Feature scaling is a crucial step in data preprocessing for machine learning algorithms. It involves transforming the values of numerical features into a specific range. This process ensures that all features contribute equally to the machine learning model, preventing any one feature from dominating the others.

Feature scaling is necessary because most machine learning algorithms are sensitive to the magnitude of features. When features have different scales, the algorithm may give undue importance to features with larger values. This can lead to biased or inaccurate results.

There are several methods for feature scaling, including normalization and standardization.

Normalization, also known as min-max scaling, transforms the values of features to a range between 0 and 1. This method subtracts the minimum value of the feature and divides it by the range (maximum - minimum). Normalized features will have a minimum value of 0 and a maximum value of 1.

Standardization, on the other hand, transforms the values of features to have a mean of 0 and a standard deviation of 1. This method subtracts the mean of the feature and divides it by the standard deviation. Standardized features will have a mean of 0 and a standard deviation of 1.

Both normalization and standardization have their advantages and use cases. Normalization is suitable when the data does not follow a Gaussian distribution, while standardization is more appropriate when the data follows a Gaussian distribution.

In conclusion, feature scaling is an important step in data preprocessing for machine learning. It ensures that all features are on a similar scale, preventing any one feature from dominating the model. Normalization and standardization are two common methods used for feature scaling, each with its own advantages.