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Dimensionality Reduction

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Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency

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# Dimensionality Reduction

## Overview

Dimensionality reduction techniques reduce the number of features while preserving important information, improving model efficiency and enabling visualization of high-dimensional data.

## When to Use

- High-dimensional datasets with many features
- Visualizing complex datasets in 2D or 3D
- Reducing computational complexity and training time
- Removing redundant or highly correlated features
- Preventing overfitting in machine learning models
- Preprocessing data before clustering or classification
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