Feature Scaling and Normalization Techniques in Python
Feature scaling and normalization are essential techniques for data preprocessing in machine learning. These techniques are used to scale and transform the input data to a similar range, allowing the machine learning algorithms to learn effectively. In this tutorial, we will discuss various feature scaling and normalization techniques in Python.
What is Feature Scaling and Normalization?
Feature scaling and normalization are methods used to transform the input data to a similar scale or range. These techniques are used to handle data with different ranges, units, and magnitudes. Feature scaling is generally used when the features have different units, and normalization is used when the features have different ranges.
There are several techniques used for feature scaling and normalization, including:
- Standardization
- Min-Max Scaling
- Robust Scaling
- Log Transformation
