Gaussian Mixture Model (GMM) for clustering - calculate AIC/BIC

In this video, I tried to implement Gaussian Mixture Model (GMM) for clustering using Scikit-Learn. Gaussian Mixture Models (GMMs) assume that a certain number of Gaussian distributions exist within a dataset. Therefore, each Gaussian distribution represents a particular cluster. We can also calculate AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) in GMM clustering to determine the best fit. GitHub address: github.com/randomaccess2023/MG2023/tree/main/Video… For more details, check Scikit-Learn documentation: scikit-learn.org/stable/modules/mixture.html#gmm 01:04 Import the required libraries 02:50 Load penguins dataset 04:45 Drop NaN values 07:26 Replace categorical variables with numeric values 08:51 Select features and targets 10:25 Perform preprocessing 10:57 Perform GMM for clustering 12:46 Comparison of predictions with targets 17:02 Calculate AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) to determine the best fit #datascience #clustering #python #jupyternotebook #unsupervisedlearning #GaussianMixtureModel #distributionbasedclustering #sklearn #matplotlib

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