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Introduction to Principal Component Analysis


This is the Part 35 of the Lecture. For the complete lecture on PCA click here.


Principal Component Analysis (PCA) is a statistical technique commonly used in data analysis to simplify the complexity of large datasets by transforming the original variables into a smaller set of uncorrelated variables known as principal components. These components capture the maximum amount of variance in the data, thereby allowing for easier interpretation and visualization of the underlying patterns and relationships. PCA is widely used in fields such as machine learning, image processing, and genetics, providing researchers with valuable insights and actionable information for decision-making. By identifying the key dimensions driving variation in the data, PCA enables researchers to better understand and extract meaningful information from complex datasets.


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