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

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

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.

Parallel-K-Means-Algorithm

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

An analysis using unsupervised Machine Learning algorithm to discover unknown patterns

This repository contains an example of using K-means clustering to partition data into distinct groups based on similarity.

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

Cluster Visualization Tool

Customer Segmentation using R

K-Means algorithm parallelized in CUDA

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.

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.

Implementation of k-means algorithm in Zig

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.