A framework for benchmarking clustering algorithms

An Implementation of fuzzy clustering algorithms in Numpy

Classification based on Fuzzy Logic(C-Means) - Computational Intelligence Course 2nd Project

Implementation of some of the most used Clustering Algorithms from scratch (only using Numpy)

Simple implementation of the KMeans Clustering algorithm in Python

Approximate Nearest Neighbors for distributed systems using any arbitrary distance function

Speeding up clustering algorithms using Sampling techniques (Lightweight Coresets)

An Engine for Dynamic Enhancement and Noise Overcoming in Spatiotemporal Multimodal Neural Observations via High-density Microelectrode Arrays

Machine learning example code in topics such classification, clustering and recommender systems in different techniques and approaches.

This repository provides classic clustering algorithms and various internal cluster quality validation metrics and also visualization capabilities to analyse the clustering results

Machine Learning codes

A data-driven approach to designing an optimal Data Science curriculum. This project extracts skills from job postings, applies NLP and clustering techniques (K-Means, Hierarchical, DBSCAN), and maps industry demands to educational recommendations. Uses Python, Scikit-learn, OpenAI embeddings, and Seaborn for visualization.

Implement several common used clustering algorithms using python

Digital platforms today need more than static dashboards, they require adaptive systems that continuously learn from user activity. This project develops a behavioural intelligence framework that dynamically groups interaction patterns, surfaces critical risk indicators, and enables strategic actions to influence experience outcomes.

Bachelor thesis is submitted in fulfilment of the requirements for the Bachelor of Science degree in the Department of Business Analytics and Information Technologies at the Faculty of Applied Sciences of Ukrainian Catholic University.