I\'m using the MinMaxScaler model in sklearn to normalize the features of a model.
MinMaxScaler
training_set = np.random.rand(4,4)*10 training_set [[
You can use pickle, to save the scaler:
pickle
import pickle scalerfile = 'scaler.sav' pickle.dump(scaler, open(scalerfile, 'wb'))
Load it back:
import pickle scalerfile = 'scaler.sav' scaler = pickle.load(open(scalerfile, 'rb')) test_scaled_set = scaler.transform(test_set)