Gaussian Mixture Models (GMM) Clustering in Python

Gaussian Mixture Model (GMM) is a probabilistic model used for clustering, density estimation, and dimensionality reduction. It is a powerful algorithm for discovering underlying patterns in a dataset. In this tutorial, we will learn how to implement GMM clustering in Python using the scikit-learn library.

Step 1: Import Libraries

First, we need to import the required libraries. We will be using the numpy, matplotlib, and scikit-learn libraries.

import numpy as np
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture

The above code imports the necessary libraries to implement Gaussian Mixture Models (GMM) clustering in Python.

numpy is a popular library for numerical computing in Python. We import it and give it the alias np for brevity.

matplotlib.pyplot is a module within the Matplotlib library that provides a convenient interface for creating plots and charts. We import it and give it the alias plt for brevity.

sklearn.mixture is a module within the Scikit-learn library that provides a class for implementing GMM clustering. We import the GaussianMixture class from this module.