used to evaluate the performance of a given model. MSE is very simple to calculate, it represents the difference between the actual and predicted values extracted by squared the average difference over the data set.

Simply, the MSE is calculated as follow:

( 1/n) * S(? - x2 MSE =

X array of the actual values

Y array of the predicted values

E: A symbol that means \"sum\".

n: number of the values

Write a python code to calculate the MSE. The python code should:

1/ Create X and Y NumPy arrays.

2/ Create a new NumPy array \"Diff\" which is the difference between the two arrays X and Y.

3/ Create a new NumPy array \"Sqd\" which is squared of \"Diff\" array values (loop is not allowed).

4/ Calculate \"sum\" which is the summation of the \"Sqd\" array values (loop is not allowed).

5/ Print the MSE, which is the division of the sum by the number of values in Y.

Example:

X= [123 3]

Y = [504 3]

Diff = [4 -2 1 0]

Sqd = [16 4 1 0]

sum = (16+4+1+0)

MSE = sum/4 = 5.25