📊Analyse de donnéesAdvancedAll AIs

Apply dimensionality reduction

Reduce high-dimensional data

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Write Python code to reduce the dimensionality of a feature matrix X with [number] features using PCA, t-SNE, and UMAP. Determine the optimal number of PCA components using explained variance (95% threshold), visualize t-SNE and UMAP embeddings colored by [label column], and compare clustering quality.

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Reduce high-dimensional data

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