Normalization and Standardization Statistics With Python
#machinelearningcourse #machinelearningprojects #machinelearningprojectsinpython #pythonforbeginners
#machinelearninginterviewquestions #python #pythonforbeginners #machinelearning #artificialintelligence #machinelearningalgorithmsfordatascience #datascience
#machinelearning #interview #questions #FutureScaling #featurescalinginmachinelearning
#datasciencecourse #datasciencefullcourse
Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization
1.Why Should we Use Feature Scaling?
2.What is Normalization?
3.What is Standardization?
4.The Big Question – Normalize or Standardize?
5.Implementing Feature Scaling in Python
.Normalization using Sklearn
.Standardization using Sklearn
Feature scaling is an important step in machine learning where you transform your data to be on the same scale, so that all features contribute equally to the final model. Two common techniques for feature scaling are normalization and standardization. Here's a brief overview of each technique:
Normalization: Normalization scales the data to have values between 0 and 1. This is done by subtracting the minimum value of the feature and then dividing by the range (i.e. the difference between the maximum and minimum values). The formula for normalization is:
X_normalized = (X - X_min) / (X_max - X_min)
Normalization is useful when you don't know the distribution of your data, or when you have outliers that you want to include in the scaling process.
Standardization: Standardization scales the data to have a mean of 0 and a standard deviation of 1. This is done by subtracting the mean value of the feature and then dividing by the standard deviation. The formula for standardization is:
X_standardized = (X - X_mean) / X_std
Standardization is useful when you have normally distributed data, or when you want to remove the effect of outliers.
Download the Code and Dataset for Future Scaling link below:
https://drive.google.com/drive/folder...
Follow me on: Instagram
/ govinzsharma
Subscribe my Channel for Data Science tutorials:
/ statistikags
#pythonforbeginners #datascience #python #python3 #machinelearning
#machinelearningprojects #machinelearningprojectsinpython #pythonforbeginners
#machinelearninginterviewquestions #python #pythonforbeginners #machinelearning #artificialintelligence #machinelearningalgorithmsfordatascience #datascience
#machinelearning #interview #questions
#datasciencecourse #datasciencefullcourse
Tools Covered
Python
Excel
NumPy
Pandas
SciPy
Descriptive Statistics
And Many More..