How to standardise IRIS Data in Python?
0

How to standardise IRIS Data in Python?

This recipe helps you standardise IRIS Data in Python
In [2]:
## How to standarise IRIS Data in Python 
def Kickstarter_Example_41():
    print()
    print(format('How to standarise IRIS Data in Python', '*^82'))
    import warnings
    warnings.filterwarnings("ignore")

    from sklearn import datasets
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
                                                        random_state=42)
    sc = StandardScaler()
    sc.fit(X_train)
    X_train_std = sc.transform(X_train)
    X_test_std = sc.transform(X_test)

    print(); print(X_train[0:5])
    print(); print(X_train_std[0:5])
    print(); print(X_test[0:5])
    print(); print(X_test_std[0:5])
Kickstarter_Example_41()
**********************How to standarise IRIS Data in Python***********************

[[5.5 2.4 3.7 1. ]
 [6.3 2.8 5.1 1.5]
 [6.4 3.1 5.5 1.8]
 [6.6 3.  4.4 1.4]
 [7.2 3.6 6.1 2.5]]

[[-0.4134164  -1.46200287 -0.09951105 -0.32339776]
 [ 0.55122187 -0.50256349  0.71770262  0.35303182]
 [ 0.67180165  0.21701605  0.95119225  0.75888956]
 [ 0.91296121 -0.02284379  0.30909579  0.2177459 ]
 [ 1.63643991  1.41631528  1.30142668  1.70589097]]

[[6.1 2.8 4.7 1.2]
 [5.7 3.8 1.7 0.3]
 [7.7 2.6 6.9 2.3]
 [6.  2.9 4.5 1.5]
 [6.8 2.8 4.8 1.4]]

[[ 0.3100623  -0.50256349  0.484213   -0.05282593]
 [-0.17225683  1.89603497 -1.26695916 -1.27039917]
 [ 2.23933883 -0.98228318  1.76840592  1.43531914]
 [ 0.18948252 -0.26270364  0.36746819  0.35303182]
 [ 1.15412078 -0.50256349  0.54258541  0.2177459 ]]