Correct Option: C. Principal Component Analysis (PCA)

Explanation:
Principal Component Analysis (PCA) is specifically designed for dimensionality reduction. It works by transforming the original variables into a new set of variables, the principal components, which are orthogonal (uncorrelated) and capture the maximum variance in the data. This makes PCA the most suitable option for the given scenario because:

• It directly addresses the need to reduce dimensionality while preserving variability.
• It helps in identifying patterns in data based on the correlation between features.
• It facilitates data visualization by reducing the data to two or three principal components.

Why other options are incorrect:

• A. Gradient Descent: While useful for optimizing model parameters, it does not inherently reduce the number of features in the dataset.
• B. Random Forest: Although it can indicate feature importance, it does not reduce the dimensionality of the dataset in a way that preserves the original variability or facilitates visualization.
• D. K-Nearest Neighbors (KNN): This algorithm does not reduce dimensionality; it uses all features to compute distances between data points.