K-means Clustering implementation on random points in 2D plane

Code snippets reflecting various aspects of computer architecture.

k-means, GMM, linear classifier, mlp

DBB: Density Based Bootstrap

Trying out clustering algorithms

K-means clustering for user impressions for Spigot Inc.

Implementation of K-Means Clustering Algorithm for Assignment 03 of the course CS6375: Machine Learning.

Stanford's Machine Learning MOOC from Coursera

K mean clustering

Performance of k-Means and k-Medoid based Data Distribution Pattern

Customer clustering with k-means and DBSCAN

A volunteer management system for distributing packets of food using MERN TECHNOLOGY STACK.

Code for the exam "Introduction to Data Science for physicists" - prof. L. Tubiana (UniTN 2021)

This project aimed to identify patterns in road networks across cities. We looked at 80 cities worldwide. We then used k-means algorithm to find different groups of cities based on the network properties. The commonalities among these groups were also discussed.

Conducted the experiment on three part; first, evaluated the clustering results for Gaussian distribution datasets, following, utilized k-Means algorithm for image compression and employed the algorithms on Mall Customers dataset.

Implementation of the k-means algorithm using serial and parallel approaches.

A Python Package to Create Synthetic Tabular Data

K-Means Clustering

This is a Python implementation of k-means algorithm including elbow method and silhouette method for selecting optimal K

UML dimensionality reduction and clustering models for predicting if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes - practical exercise. (Python 3)