BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -Dimensionality reduction is the process of reducing the number of variables in high-dimensional data while keeping as much of the variability in the original data as possible.

Detailed explanation-2: -Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.

Detailed explanation-3: -The process of dimensionality reduction is divided into two components, feature selection and feature extraction. In feature selection, smaller subsets of features are chosen from a set of many dimensional data to represent the model by filtering, wrapping or embedding.

Detailed explanation-4: -Principal Component Analysis (PCA) Principal Component Analysis is one of the leading linear techniques of dimensionality reduction. This method performs a direct mapping of the data to a lesser dimensional space in a way that maximizes the variance of the data in the low-dimensional representation.