Why Data Preprocessing? Data in the real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data a e.g. occupation= mm a noisy: containing errors or outliers aeg, salary=-10″ inconsistent: containing discrepancies in codes or names e.g. Age=42 Birthday=03/07/1997 e.g Was rating 1,213 now rating A, B,c e.g. discrepancy between duplicate records2 Why Data Preprocessing? ◼ Data in the real world is dirty ◼ incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data ◼ e.g., occupation=“ ” ◼ noisy: containing errors or outliers ◼ e.g., Salary=“-10” ◼ inconsistent: containing discrepancies in codes or names ◼ e.g., Age=“42” Birthday=“03/07/1997” ◼ e.g., Was rating “1,2,3”, now rating “A, B, C” ◼ e.g., discrepancy between duplicate records