**Write simple code from scratch to standardize data (feature scaling) in Python**

**library sklearn cannot be used for this problem**

Given a an ndarray:

X = np.array([[50, 30], [20, 90], [30, 50]])

1. Calculate the means for each column. The output should be an array with the shape of (2, 1).

2. Calculate the standard deviation for each column. The output should be an array with the shape of (2, 1).

3. For each value, subtract it from the corresponding mean and divide by the corresponding standard deviation. The output should return the 3x3 matrix as shown in the image.

Givena X X2 50 20 30 30 90 50 column means = [ 33.33 56.66 2. column standard deviation - (15.27 30.55 ] X 50-33.33 15.27 30-56.66 30.55 1.09 -0.87 -0.87 1.09 L-0.21 0.21 20-333 15.27 90-56.66 30.55 30 - 33.33 15.29 50 - 56.66 30.55