Which technique helps in reducing the dimensionality of the data?

Principal Component Analysis (PCA) is a powerful technique specifically designed to reduce the dimensionality of data while preserving as much variance as possible. It works by transforming the original set of variables into a new set of variables, known as principal components, which are orthogonal to each other. These principal components capture the most significant patterns in the data, allowing for a simplified representation without losing critical information.

By focusing on a smaller number of principal components that explain the greatest amount of variance, PCA enables analysts to streamline their datasets, making them easier to visualize and analyze, especially when dealing with high-dimensional spaces that may lead to issues like overfitting in machine learning models.

While data normalization and feature scaling are important preprocessing steps to ensure that different scales of data do not bias the analysis, they do not inherently reduce the number of dimensions. Similarly, Recursive Feature Elimination focuses on selecting the most important features from the dataset but does not fundamentally transform the dimensionality in the same way PCA does.