👉 Prior computing refers to the process of preparing and organizing data before it is used in a machine learning or computational model. This involves several key steps: data cleaning, which removes or corrects inaccuracies and inconsistencies; data normalization, where values are scaled to a standard range to ensure that no single feature dominates the model; feature extraction, where relevant attributes or features are identified and extracted from raw data to improve model performance; and feature selection, where the most important features are chosen to reduce dimensionality and enhance model efficiency. Prior computing is crucial because it lays the groundwork for effective model training, ensuring that the data fed into the computational model is accurate, relevant, and optimized for learning.
