Managing data complexity involves several strategies:
Data Preprocessing: Cleaning and normalizing data to reduce noise and inconsistencies. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce the number of variables. Machine Learning: Using algorithms to model complex relationships and predict outcomes. Experimental Design: Planning experiments to efficiently explore the multivariate space.
