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

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

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.

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

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

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.