From the full set of variables, you might end up with four principal components that explain 90% of the original data. That’s much easier to understand! Later in this post, I include a sample …

Nov 13, 2025 · PCA (Principal Component Analysis) is a dimensionality reduction technique and helps us to reduce the number of features in a dataset while keeping the most important …

Mar 28, 2024 · Lets start off by a numeric example that we will approach its solution slowly, step-by-step. Later on, we will stretch our solution to dive deeper in the theory behind it in exactly …

Jun 23, 2025 · PCA is a widely covered machine learning method on the web. Below we cover how principal component analysis works in a simple step-by-step way, so everyone can …

In this article, I will discuss how to find the principal components with a simple solved numerical example.

Dec 1, 2020 · Principal components analysis, often abbreviated PCA, is an unsupervised machine learning technique that seeks to find principal components – linear combinations of the original …

Jun 8, 2024 · In this article, I show the intuition of the inner workings of the PCA algorithm, covering key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues, then …

Mar 20, 2025 · Learn what Principal Component Analysis (PCA) is, how it works, and explore its uses with simple examples in machine learning.

Apr 8, 2025 · In this article, we’ll explore detailed examples of PCA across multiple domains including image compression, finance, genomics, and customer segmentation. We’ll also walk …

Jul 20, 2022 · Principal Component Analysis reduces dimensions of measurement without losing the data accuracy. This guide explains where PCA is used with a solved example.