{"id":78,"date":"2026-04-03T11:24:21","date_gmt":"2026-04-03T11:24:21","guid":{"rendered":"https:\/\/gigz.pk\/ml\/?post_type=lesson&#038;p=78"},"modified":"2026-04-08T08:51:04","modified_gmt":"2026-04-08T08:51:04","slug":"pca-technique","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/ml\/lesson\/pca-technique\/","title":{"rendered":"PCA Technique"},"content":{"rendered":"\n<p>Principal Component Analysis (PCA) is a <strong>dimensionality reduction technique<\/strong> used in Machine Learning and data analysis. It transforms high-dimensional data into a lower-dimensional form while retaining most of the important information (variance) in the data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why PCA is Used<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Reduces the number of features in a dataset, making models faster and less complex<\/li>\n\n\n\n<li>Helps visualize high-dimensional data<\/li>\n\n\n\n<li>Removes redundant or correlated features<\/li>\n\n\n\n<li>Can improve model performance by reducing noise<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">How PCA Works<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Standardize Data:<\/strong> Scale the features so they have mean 0 and standard deviation 1.<\/li>\n\n\n\n<li><strong>Compute Covariance Matrix:<\/strong> Measure how features vary together.<\/li>\n\n\n\n<li><strong>Compute Eigenvectors and Eigenvalues:<\/strong> Identify directions (principal components) that capture maximum variance in the data.<\/li>\n\n\n\n<li><strong>Sort Components:<\/strong> Rank principal components by the amount of variance they explain.<\/li>\n\n\n\n<li><strong>Transform Data:<\/strong> Project the original data onto the selected principal components to reduce dimensionality.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Key Concepts<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Principal Components (PCs):<\/strong> New uncorrelated features that represent the directions of maximum variance in the data.<\/li>\n\n\n\n<li><strong>Explained Variance:<\/strong> Percentage of total variance captured by each principal component.<\/li>\n\n\n\n<li><strong>Dimensionality Reduction:<\/strong> Using fewer principal components than original features while retaining most of the information.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Advantages of PCA<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Reduces computational cost for high-dimensional datasets<\/li>\n\n\n\n<li>Helps in visualizing and understanding complex data<\/li>\n\n\n\n<li>Can improve model performance by reducing overfitting<\/li>\n\n\n\n<li>Removes multicollinearity among features<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Limitations of PCA<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Transformed features are not easily interpretable<\/li>\n\n\n\n<li>Assumes linear relationships between features<\/li>\n\n\n\n<li>Sensitive to scaling and outliers<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Applications of PCA<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Image compression and recognition<\/li>\n\n\n\n<li>Visualizing high-dimensional data in 2D or 3D<\/li>\n\n\n\n<li>Preprocessing step for Machine Learning models<\/li>\n\n\n\n<li>Finance for portfolio optimization and risk analysis<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>PCA is a powerful technique for simplifying complex datasets by reducing dimensionality while preserving most of the data\u2019s variance. It is widely used in data preprocessing, visualization, and improving Machine Learning model efficiency.<\/p>\n\n\n\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-1775638209618\"><strong class=\"schema-faq-question\"><\/strong> <p class=\"schema-faq-answer\"><\/p> <\/div> <\/div>\n\n\n<div class=\"yoast-breadcrumbs\"><span><span><a href=\"https:\/\/gigz.pk\/ml\/\">Home<\/a><\/span> \u00bb <span class=\"breadcrumb_last\" aria-current=\"page\">Intermediate Machine Learning >Unsupervised Learning > PCA Technique<\/span><\/span><\/div>","protected":false},"menu_order":35,"template":"","class_list":["post-78","lesson","type-lesson","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>PCA Technique - Machine Learning Mastery<\/title>\n<meta name=\"description\" content=\"Learn how Principal Component Analysis (PCA) reduces data dimensions, retains key variance, removes redundancy, and improves ML models.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/gigz.pk\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PCA Technique - 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