principal component analysis

Principal Component Analysis (PCA)

Towards Data Science

The principal component analysis (PCA) involves rotating a cloud of data points in Euclidean space such that the variance is maximal along the first axis, the so-called first principal component. The…...

Principal Component Analysis- A Brief Understanding

Analytics Vidhya

Principal Component Analysis (PCA) is an unsupervised technique for reducing the dimension of the data. The idea behind PCA is to seek the most accurate data representation in a lower-dimensional…

Principal Component Analysis (PCA)— Part 1 — Fundamentals and Applications

Analytics Vidhya

Principal Component Analysis is among the most popular, fastest and easiest to interpret Dimensionality Reduction Techniques which exploits the Linear Dependence among variables. Some of its…

Understanding Principle Component Analysis

Analytics Vidhya

Principle Component Analysis (PCA) is widely used in machine learning and data science. PCA finds a representation of the model’s data in a lower dimensional space without loosing a large amount of…

In Depth: Principal Component Analysis

Python Data Science Handbook

Up until now, we have been looking in depth at supervised learning estimators: those estimators that predict labels based on labeled training data. Here we begin looking at several unsupervised estima...

The Magic of Principal Component Analysis through Image Compression

Towards Data Science

Principal Component Analysis or PCA is a dimensionality reduction technique for data sets with many continuous (numeric) features or dimensions. It uses linear algebra to determine the most important…...

Like Principal Components Analysis?

Towards AI

Introduction Principal Component Analysis (PCA) is a dimensionality reduction technique that projects the input variables that describe a set of objects into linear combinations of these variables to ...

In-depth Principal Component Analysis

Analytics Vidhya

The Principal Component Analysis or PCA is an unsupervised learning method and is used for dimensionality reduction. In this blog, we will understand it in-depth and learn how to use it. Machine…

Principal Component Analysis

Analytics Vidhya

In this section, I will be detailing the PCA code posted on Kaggle by Niraj Verma…. “Principal Component Analysis” is published by Sai Krishna Devireddy in Analytics Vidhya.

A Step By Step Implementation of Principal Component Analysis

Towards Data Science

Principal Component Analysis or PCA is a commonly used dimensionality reduction method. It works by computing the principal components and performing a change of basis. It retains the data in the…

The Essence of Principal Component Analysis (PCA)

Towards Data Science

PCA is the simplest of the true eigenvector-based multivariate analyses. It is most commonly used as a dimensionality-reduction technique, reducing the dimensionality of large data-sets while still…

The Mathematics Behind Principal Component Analysis

Towards Data Science

The central idea of principal component analysis (PCA) is to reduce the dimensionality of a data set consisting of a large number of interrelated variables while retaining as much as possible of the…