model-based-clustering

R Package With Shiny App: Permit 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 with Shiny App: Permit 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.

Model-based clustering with variable selection for missing data

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.

Python code to fit parsimonious Markov models

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

Model-Based Clustering and Variable Selection for Multivariate Count Data

A Predictive View of Bayesian Clustering

R & Python | Unsupervised Learning Project

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

Clustering NBA players and teams through Model-Based methods