Finance, Risk Analytics, Data Science, Machine Learning.
the data followed by visualising the covariance matrix: # Remove the 'Date' column as it's not needed for standardization df = x.drop(axis=1, columns=['Date']) # Get the values of the DataFrame X = df.values # Calculate the mean for each feature mean = np.mean(X, axis=0) # Calculate the standard