factoextra

This project clusters white wines based on their chemical properties to understand their relationship with quality ratings, using techniques like k-means and PCA.

Customer Segmentation using R

Clustering tutorial in R for UConn's Social-Ecological-Environmental (SEE) Lab

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.

Tutorial code for performing PCA (with mathematical explanation) on breast cancer features computed from digitized images of fine needle aspirate (FNA) of a breast mass. Center the data, calculate correlation matrix, compute principal components, visualize and interpret results.

R script performing Principal Component Analysis (PCA, prcomp/factoextra) for dimensionality reduction of Mariana Trench bathymetric data, with scree, loadings and biplot. Companion to Lemenkova (2019), Geodesy and Cartography 45(2):57-84.

Project uses K-Means clustering to analyze mall customers based on spending behavior and annual income

Clustering to find customer segment of the Boston based ride sharing program Hubway

Clustering no supervisado en R con USArrests: comparación de k-means, k-means++ y clustering jerárquico (Ward).

Multivariate analysis of the EU automotive and electromobility market (2012-2024). Built with R, it utilizes clustering algorithms (Ward's, k-means), Wrocław taxonomy, and PCA to classify countries based on EV adoption, infrastructure, fleet age, and road safety, revealing a "Three-Speed Europe".

Segmentação de clientes com base em análise RFM

R scripts performing k-means (factoextra fviz_cluster, k=3-7) and Gaussian-mixture (mclust, BIC) cluster analysis of Mariana Trench bathymetric profiles. Lemenkova (2019), K-means Clustering in R {cluster}/{factoextra}, Int. J. Informatics and Applied Mathematics 2(1):1-26.

Análisis de pandemia utilizando base de datos .csv

Analyzing key air quality variables with R through visualizations and statistical methods.

Perform cluster analysis on household electricity consumption data using K-means clustering in R