Answers for "normalizng dataset sklearn"

normalize data python

>>> from sklearn import preprocessing
>>>
>>> data = [100, 10, 2, 32, 31, 949]
>>>
>>> preprocessing.normalize([data])
array([[0.10467389, 0.01046739, 0.00209348, 0.03349564, 0.03244891,0.99335519]])

Scaling features to a range

# Scaling features to a range using MaxAbsScaler

X_train = np.array([[ 1., -1.,  2.],
                    [ 2.,  0.,  0.],
                    [ 0.,  1., -1.]])

max_abs_scaler = preprocessing.MaxAbsScaler()
X_train_maxabs = max_abs_scaler.fit_transform(X_train)
X_train_maxabs
# array([[ 0.5, -1.,  1. ],
#        [ 1. ,  0. ,  0. ],
#        [ 0. ,  1. , -0.5]])
X_test = np.array([[ -3., -1.,  4.]])
X_test_maxabs = max_abs_scaler.transform(X_test)
X_test_maxabs
# array([[-1.5, -1. ,  2. ]])
max_abs_scaler.scale_
# array([2.,  1.,  2.])