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What is Dimensionality Reduction Techniques Explained about?

Dimensionality reduction is a technique in data analysis and machine learning that reduces the number of features in a dataset while preserving relevant information, aiding in model simplification, noise reduction, and data visualization. Key methods include Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and Factor Analysis (FA), each serving distinct purposes such as improving model performance and identifying latent factors. The document outlines the steps and applications of thes

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vyshnavireddy598

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EN