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

A data science project for customer segmentation using K-Medoids and PCA. Analyzing the "Customer Personality Analysis" dataset from Kaggle to identify customer archetypes and enable targeted marketing strategies.

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.

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

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

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.

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)

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

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

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

[CSE 4255] Introduction to Data Mining and Warehousing Lab

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

statistical inference project with the task of clustering

Graph clustering project using Markov clustering algorithm, K-medoid algorithm, Spectral algorithm with GUI PyQt5

Selection of the best centroid based clustering version with k-medoids and k-means

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.