How to Create simulated data for classification in Python?
DATA MUNGING

How to Create simulated data for classification in Python?

How to Create simulated data for classification in Python?

This recipe helps you Create simulated data for classification in Python

0
This data science python source code does the following: 1.Creates custom classification types datasets 2.How to use parameters related to classification in "make_classification". 3. Obtaining the features and classes and the target variable.
In [1]:
## 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()
************How to Create simulated data for classification in Python*************

Feature Matrix:
   Feature 1  Feature 2  Feature 3  Feature 4  Feature 5  Feature 6  \
0  -1.867524   1.745983   2.952435  -0.177492  -3.088648   1.762311
1   0.450144  -2.106431  -1.065847  -1.958231  -0.451780  -1.990662
2  -4.647836  -4.214226  -1.830341  -1.714825  -6.590249  -0.315993
3   1.958901  -1.313546   1.409145  -2.069271   1.508912   3.774923
4   2.001750   0.879350  -2.041154   1.917629  -0.760137   1.310228

   Feature 7  Feature 8  Feature 9  Feature 10
0  -0.195266   1.029769   2.814171    0.071059
1  -2.530104  -1.377802  -0.013353   -2.849859
2   2.780038  -3.325841  -4.008319    2.001941
3   5.012315  -5.772415  -0.818187   -0.392333
4   0.671990   1.444606  -1.731576   -0.378597

Target Class:
   TargetClass
0            1
1            2
2            1
3            0
4            0

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