High-quality, well-preprocessed data is essential for accurate and meaningful analysis. In catalytic research, the presence of noise, missing values, or irrelevant features can lead to incorrect conclusions. Proper data preprocessing ensures that the dataset is consistent, reliable, and ready for subsequent steps like machine learning or statistical analysis.
