5.6 Structuring data using scale() and MinMaxScaler() The hmeq small dataset contains information on 5960 home equity loans, including 7 features on the characteristics of the loan. - Load the hmeq_small.csv data set as a data frame - Standardize the data set as a new data frame. - Normalize the data set as a new data frame. - Print the means and standard deviations of both the standardized and normalized data main.py Load default template... 1 import pandas as pd 2 from sklearn import preprocessing 3 4 hmeq = pandas. read_cSv('hmeq_small.csv') 5 6 \# Standardize the data 7 standardized = 8 9 \# Output the standardized data as a data frame 10 hmeqStand = \# Your code here 11 12 \# Normalize the data 13 normalized = \# Your code here 14 \# Output the normalized data as a data frame 15 \# Our code here 16 hmeqNorm = \# Your con \# Print the means and standard deviations of hmeqStand and hmeqNorm \# Standardize the data standardized = 8 9 \# Output the standardized data as a data frame 10 hmegstand = \# Your code here 11 12 \# Normalize the data 13 normalized =# Your code here 14 15 \# Output the normalized data as a data frame 16 hmeqNorm = \# Your code here 17 18 \# Print the means and standard deviations of hmeastand and hmeaNorm 19 print ("The means of hmeqstand are ", \# Your code here) 20 print("The standard deviations of hmeqstand are ", \# Your code here) 21 print ("The means of hmeqNorm are ", \# Your code here) 22 print("The standard deviations of hmeqNorm are ", \# Your code here)