The analysis of large datasets in catalysis involves several steps. Initially, data preprocessing is performed to clean and organize the data. Following this, various statistical and machine learning techniques are applied to identify patterns and correlations. Techniques such as principal component analysis (PCA), regression analysis, and clustering are commonly used. Machine learning algorithms, including neural networks and random forests, can also be employed to develop predictive models and gain deeper insights.
