Data preprocessing is critical in a ML workflow that involves cleaning (i.e., filtrating), transforming, and organizing raw data. Cleaning is identifying and rectifying errors, inaccuracies, inconsistencies, and anomalies in the collected data, which involves filtrating incorrect values, detecting outliers, removing duplicate entries, and converting data types. Normalization, an example of data transformation, scales data into a specific range, such as 0 to 1. Normalization ensures that each feature of the data contributes equally to model training, preventing any single attribute from disproportionately influencing the results. Organizing raw data entails structuring and optimizes the data for the specific ML algorithms to be applied.