explained-variance
There are 10 repositories under explained-variance topic.
erdogant/pca
pca: A Python Package for Principal Component Analysis.
anishdulal/principal-component-analysis-PCA
We perform PCA for both visualization and feature selection here.
jonperk318/machine-learning-analysis-of-hyperspectral-data
Using Non-negative Matrix Factorization (NMF) and Variational Autoencoder (VAE) machine learning architectures to analyze spatial and spectral features of hyperspectral cathodoluminescence (CL) spectroscopy images taken from hybrid inorganic-organic perovskite material
gouravaich/faces-recognition-pca
Faces recognition example using eigenfaces and SVMs
HarikrishnanK9/Health_Profile_Analysis
Health Profile Analysis:Revealing Disorder Paterns,Medication Guidance and Risk Classification-ML Project
celiacailloux/Machine-Learning-and-Data-Mining-Examples
Scripts from ML course 02459 from Technical University of Denmark. Scripts have been modified for custom use (e.g. automation of various things, use of pandas rather than numpy arrays and such).
JLeigh101/CryptoClustering
NU Bootcamp Module 19
hung2jj/Principal_component_analysis
In this repository you find a python program and the prints and 3D-visualization of it. After the KNN-Classification I wanted to know which variables have the most relevance for the results. One approach for this is the Principal-Component-Analysis (PCA). More details in the python program as comments.
srinathsai/Dimensionality-Reduction
This project highlights the importance of dimensionality reduction by exploring 2 Machine learning techniques called "Principal Component Analysis" and "T-SNE".