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)
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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F39628135-d8f9-472b-a48d-a4792a06b79c%2Fde0c53a9-7955-4c27-85de-0da0990e95f4%2Five6vl_processed.png&w=3840&q=75)
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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