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


Recipe Objective

Many times we need dataset for practice or to test some model so we can create a simulated dataset for any model from python itself.

So this is the recipe on we can Create simulated data for regression in Python.

Step 1 - Import the library

import pandas as pd from sklearn import datasets

We have imported datasets and pandas. These two modules will be required.

Step 2 - Creating the Simulated Data

We can create Datasets for regression by passing the parameters which are required for regression like n_samples, n_features, n_targets etc. The function will give the output as a dataset features, output and coefficient. features, output, coef = datasets.make_regression(n_samples = 80, n_features = 4, n_informative = 4, n_targets = 1, noise = 0.0, coef = True)

Step 3 - Printing the Dataset

Here we have printed the dataset's different components i.e. Features, Output and Coef. print(pd.DataFrame(features, columns=['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4']).head()) print(pd.DataFrame(output, columns=['Target']).head()) print(pd.DataFrame(coef, columns=['True Coefficient Values'])) So the output comes as

   Feature_1  Feature_2  Feature_3  Feature_4
0  -0.061616   0.322765   1.329021  -0.975053
1   0.489019  -0.838662   0.445058  -0.244990
2   0.324046   0.656792  -0.034017  -1.445877
3   0.227775  -0.174360   0.652398  -0.336352
4   0.837811  -2.410269  -0.368019  -1.066476

0  -68.619492
1  -16.114323
2 -122.108491
3  -18.132927
4 -124.770731

   True Coefficient Values
0                26.722153
1                15.494463
2                17.067228
3                97.078600

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