Here’s a summary of StandardScaler and MinMaxScaler in Scikit-learn:
StandardScaler:
Makes all features have a mean of 0 and a standard deviation of 1. Use it when your parts have different scales, and you want them to have equal importance. It’s suitable for models like SVM, logistic regression, and k-means.
MinMaxScaler:
Scales feature a range between 0 and 1. Use it when you want to maintain the original distribution of your data and your features have similar minimum and maximum values. It’s useful for algorithms like k-nearest neighbors and neural networks with certain activation functions.
In summary, choose StandardScaler when you want standardized features with mean 0 and variance 1, and choose MinMaxScaler when you want to preserve the original data distribution and scale features to a specific range.
Workflow:-
Sample input:-
Output:-
