In data science, what is the primary goal of dimensionality reduction techniques like Principal Component Analysis (PCA)?

The correct answer is: A. To reduce the number of features while preserving important information.

Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables.

PCA is often used to reduce the dimensionality of data sets, i.e., to reduce the number of features while preserving important information. This can be useful for visualization, data analysis, and machine learning.

Option B is incorrect because PCA does not add more features to the dataset. It only reduces the number of features.

Option C is incorrect because PCA does not normalize data. It only reduces the number of features.

Option D is incorrect because PCA can be used for visualization, but it is not the primary goal of PCA.