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  Dimensionality reduction techniques are used to reduce the number of input features or variables in a dataset while retaining the most important information. These methods help address the curse of dimensionality, improve computational efficiency, and mitigate the risk of overfitting. The choice of dimensionality reduction method depends on the specific characteristics of the data, the desired level of interpretability, the presence of linearity or nonlinearity in the relationships, and the specific goals of the analysis. Careful evaluation and experimentation should be conducted to select the most appropriate method for a given task. Here are some common dimensionality reduction methods: Principal Component Analysis (PCA): PCA is a widely used linear dimensionality reduction technique. It identifies the directions (principal components) in the feature space that capture the maximum variance in the data. By projecting the data onto a lower-dimensional subspace defined by the principa