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

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)

Repository for Customer Segment Analysis using Python & Shiny App Dashboard

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.

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

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

[CSE 4255] Introduction to Data Mining and Warehousing Lab

Improve Text Categorization using k-Medoids Clustering Feature Selection

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.

Comparing different clustering algorithms

statistical inference project with the task of clustering

Segmenting with Mixed Type Data - A Case Study Using K-Medoids on Subscription Data