What role does data preprocessing play in machine learning?

Data preprocessing is a critical step in the machine learning pipeline that involves cleaning and transforming raw data into a format suitable for modeling. It includes tasks such as handling missing values, encoding categorical variables, normalizing or standardizing numerical features, and removing duplicates or outliers. Proper preprocessing enhances model performance by improving data quality and ensuring that models learn effectively from accurate, relevant information. Neglecting this step often leads to suboptimal results due to noise and irrelevant features affecting the learning process.