When should I use data normalization?

When should I use data normalization in my data analysis?

Data normalization is essential when your data features have different scales. It helps improve model performance.

Can you give me an example of when normalization is necessary?

Sure! If you're using a dataset with income and age, income might range from thousands to millions, while age is just 0-100. Normalizing helps treat both features equally.

What are the different methods of normalization?

Common methods include Min-Max scaling, Z-score normalization, and Robust scaling. Each has its use cases depending on the data distribution.

Are there situations where I shouldn't normalize my data?

Yes, if your data is already on a similar scale or if you're using algorithms that are scale-invariant, like tree-based models, normalization may not be necessary.