python-scikit-learnScaling data using MinMaxScaler
from sklearn import datasets, preprocessing
data = datasets.load_diabetes(scaled=False).data
scl = preprocessing.MinMaxScaler()
scl.fit(data)
data_scaled = scl.transform(data)ctrl + cfrom sklearn importimport module from scikit-learn | datasets.load_diabetesloads sample diabetes database |
.MinMaxScaler(Scales each feature than values are distributed in | scl.fit(fit scaler |
scl.transform(scale data using trained scaler | data_scaledwill contain scaled dataset |
Usage example
from sklearn import datasets, preprocessing
data = datasets.load_diabetes(scaled=False).data
print(data[0])
scl = preprocessing.MinMaxScaler()
scl.fit(data)
data_scaled = scl.transform(data)
print(data_scaled[0])output
[ 59. 2. 32.1 101. 157. 93.2 38. 4.
4.8598 87. ]
[0.66666667 1. 0.58264463 0.54929577 0.29411765 0.25697211
0.20779221 0.28208745 0.562217 0.43939394]
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