Why is normalization important in data processing?

Why is normalization important in data processing?

Normalization is crucial as it helps to scale data, reduce redundancy, and improve the accuracy of machine learning models.

Can you explain how normalization affects machine learning?

Certainly! Normalization ensures that features contribute equally to the distance calculations, which enhances model performance.

What are the different methods of normalization?

Common methods include Min-Max scaling, Z-score normalization, and decimal scaling, each serving different purposes.

When should I use normalization in my data?

Normalization is recommended when your data features have different scales or when using algorithms sensitive to feature magnitudes.