Save MinMaxScaler model in sklearn

Even better than pickle (which creates much larger files than this method), you can use sklearn's built-in tool:

from sklearn.externals import joblibscaler_filename = "scaler.save"joblib.dump(scaler, scaler_filename) # And now to load...scaler = joblib.load(scaler_filename) 

Note: sklearn.externals.joblib is deprecated. Install and use the pure joblib instead

So I'm actually not an expert with this but from a bit of research and a few helpful links, I think pickle and sklearn.externals.joblib are going to be your friends here.

The package pickle lets you save models or "dump" models to a file.

I think this link is also helpful. It talks about creating a persistence model. Something that you're going to want to try is:

# could use: import pickle... however let's do something elsefrom sklearn.externals import joblib # this is more efficient than pickle for things like large numpy arrays# ... which sklearn models often have.   # then just 'dump' your filejoblib.dump(clf, 'my_dope_model.pkl') 

Here is where you can learn more about the sklearn externals.

Let me know if that doesn't help or I'm not understanding something about your model.

Note: sklearn.externals.joblib is deprecated. Install and use the pure joblib instead

Just a note that sklearn.externals.joblib has been deprecated and is superseded by plain old joblib, which can be installed with pip install joblib:

import joblibjoblib.dump(my_scaler, 'scaler.gz')my_scaler = joblib.load('scaler.gz')

Note that file extensions can be anything, but if it is one of ['.z', '.gz', '.bz2', '.xz', '.lzma'] then the corresponding compression protocol will be used. Docs for joblib.dump() and joblib.load() methods.