{"id":1514,"date":"2025-10-01T02:24:47","date_gmt":"2025-09-30T19:24:47","guid":{"rendered":"https:\/\/people.usd.ac.id\/~ydkristanto\/?page_id=1514"},"modified":"2025-10-02T14:45:32","modified_gmt":"2025-10-02T07:45:32","slug":"explore-pca","status":"publish","type":"page","link":"https:\/\/people.usd.ac.id\/~ydkristanto\/index.php\/learning-resources\/explore-pca\/","title":{"rendered":"Exploring Principal Component Analysis (PCA)"},"content":{"rendered":"\n
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Principal Component Analysis (PCA) is one of the most powerful techniques for simplifying data while preserving as much information as possible. But what does that actually mean? Basically, PCA looks for new axes (principal components) that capture the most variation in a dataset.<\/p>\n\n\n\n

This interactive media lets you explore PCA step by step. On the left, you can manipulate original data points and see how they project onto the original x<\/em> and y<\/em> axes. On the right, you can observe how the same data is represented in the PCA space (PC1 and PC2). By dragging the points and comparing both views, you will develop an intuition for how PCA transforms data and why it is such a useful tool in data science.<\/p>\n\n\n\n

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PCA Explorer<\/h3>\n\n\n\n

Let’s explore how data transforms from original axes to principal components!<\/p>\n\n\n\n

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Move the points to see how data projects onto the original x<\/em> and y<\/em> axes.<\/p>\n<\/div>\n\n\n\n

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