Thread by @ItsMikaeil

Let's assume you have three Features(age, height, salary) in your example.
The first feature varies from 1 to 90. The second one varies from 120 to 210 and the Third one varies from 1000 Euro to 4500 Euro.
#Thread
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As you can see the value of your features are in a different range. In this case, if you want to use gradient descent to find optimum parameters for your model( for instance linear regression), that leads to a slow speed of your model to converge. In this case,
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you can utilize Feature Scaling to bring the value of features in a range from 0 to 1 depending on the Scaling technique, that you use. So you improve the speed of your model convergence.

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The Algorithms that need Feature Scaling:

1- KNN
2- Neural Network,
3- Linear Regression
4- Logistic Regression
5- SVM
6- PCA
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Those that don't need Feature Scaling:

1- Decision trees
2-Random Forest
3- AdaBoost
4-Naïve Bayes
5- XGBoost

RT/Like makes me happy.

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