> For the complete documentation index, see [llms.txt](https://digitalgarden.batamladen.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://digitalgarden.batamladen.com/notes/machine-learning/feature-engineering/feature-scaling.md).

# Feature Scaling

In Machine Learning we train our data to predict or classify things in such a manner that isn’t hardcoded in the machine. So for the first, we have the Dataset or the input data to be pre-processed and manipulated for our desired outcomes. Any ML Model to be built follows the following procedure:

* Collect Data
* Perform Data Munging/Cleaning (Feature Scaling)
* Pre-Process Data
* Apply Visualizations

Scailing is required when we use any machine learning algorithm that requires gradient calculation

**Feature Scaling** is a method to standardize the features present in the data in a fixed range. It has to perform during the data pre-processing. It has two main ways:[ Standardization](/notes/machine-learning/feature-engineering/feature-scaling/standardization.md) and [Normalization](/notes/machine-learning/feature-engineering/feature-scaling/normalization.md).


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