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.

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.

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 C implementation of K-Means clustering algorithm with Python bindings

Customer Segmentation using R

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

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

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.

Unsupervised learning algorithms are used here. agglomerative algorithms and k-means clustering are used here.

We calculate how a country should shift from alert to a warning condition using Action-Rules. This focus also emphasizes the importance of each dataset attribute in generating the country's fragile state index.