Data Privacy

Data preprocessing in data science involves cleaning and transforming raw data to ensure accuracy, consistency, and usability. This process includes handling missing values, removing duplicates, normalizing data, and encoding categorical variables. Proper preprocessing enhances model performance, reduces errors, and ensures reliable insights, forming a critical step before applying machine learning algorithms or analytical techniques.