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It is a very effective technique which re-scales a feature value so that it has distribution with 0 mean value and variance equals to 1. Standardization rescales data to have a mean (µ) of 0 and standard deviation (a) of 1 (unit variance). Xtd = X-µ Standardization can be visualized as a shifting and stretching/shrinking process. # Perform standardization on all the columns of 'df_new' # Store the modified dataframe in a new dataframe 'df_std' #YOUR CODE HERE: # Generate descriptive statistics after standardization # Observe that the mean and standard deviation statistics have been modified df std.describe()

It is a very effective technique which re-scales a feature value so that it has distribution with 0 mean value and variance equals to 1. Standardization rescales data to have a mean (µ) of 0 and standard deviation (a) of 1 (unit variance). Xtd = X-µ Standardization can be visualized as a shifting and stretching/shrinking process. # Perform standardization on all the columns of 'df_new' # Store the modified dataframe in a new dataframe 'df_std' #YOUR CODE HERE: # Generate descriptive statistics after standardization # Observe that the mean and standard deviation statistics have been modified df std.describe()

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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# Displaying histogram plots for the 'hp' column plt.figure() plt.subplot(3,1,1) plt.title("Histogram: Original data - 'hp''') ax1 = df_new["hp"].plot.hist(bins=50, alpha=1) plt.subplot(3,1,2) plt.title("Histogram: After min-max scaling - 'hp'') ax2 = df_scaled ["hp"].plot.hist(bins=50, alpha=1) plt.subplot(3,1,3) plt.title("Histogram: After standardization - 'hp'") df std["hp"].plot.hist(bins=50, alpha=1) plt.tight_layout () ax3 =
Transcribed Image Text:# Displaying histogram plots for the 'hp' column plt.figure() plt.subplot(3,1,1) plt.title("Histogram: Original data - 'hp''') ax1 = df_new["hp"].plot.hist(bins=50, alpha=1) plt.subplot(3,1,2) plt.title("Histogram: After min-max scaling - 'hp'') ax2 = df_scaled ["hp"].plot.hist(bins=50, alpha=1) plt.subplot(3,1,3) plt.title("Histogram: After standardization - 'hp'") df std["hp"].plot.hist(bins=50, alpha=1) plt.tight_layout () ax3 =
It is a very effective technique which re-scales a feature value so that it has distribution with 0 mean value and variance equals to 1. Standardization rescales data to have a mean (µ) of 0 and standard deviation (a) of 1 (unit variance). X std X-µ Standardization can be visualized as a shifting and stretching/shrinking process. # Perform standardization on all the columns of 'df_new' # Store the modified dataframe in a new dataframe 'df_std' #YOUR CODE HERE: # Generate descriptive statistics after standardization # Observe that the mean and standard deviation statistics have been modified df std.describe()
Transcribed Image Text:It is a very effective technique which re-scales a feature value so that it has distribution with 0 mean value and variance equals to 1. Standardization rescales data to have a mean (µ) of 0 and standard deviation (a) of 1 (unit variance). X std X-µ Standardization can be visualized as a shifting and stretching/shrinking process. # Perform standardization on all the columns of 'df_new' # Store the modified dataframe in a new dataframe 'df_std' #YOUR CODE HERE: # Generate descriptive statistics after standardization # Observe that the mean and standard deviation statistics have been modified df std.describe()
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