Data normalization in Python

Normalize your data in 4 different methods in Python

What is data normalization?

Normalization of data refers to scaling and transforming data so that its values fall within a specific range. Data normalization is often performed to make the data easier to interpret or improve the performance of machine learning models. The primary goal of normalization is to ensure that different features (variables) contribute equally to the model, especially when the features have different units, scales, or ranges.

For example, in a dataset where one feature represents distances in kilometers and another feature represents weight in kilograms, the numerical ranges of these features might differ significantly. Normalizing the data ensures that both features are on a similar scale, which helps prevent models from being biased toward larger-scale features.

Why normalization is important?

Normalization is crucial in machine learning because many algorithms, particularly distance-based algorithms (e.g., k-NN, SVM), are sensitive to the scale of input data. Without normalization, features with larger ranges dominate distance calculations, leading to biased results.

Normalization ensures that features contribute equally to the model, improving performance and convergence speed, especially in gradient descent-based models like neural networks.