This tutorial is created for educational purpose

R package PCAtest for evaluating the statistical significance of PCA analysis, selecting number of significant PC axes, and testing the contributions of the variables to those PCs.

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

Demographic influences on smoking habits in the UK using Principal Component Analysis

The best aesthetic plotting of princals function of Gifi package

Multidimensional analysis to search for the best voivodeship for families with children

Code to support: "pcaReduce: hierarchical clustering of single cell transcriptional profiles"

first business data analysis - factors affecting student satisfaction and bank clients

This project analyzes energy security in EU countries using multivariate analysis methods like linear ordering, PCA, and cluster analysis. Key variables include renewable energy share, fossil fuel dependency, and energy import reliance.

In this project the Turkey Student Evaluation dataset which in question consists of feedback from students who attended multiple courses at Gazi University, Ankara used.To analyze the questions we used unsupervised learning methods, which involve finding patterns or groupings in a given dataset without any labeled output variable.

App Shiny : Optimised matrix visualization of Non-negative Sparse PCA components.

This is a university project on the analysis of the software production sector using Principal Component Analysis (PCA). The work was carried out in 2020 by Gabriele Pillitteri, Simone Benzi, and Marco Contucci.

PCA analysis of GUS data

Repositório para armazenamento dos códigos utilizados no meu trabalho de conclusão de curso.

Biological network analysis of Mtb

A shiny application to perform differential gene expression analysis of count data using DESeq2. The app also allows unsupervised exploration of data using PCA and hierarchical clustering.

Credit risk analysis: based on the number of family dependents and the duration of the month's loan, classify the credit rating or risk rating using the decision tree method (C50 with R).

Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve Transform

Implémentation des méthodes statistiques notamment de l'analyse factorielle pour identifier la combinaison des caractéristiques qui sont plus susceptibles d'être associées à un accident vasculaire cérébral (AVC).

Advanced Multivariate Statistics project