One hot Encoding with multiple labels in Python?
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One hot Encoding with multiple labels in Python?

One hot Encoding with multiple labels in Python?

One hot Encoding with multiple labels in Python

0
This python source code does the following: 1. Converts categorical into numerical types.2. Loads the important libraries and modules. 3. Implements multi label binarizer. 4. Creates your own numpy feature matrix. 5.Extracts and interprets the final result
In [1]:
## One hot Encoding with multiple labels in Python 
def Kickstarter_Example_36():
    print()
    print(format('How to do One hot Encode with multiple labels in Python', '*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # Load libraries
    from sklearn.preprocessing import MultiLabelBinarizer

    # Create NumPy array
    y = [('Texas', 'Florida'),
         ('California', 'Alabama'),
         ('Texas', 'Florida'),
         ('Delware', 'Florida'),
         ('Texas', 'Alabama')]

    # Create MultiLabelBinarizer object
    one_hot = MultiLabelBinarizer()

    # One-hot encode data
    print(); print(one_hot.fit_transform(y))

    # View Column Headers
    # View classes
    print(); print(one_hot.classes_)

Kickstarter_Example_36()
*************How to do One hot Encode with multiple labels in Python**************

[[0 0 0 1 1]
 [1 1 0 0 0]
 [0 0 0 1 1]
 [0 0 1 1 0]
 [1 0 0 0 1]]

['Alabama' 'California' 'Delware' 'Florida' 'Texas']

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