NORMALIZATION PROBLEM: Normalization is one of the most basic preprocessing techniques in data analytics. This involves centering and scaling process. Centering means subtracting the data from the mean and scaling means dividing with its standard deviation. Mathematically, normalization can be expressed as: X-X 0 In Python, element-wise mean and element-wise standard deviation can be obtained by using .mean() and .std() calls. In this problem, create a random 5 x 5 ndarray and store it to variable X. Normalize X. Save your normalized ndarray as X_normalized.npy

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
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NORMALIZATION PROBLEM: Normalization is one of the most basic preprocessing techniques in
data analytics. This involves centering and scaling process. Centering means subtracting the data from the
mean and scaling means dividing with its standard deviation. Mathematically, normalization can be
expressed as:
X-X
0
In Python, element-wise mean and element-wise standard deviation can be obtained by using .mean() and
.std() calls.
In this problem, create a random 5 x 5 ndarray and store it to variable X. Normalize X. Save your normalized
ndarray as X_normalized.npy
Transcribed Image Text:NORMALIZATION PROBLEM: Normalization is one of the most basic preprocessing techniques in data analytics. This involves centering and scaling process. Centering means subtracting the data from the mean and scaling means dividing with its standard deviation. Mathematically, normalization can be expressed as: X-X 0 In Python, element-wise mean and element-wise standard deviation can be obtained by using .mean() and .std() calls. In this problem, create a random 5 x 5 ndarray and store it to variable X. Normalize X. Save your normalized ndarray as X_normalized.npy
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