## How to Create simulated data for clustering in Python 
def Kickstarter_Example_24():
    print()
    print(format('How to Create simulated data for clustering in Python', '*^82'))

    # Load libraries
    from sklearn.datasets import make_blobs
    import matplotlib.pyplot as plt
    import pandas as pd

    # Make the features (X) and output (y) with 200 samples,
    features, clusters = make_blobs(n_samples = 2000,
                  n_features = 10, centers = 5,
                  # with .5 cluster standard deviation,
                  cluster_std = 0.4,
                  shuffle = True)

    # 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())

    # Create a scatterplot of the first and second features
    plt.scatter(features[:,0], features[:,1])

    # Show the scatterplot
    plt.show()

Kickstarter_Example_24()