pca: A Python Package for Principal Component Analysis.
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Updated
Dec 17, 2024 - Jupyter Notebook
pca: A Python Package for Principal Component Analysis.
A fast and scalable phylogenetic tree viewer for microbiome data analysis
manage ordinations and render biplots in a tidyverse workflow
A ggplot2 based biplot for principal components-like methods
Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree
This repository contains materials associated to the course "Multivariate Analysis" taught at the Faculty of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Interuniversity Program under the instructors "Ferran Revertar", "Miguel Salicru" and "Jan Graffelman"
Text Mining and Analysis with Biplots.
Worked with fellow peers at University of Pittsburgh under supervision of Dr. Junshu Bao, Department of Statistics, University of Pittsburgh, to do a Differential Gene Expression Analysis on a Dexamethasone treatment data set and incorporated Machine Learning into project.
Interactive data visualizations for Kaggle Brooklyn Home Sales data, built using D3.js
VTubers as influencers might sound naive back then, but nowadays their presence is all around us. So, what are they?
Package for conducting PCA incl. scree-plot and bi-plot
Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments
Biplots, Volcano plots, PCA plots, Heatmaps and more Computational Genomics data created and visualized during University of Pittsburgh course, Computational Biology (BIOSC1540), with Dr. Miler Lee
Apply unsupervised machine learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data
The main goal of this project is to use various Clustering Methods for Bank Customer Segmentation.
Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor
R package, creation of customizable biplots, both in two and three dimensions. Tested in Windows 7 and Mac OS X 10.9.5.
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