How to Create simulated data for regression in Python?

This recipe helps you Create simulated data for regression in Python
In [1]:
## How to Create simulated data for regression in Python 
def Kickstarter_Example_22():
    print()
    print(format('How to Create simulated data for regression in Python', '*^82'))

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

    # Create Simulated Data
    # Generate fetures, outputs, and true coefficient of 100 samples,
    features, output, coef = make_regression(n_samples = 100, n_features = 3,
                                n_informative = 3, n_targets = 1,
                                noise = 0.0, coef = True)

    # View Simulated Data
    # View the features of the first five rows
    print()
    print(pd.DataFrame(features, columns=['Feature 1', 'Feature 2', 'Feature 3']).head())

    # View the output of the first five rows
    print()
    print(pd.DataFrame(output, columns=['Target']).head())

    # View the actual, true coefficients used to generate the data
    print()
    print(pd.DataFrame(coef, columns=['True Coefficient Values']))

Kickstarter_Example_22()
**************How to Create simulated data for regression in Python***************

   Feature 1  Feature 2  Feature 3
0  -1.361349   1.982526  -1.144529
1  -0.925345  -0.861086   0.137837
2   0.419575  -1.925695  -0.178226
3   0.053922  -0.520252  -0.386195
4  -0.397518   1.220856  -0.893696

       Target
0 -145.429093
1  -51.263123
2  -30.024469
3  -43.656632
4  -84.419295

   True Coefficient Values
0                52.736720
1                18.208275
2                95.877359