Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.

E-commerce customers automatic grouping by unsupervised ML/AI. Data from the Kaggle Olist dataset

Calculating pairwise euclidean distance matrix for horizontally partitioned data in federated learning environment

Library and hand-made clustering algorithms are implemented in this project

Exploration and analysis of socio-economic and health data from 167 countries using MATLAB. Application of clustering algorithms to identify development patterns, visualize disparities, and understand global trends.

Projetos da disciplinas Clusterização de Dados, da Pós-Graduação MIT em Inteligência Artificial, Machine Learning e Deep Learning do Instituto Infnet

Conducted a comprehensive clustering analysis to categorize beers based on features such as Astringency, Alcohol content, Bitterness, Sourness, and more. Utilized k-medoids and hierarchical agglomerative clustering algorithms to achieve this classification. Tech: Python (numpy, pandas, seaborn, matplotlib, sklearn, scipy)

This is a capstone research project for my Certificate in Applied Data Science (CADS) at my undergraduate institution, Wesleyan University, on the topic of "Understanding the Variances in COVID-19 Pandemic Outcome - Excess Mortality - with Social, Cultural, and Environmental Factors", sponsored by Prof. Maryam Gooyabadi.

Implementation of k-medoids algorithm in C (standard C89/C90)

Conducted a comprehensive clustering analysis to categorize beers based on features such as Astringency, Alcohol content, Bitterness, Sourness, and more. Utilized k-medoids and hierarchical agglomerative clustering algorithms to achieve this classification. Tech: Python (numpy, pandas, seaborn, matplotlib, sklearn, scipy)

This project focuses on customer segmentation using unsupervised machine learning techniques. The goal is to analyze customer data, identify distinct customer groups (clusters), and extract useful insights for business decision-making.

Repository for Customer Segment Analysis using Python & Shiny App Dashboard

A fun side project to perform machine learning algorithms using plain java code.

[CSE 4255] Introduction to Data Mining and Warehousing Lab

Improve Text Categorization using k-Medoids Clustering Feature Selection

statistical inference project with the task of clustering

An interactive, visual way to understand the famous K-Means,K-medoids and DBSCAN clustering algorithms in real time using Python and Pygame.

Use unsupervised machine learning techniques to explore the Leukemia dataset by focusing more on dimensional reduction and clustering to find similarities between samples or how they are related to each other.

Analytical and computational exploration of clustering algorithms, focusing on k-means and k-medians, with MATLAB implementations and synthetic dataset analyses.