How do I choose the right normalization technique for my data?

How do I choose the right normalization technique for my data?

Choosing the right normalization technique depends on your data's distribution and the analysis you plan to perform. Common techniques include Min-Max scaling, Z-score normalization, and Robust scaling.

What factors should I consider when selecting a normalization method?

You should consider the scale of your data, the presence of outliers, and the specific requirements of the algorithms you plan to use.

Can you explain the difference between Min-Max scaling and Z-score normalization?

Min-Max scaling rescales the data to a fixed range, usually [0, 1], while Z-score normalization standardizes the data based on its mean and standard deviation, resulting in a distribution with a mean of 0 and a standard deviation of 1.

When should I use Robust scaling?

Robust scaling is ideal when your data contains outliers, as it uses the median and interquartile range, making it less sensitive to extreme values.