- Data Simplification: PCA reduces the complexity of large datasets by focusing on the most important variables, making it easier to interpret and analyze the data. - Noise Reduction: By concentrating on the principal components, PCA helps in filtering out noise and irrelevant variations, leading to more accurate results. - Visualization: PCA enables the visualization of high-dimensional data in two or three dimensions, facilitating a better understanding of the underlying patterns and relationships. - Improved Predictive Models: By identifying the key factors influencing catalytic performance, PCA enhances the development of predictive models that can guide the design of more efficient catalysts.
