--- title: Principal Component Analysis (PCA) in Machine Learning description: Principal Component Analysis (PCA) is a dimensionality reduction technique that simplifies complex datasets by transforming correlated features into... canonical: https://daily.dev/posts/principal-component-analysis-pca-in-machine-learning-xb1oeiyld twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Principal Component Analysis (PCA) in Machine Learning | daily.dev og:description: Principal Component Analysis (PCA) is a dimensionality reduction technique that simplifies complex datasets by transforming correlated features into... og:url: https://daily.dev/posts/principal-component-analysis-pca-in-machine-learning-xb1oeiyld og:image: https://api.daily.dev/og/posts/xB1oEIYLd.png og:image:alt: Principal Component Analysis (PCA) in Machine Learning og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Principal Component Analysis (PCA) in Machine Learning **[DigitalOcean Community](https://daily.dev/sources/do_community)** · 12 min read · 0 upvotes · 0 comments ## Summary Principal Component Analysis (PCA) is a dimensionality reduction technique that simplifies complex datasets by transforming correlated features into uncorrelated principal components while preserving maximum variance. The guide walks through PCA implementation in Python, covering standardization, covariance matrix computation, eigenvalue decomposition, and component selection. It demonstrates both manual implementation using NumPy and the scikit-learn library approach, includes practical examples with student marks data, explains loadings and variance ratios, and compares PCA with other techniques like t-SNE. The article addresses common limitations including sensitivity to outliers, loss of interpretability, and the assumption of linear relationships. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [PCA in R: Principal Component Analysis Step-by-Step \(prcomp \+ ggplot2\)](https://daily.dev/posts/pca-in-r-principal-component-analysis-step-by-step-prcomp-ggplot2--3fjct60wn) · R-bloggers · 0 upvotes · 0 comments - [PCA: an embedding shrink-ray](https://daily.dev/posts/pca-an-embedding-shrink-ray-ayowx3qqb) · Software Doug · 0 upvotes · 0 comments - [Numpy Implementation of PCA for Recognizing MNIST \(handwritten digits\) dataset](https://daily.dev/posts/numpy-implementation-of-pca-for-recognizing-mnist-handwritten-digits-dataset-fclpooc2o) · Medium · 0 upvotes · 0 comments - [Linear Discriminant Analysis \(LDA\) in Real-Life: Dimensionality Reduction in a Real-Estate Dataset](https://daily.dev/posts/linear-discriminant-analysis-lda-in-real-life-dimensionality-reduction-in-a-real-estate-dataset-mrjbr3clb) · Towards Data Science · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#data-science](https://daily.dev/tags/data-science), [#scikit](https://daily.dev/tags/scikit) [View this post on daily.dev](https://daily.dev/posts/principal-component-analysis-pca-in-machine-learning-xb1oeiyld) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Principal Component Analysis (PCA) in Machine Learning","url":"https://daily.dev/posts/principal-component-analysis-pca-in-machine-learning-xb1oeiyld","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/principal-component-analysis-pca-in-machine-learning-xb1oeiyld"},"datePublished":"2025-10-10T12:40:47.108Z","dateModified":"2025-10-10T12:41:08.670Z","description":"Principal Component Analysis (PCA) is a dimensionality reduction technique that simplifies complex datasets by transforming correlated features into...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/c9a93c79325f43d9eb2eb113ce25e396?_a=AQAEulh","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/c9a93c79325f43d9eb2eb113ce25e396?_a=AQAEulh","isAccessibleForFree":true,"articleSection":"DigitalOcean Community","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"DigitalOcean Community","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/c1b9d07730e34ea388c39a498a753d6c","url":"https://daily.dev/sources/do_community"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/principal-component-analysis-pca-in-machine-learning-xb1oeiyld","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,python,data-science,scikit","timeRequired":"PT12M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"DigitalOcean Community","item":"https://daily.dev/sources/do_community"},{"@type":"ListItem","position":3,"name":"Principal Component Analysis (PCA) in Machine Learning"}]} ```