k-means-algorithm

K-Means algorithm parallelized in CUDA

This repository aims to provide an overview of various clustering methods, along with practical examples and implementations.

An API for managing chat completions, fine-tuning, payments, plans, and configurations.

Turn your best memories into plotter art

In this Python notebook, we explore how K-Means can be used for customer segmentation to gain a competitive advantage and improve a business's bottom line.

This program implements the K-means clustering algorithm using OpenMP APIs. The K-means algorithm is a popular method of vector quantization that aims to partition n observations into k clusters. Each observation is assigned to the cluster with the nearest mean, serving as a prototype of the cluster.

This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.

This repo contains the Implementation of K-Means Clustering Algorithm from scratch and an Image Segmentation Project, implemented using the same algorithm.

Customer Segmentation using R

This Repo contains various Machine learning Algorithm including Linear regression, Logistic regression, Neural Networks, SVM, Clustering algorithms, K-means Algorithm, Anomaly detection, and Recommander system etc...

K-means clustering algorithm using MapReduce.

A pipe-friendly command-line tool for k-means clustering and neighbor analysis. Built around Unix principles: read from stdin, write to stdout, and stay composable. Ideal for shell pipelines, data exploration, and automation on macOS and Linux.

A C implementation of K-Means clustering algorithm with Python bindings

K-means algorithm is implemented from scratch for clustering on iris dataset and MNIST dataset.

The K -Means algorithm implementation from scratch in Python based on Euclidean distance

Parallel-K-Means-Algorithm

A movie recommendation engine built with python and a Qt GUI.

Gen-AI powered Customer Segmentation and Retention analysis system. Utilizing over 100,000 Olist records, employs RFM-T Feature Engineering and K-Means Clustering for behavioral analysis. A Generative AI agent translates technical clusters into detailed, personalized personas for retention strategies, visualized via a Streamlit Dashboard.

🚀 GPU-accelerated prototype learning framework. Condenses infinite data streams into differentiable topological grids, reducing metric inference complexity from O(N×D) to strictly O(K×D) .