Classification of the states of Mexico according to their level of electoral complexity.

In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM), Random Forest (RF) Support Vector Machine (SVM) with or without M…

Machine Learning in R

How does PCA work?

Fiverr Project

Using R, R Markdown

R-Programming using R studio as per college curriculum .

Principle Component Analysis

Performed Principal Component Analysis, Cluster models and Exploratory Factor Analysis on FIFA dataset to identify the clusters among players, latent variables of management, and coach perspective on this Multivariate DataSet.

Principal component analysis using R

This project includes framework for creating multivariate process monitoring control charts, identifying out-of-control points and removing the out-of-control data points (All the iterations are identified automatically by a loop and removed). The project also gives reader an idea of the approach followed for dimension reduction using PCA.

Symbolic principal component analysis of interval-valued data

computational multivariate statistics course, scripts for data analysis and classification

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.

Data Understanding using- PCA, LDA, tSNE, and UMAP.

A Collection of Data-Sets, R-programs, and Vignettes for the Advanced Workshop in Sensory Evaluation of SPISE 2022.

Elementary algorithm for face recognition using PCA

online data science competition