## How to Create simulated data for classification in Python 
def Kickstarter_Example_23():
    print()
    print(format('How to Create simulated data for classification in Python', '*^82'))

    # Load libraries
    from sklearn.datasets import make_classification
    import pandas as pd

    # Create Simulated Data
    # Create a simulated feature matrix and output vector with 100 samples,
    features, output = make_classification(n_samples = 100,
                                       n_features = 10,
                                       n_informative = 10,
                                       n_redundant = 0,
                                       n_classes = 3,
                                       weights = [.2, .3, .8])

    # View the first five observations and their 10 features
    print()
    print("Feature Matrix: ");
    print(pd.DataFrame(features, columns=['Feature 1', 'Feature 2', 'Feature 3',
         'Feature 4', 'Feature 5', 'Feature 6', 'Feature 7', 'Feature 8', 'Feature 9',
         'Feature 10']).head())

    # View the first five observation's classes
    print()
    print("Target Class: ");
    print(pd.DataFrame(output, columns=['TargetClass']).head())

Kickstarter_Example_23()