How does normalization relate to flattening?

Can you explain how normalization is related to flattening in data processing?

Normalization is the process of adjusting values in a dataset to a common scale, while flattening involves transforming nested data structures into a single-level structure.

Why is normalization important before flattening data?

Normalization ensures that the data is on a similar scale, which helps in reducing bias and improving the accuracy of the analysis after flattening.

Can you give an example of normalization and flattening?

Sure! For instance, if you have a dataset with varying ranges of values, normalization might scale them between 0 and 1. Flattening would then take this normalized data and convert it from a multi-dimensional array into a single list.

What are some common techniques for normalization?

Common techniques include Min-Max scaling, Z-score normalization, and decimal scaling. Each method has its own use cases depending on the data.