Generating Synthetic Data with Numpy and Scikit-Learn



In this tutorial, we’ll discuss the details of generating different synthetic datasets using Numpy and Scikit-learn libraries. We’ll see how different samples can be generated from various distributions with known parameters.

We’ll also discuss generating datasets for different purposes, such as regression, classification, and clustering. At the end we’ll see how we can generate a dataset that mimics the distribution of an existing dataset.

The Need for Synthetic Data

In data science, synthetic data plays a very important role. It allows us to test a new algorithm under controlled conditions. In other words, we can generate data that tests a very specific property or behavior of our algorithm.

For example, we can test its performance on balanced vs. imbalanced datasets, or we can evaluate its performance under different noise levels. By doing this, we can establish a baseline of our algorithm’s performance under various scenarios.




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