This repository contains code used to create Paper "Navigating Complexity: Evaluating the Effectiveness of Dimension Reduction and Clustering Approaches on Challenging Datasets" as a Bachelor's thesis for EUR.

Collection of Code used in Articles for Coffee and Data Science

The goal of this R Function is to compress arbitrary images using numerical principal component analysis techniques to obtain the most visually appealing compressed image.

Public data for the Embedding Projector

Multivariate Statistics Project

Principal Component Analysis of surfaces, i.e. functions defined on a bivariate domain.

DESeq2Shiny2 is an R shiny application to handle NGS count data and perform unsupervised clustering and Differential Gene Expression Analysis using DESeq2.

LearnR tutorial about PCA and k-Means Clustering https://inayatus.shinyapps.io/AlgLearnRUL/

Archived repo - This R Package is not developed anymore (only maintenance). It was replaced by R package rchemo

The data at hand is of flight satisfaction survey along with the customer flight information, the task at hand is to build a model that predicts satisfaction/dissatisfaction given the various attributes

Dimension Reduction

Principal component analysis using R

Comparing the use of original data vs PCA in multiple regression. Analysis also showcases how principal components can be transformed back to their original vector space after model fitting for descriptive purposes

This work involves two subtasks: assessing clustering results using all input variables and applying PCA for dimensionality reduction to improve understanding of multi-dimensional problems.

The HotellingEllipse package helps draw the Hotelling's T-squared ellipse on a PCA or PLS score scatterplot by computing the Hotelling's T-squared statistic and providing the ellipse's x-y coordinates, semi-minor, and semi-major axes lengths.

This repository contains the R scripts and data for an automated PCA of the gravier dataset

Analysis of school approval and dropout rates stratified by geolocation and school category (private, public, state, federal, etc.)