Data Normalization Methods: Understanding and Choosing the Right One

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SUMMARY

This discussion focuses on data normalization methods, specifically the "Approximate with a normal distribution" and "Scale to the same range" techniques. The first method involves subtracting the mean from each data point and dividing by the standard deviation to achieve a standard normal distribution. The second method entails dividing each score by the highest score to ensure all values fall within a uniform range. Both methods are essential for preparing data for analysis by ensuring comparability across different categories.

PREREQUISITES
  • Understanding of statistical concepts such as mean and standard deviation
  • Familiarity with data preprocessing techniques
  • Basic knowledge of numerical data manipulation
  • Experience with data analysis tools like Python or R
NEXT STEPS
  • Learn how to implement normalization in Python using libraries like NumPy and Pandas
  • Explore the implications of normalization on machine learning model performance
  • Study the differences between normalization and standardization in data preprocessing
  • Investigate other normalization techniques such as Min-Max scaling and Z-score normalization
USEFUL FOR

Data analysts, machine learning practitioners, and anyone involved in data preprocessing and analysis will benefit from this discussion on normalization methods.

aruwin
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Hello, please help me with this problem. The question is in the picture. I don't know how to normzalize the data. How to know which method should be used? I need two methods of normalization here. HELP!
Please teach me in detail.
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aruwin said:
Hello, please help me with this problem. The question is in the picture. I don't know how to normzalize the data. How to know which method should be used? I need two methods of normalization here. HELP!
Please teach me in detail.
View attachment 1755

Hi aruwin! :)

Two different normalization methods might be:
  1. Approximate with a normal distribution.
    Subtract the mean, and divide the result by the standard deviation.
  2. Scale to the same range.
    For instance by dividing each score by the highest one.
 
I like Serena said:
Hi aruwin! :)

Two different normalization methods might be:
  1. Approximate with a normal distribution.
    Subtract the mean, and divide the result by the standard deviation.
  2. Scale to the same range.
    For instance by dividing each score by the highest one.
Thanks for replying but could you please tell me why do you pick those two methods? And tell me how to use them.
 
aruwin said:
Thanks for replying but could you please tell me why do you pick those two methods? And tell me how to use them.

Well, we need to combine the different categories numerically.
For that they have to be "normalized", that is, they need to have a similar range.
I'm just mentioning 2 methods to do it.

How to use it?
Well, for method 1, do you know how to calculate a mean? And a standard deviation?
And for method 2, what do you get if you divide each price by the highest price?
 

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