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 and Normalization.