In this machine learning project, we will make use of K-means clustering which is the essential algorithm for clustering unlabeled dataset.

Analyzing the vast data of learners can uncover patterns in their professional backgrounds and preferences. Allowing Scaler to make tailored content recommendations and provide specialized mentorship.

IU Projects

Implementation of the FLS++ algorithm for K-Means clustering.

BICO is a fast streaming algorithm to compute coresets for the k-means problem on very large sets of points.

Exploring the Relationship Between UFOs, Location, Time, and Human Emotion [SQL, Python]

Discovering energy consumption patterns of residential and commercial users.

Project for the University Master Degree Course of Information Retrieval

K-Means Image Compression is a Python-based project that compresses an image by reducing the number of colors used. This technique is implemented using the K-Means clustering algorithm, making it ideal for those looking to understand and apply machine learning concepts in image processing.

Grouping of drinks according to their nutritional values, making it easier to categorize them in a future catalog, increasing organization and facilitating the search depending on individual preferences

Exploring how different factors contributed to worldwide happiness over the past 5 years (2019-2023). In particular, we are interested in the role played by economic prosperity in determining happiness.

This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.

This a simple RFM Analysis Using K Means Clustering On A Publicly Available Brazilian e Commerce Dataset on Kaggle

This project applies K-means and LDA to the Twenty Newsgroups dataset to group similar documents and discover underlying topics. Explore clustering and topic modeling techniques for organizing and understanding text data.

ICASSP 2023: AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D): An accurate automated solution for COVID-19 diagnosis using UNet segmentation, lung extraction, and classification framework

Image analysis with Gaussian Mixture Model (GMM), with Principal Component Analysis (PCA) for dimensionality reduction of images prior to expectation-maximization (EM) algorithm implementation.

This repository contains functions/codes related to different methods of machine learning for classification and clustering in python.

An implementation of k-means clustering that maintains data association.