k-means-clustering

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

The goal of this project is to identify students at risk of dropping out the school

Hueclid turns any screenshot or photo into an accessible UI color palette. It creates colors for backgrounds, surfaces, text, buttons, and more, making sure they all work together with readable contrast.

EDA, data processing, cleaning and extensive geospatial analysis on a selenium based web crawled dataset

This repository gives you access to the CLIMATEREADY survey dataset containing thermal comfort votes during the 2021 and 2022 heatwave periods in Pamplona, Spain, as well as other relevant parameters self-reported by surveyees (e.g. occupant characteristics and behaviour, key building/dwelling characteristics, sleep problems, heat-related symptoms)

Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.

python k-means clustering jupyter notebook

Assignments on ML using Python.........

Hierarchical and K-Means Clustering in R and application to California housing data

Identifying customer segments based on their purchasing behavior using RFM analysis and K-Means clustering.

K-Means, Geo2Vec and Sequence Analysis on IP-Probe mobile data to cluster the flows of tourists in the Tuscany Region.

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

A library of implementations in the 'iads' directory, plus Jupyter notebooks for testing

⚡ Epic Game Store (EGS) Ecosystem Intelligence: A strategic Data Science & UXR audit of the Epic Games Store. Uses K-Means Clustering, NLP, and Predictive Modeling to identify UX friction points, hidden market opportunities, and the "Holiday Quality Trap."

Master thesis research project prepared for the MSc in Management at Barcelona School of Management.

This repository contains data, scripts, and figures for the manuscript entitled 'A fast spectral recovery does not necessarily indicate post-fire forest recovery' which compares spectral recovery, topographic and climatic data, and field measurements of post-fire vegetation dynamics in the Blue Mountains, OR. For more information, see the README

Data Science : clustering, preprocessing

A data-driven intelligence tool that identifies and tracks the momentum of emerging technologies by correlating Google Search Trends with GitHub open-source activity. The project leverages time-series forecasting and clustering algorithms to classify technologies into lifecycle stages providing actionable insights for R&D investment.

This project carried out in R applies PCA for dimensionality reduction and K-Means for clustering on the IRIS dataset. It includes EDA, PCA variance analysis, and cluster evaluation using ggplot2 and factoextra. Additionally, it visualizes the impact of reducing dimensions on clustering.