R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model

Model-based time series clustering using variational inference.

**Unsupervised-Learning**(with practice of PCA, ICA and Model-based Clustering)

Python wrapper for R's Mclust algorithm: Gaussian model-based clustering with automatic model selection via BIC

Infinite Mixtures of Infinite Factor Analysers

R Package to Perform Clustering of Three-way Count Data Using Mixtures of Matrix Variate Poisson-log Normal Model With Parameter Estimation via MCMC-EM, Variational Gaussian Approximations, or a Hybrid Approach Combining Both.

Gaussian Parsimonious Clustering Models with Gating and Expert Network Covariates

Unsupervised Learning

Mixtures of Exponential-Distance Models for Clustering Longitudinal Life-Course Sequences with Gating Covariates and Sampling Weights

This project is an extension of the Gaussian Mixture Regression (GMR) model to handel censored multivariate responses.

This code is part of the "Comparison of K-Means and Model-Based Clustering methods for drill core pseudo-log generation based on X-Ray Fluorescence Data" written by researchers of the Directory of Geology and Mineral Resources from the Geological Survey of Brazil – CPRM.

Analyze World Happiness Report data for 168 countries and generate machine learning forecasts through 2030 using Python, PyTorch, and scikit-learn.

EMMIX fits the data into the specified multivariate mixture models via the EM Algorithm.

Python code to fit parsimonious Markov models

Model-Based Clustering and Variable Selection for Multivariate Count Data

A Predictive View of Bayesian Clustering

Clustering NBA players and teams through Model-Based methods

This repository contains projects for the STATS 790 Statistical Learning course at McMaster University completed during my master's studies.

R & Python | Unsupervised Learning Project

Bayesian Specification of model-based clustering