You are asked to implement three different data preprocessing approaches:

Min-Max Scaling: Scaling each column of the data to be between lower and higher.

Standard Scaling: Scale each column of the data so that it has zero mean and unit variance

Max Filling: Replace any missing data with the maximum of the non-missing data

Assume that the given data is a 2D NumPy array of shape (n,m).

You do not need to do any error handling.

It is strongly recommended to test your methods to check whether they work as expected.

def fill_with_max(data, inPlace = False): "" This function replaces the missing data (given as np.nan) with the maximum of the rest of the data. Example: data = [ [np.nan, np.nan, 3,5],[2,4,6,8]] Then output =[[2,4,3,5],[2,4,6,8]] Input: data: A 2 D numpy array of shape (n,m) Output: The filled version of the data, still a 2D numpy array of shape (n,m) WARNINGs: - This is per column so do not mix the maximum of one column with the other "" \# YouR CODE HERE