What is data preprocessing and why is it important?

Data preprocessing transforms raw data into a clean, structured format suitable for machine learning algorithms, directly impacting model performance.\n\n• Cleaning: Remove duplicates, handle missing values, fix errors\n• Transformation: Scaling, encoding categorical variables, normalization\n• Feature selection: Choose relevant variables, remove noise\n• Quality impact: "Garbage in, garbage out" principle\n\nExample: Customer data preprocessing includes removing duplicate records, filling missing ages with median values, converting categorical variables like "gender" to numerical codes, and scaling income values to prevent bias toward larger numbers.