In many real-world datasets, the number of features or variables can be very high, which can make it difficult to analyse and interpret the data.
Dimensionality reduction techniques are used to transform the high-dimensional data into a lower-dimensional space.
The reduced dimensional space can make it easier to visualize and analyse the data.
Dimensionality reduction techniques can be used for data compression, feature extraction, and noise reduction.
The two main approaches to dimensionality reduction are feature selection and feature extraction.
Feature selection involves selecting a subset of the original features that are most relevant to the task at hand.
Feature extraction involves transforming the original features into a new set of features
Principal Component Analysis (PCA) is a commonly used feature extraction technique
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