In the field of data science, dimensionality reduction is not about deleting columns but about transforming the way information is represented to optimize models and speed up analysis
Among the fundamental techniques, PCA or principal component analysis stands out for simplifying linear relationships, t-SNE for discovering non-linear patterns, UMAP which preserves the global structure of the data, and autoencoders based on neural networks that learn more efficient encodings
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