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Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle highly varying magnitudes or values or units.In this approach, the data is scaled to a fixed range - usually 0 to 1. A min-max scaling is typically done via the following equation: X poaled = x Eg. Given a sequence [ 1, 2, 3, 4, 5 ] The min-max scaled version would be [ 0, 0.25, 0.50, 0.75. 1.0 ] # Perform min-max scaling scaling on all the columns of 'df_new' # Store the modified dataframe in a new dataframe 'df_scaled' #YOUR CODE HERE: Pyth # Generate descriptive statistics after min-max scaling # Observe that the min and max statistics have been modified df_scaled.describe() Pyth

Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle highly varying magnitudes or values or units.In this approach, the data is scaled to a fixed range - usually 0 to 1. A min-max scaling is typically done via the following equation: X poaled = x Eg. Given a sequence [ 1, 2, 3, 4, 5 ] The min-max scaled version would be [ 0, 0.25, 0.50, 0.75. 1.0 ] # Perform min-max scaling scaling on all the columns of 'df_new' # Store the modified dataframe in a new dataframe 'df_scaled' #YOUR CODE HERE: Pyth # Generate descriptive statistics after min-max scaling # Observe that the min and max statistics have been modified df_scaled.describe() Pyth

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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Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle highly varying magnitudes or values or units.In this approach, the data is scaled to a fixed range - usually 0 to 1. A min-max scaling is typically done via the following equation: X scaled = x Xmar Eg. Given a sequence [ 1, 2, 3, 4, 5] The min-max scaled version would be [0, 0.25, 0.50, 0.75. 1.0 ] # Perform min-max scaling scaling on all the columns of 'df_new' # Store the modified dataframe in a new dataframe 'df scaled' #YOUR CODE HERE: Python # Generate descriptive statistics after min-max scaling # Observe that the min and max statistics have been modified df scaled.describe() Python
Transcribed Image Text:Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle highly varying magnitudes or values or units.In this approach, the data is scaled to a fixed range - usually 0 to 1. A min-max scaling is typically done via the following equation: X scaled = x Xmar Eg. Given a sequence [ 1, 2, 3, 4, 5] The min-max scaled version would be [0, 0.25, 0.50, 0.75. 1.0 ] # Perform min-max scaling scaling on all the columns of 'df_new' # Store the modified dataframe in a new dataframe 'df scaled' #YOUR CODE HERE: Python # Generate descriptive statistics after min-max scaling # Observe that the min and max statistics have been modified df scaled.describe() Python
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