How to Create simulated data for regression in Python?

How to Create simulated data for regression in Python?

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(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(pd.DataFrame(features, columns=['Feature 1', 'Feature 2', 'Feature 3']).head())

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

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

**************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

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

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