Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering

Animated Visualizations of Popular Machine Learning Algorithms

Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering

An implementation of K-Means algorithm in R

It's a package containing functions that allow you to create your own color palette from an image, using mathematical algorithms

Repository for Udemy Course: Identify problems with Artificial Intelligence

Kmeans is an R package providing an implementation of the K-Means clustering algorithm. The package includes additional features such as visualization of clustering results and performance benchmarking against the base R kmeans function.

Designing and applying unsupervised learning on the Radar signals to perform clustering using K-means and Expectation maximization for Gausian mixture models to study ionosphere structure. Both the algorithms have been implemented without the use of any built-in packages. The Dataset can be found here: https://archive.ics.uci.edu/ml/datasets/ion…

The feature of interest is whether or not a customer buys a caravan insurance, based on socio-demographic factors and ownership of other insurance policies; and to build profile of a typical customer.

Imagine you are the front runner for democratic party primaries in 2008 - 1 week into elections you have won a few states(Obama) and your opponent (Hillary) is catching up. How you can use analytics to predict which of the remaining seats will you win using demographic data from states you won and lost. Can we accurately classify win or lose for…

Comparison of kmeans and supercells for image semgentiation in R

This repository contains R scripts for performing logistic regression and Naive Bayes classification on various datasets. The scripts demonstrate data loading, visualization, model training, prediction, and evaluation.

Here are some of the algorithms for machine learning mostly clustering.....

R exercises (2016)

Using Machine Learning tools to predict a patient's diagnosis from biopsy data

Cluster analysis - using different approaches

This project implements canonical correlation analysis between two data matrices. I first create the latent dimensions between the two data matrices. Then I use Kmeans and hierarchical clustering on principal component to group individuals using the latent dimensions and the distance created by the canonical analysis. Last step, I give a profili…

KNN (K nearest neighbors) and KMeans clustering implementation using the infamous iris dataset using the R language

K-means as an unsupervised machine learning technique. Customer Segmentation Case.

A project experimenting with implementing clustering algorithms in R