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.

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.

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

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

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.

Customer Segmentation using R

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

In this two cluster approaches are used: hierarchical clustering and K-means clustering. It is unsupervised learning technique for grouping related data points which shows same behaviour in the dataset regardless of the outcome.

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

In this project, I used unsupervised machine learning techniques to analyze cryptocurrency data.

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

An analysis using unsupervised Machine Learning algorithm to discover unknown patterns

This project segments customers based on Annual Income and Spending Score using K-Means Clustering. It includes data exploration, cluster analysis, and visualization of customer groups. An interactive Streamlit app allows users to upload datasets, perform clustering, and visualize results. This tool helps businesses understand customer behavior.