Which term describes the activity of removing duplicates and handling missing values to prepare data for modeling?

Which term describes the activity of removing duplicates and handling missing values to prepare data for modeling?

• A. Preprocessing
• B. Data cleaning
• C. Data
• D. Tool creation

Answer: (A)

Preparing data for modeling involves turning raw data into a clean, consistent format that algorithms can work with. Removing duplicates and handling missing values are classic steps in preprocessing because they modify the data to reduce noise and ensure the modeling algorithm can learn effectively. Preprocessing is the umbrella term that includes cleaning, normalization, encoding, imputation, scaling, and sometimes feature engineering. Data cleaning, while related, describes cleaning up messy data more narrowly—fixing errors, removing duplicates, and correcting inconsistencies—so it sits under preprocessing rather than replacing it. Data refers to the information itself, not the transformation process, and tool creation is not about preparing data for modeling. Hence, the best fit is preprocessing.

Preparing data for modeling involves turning raw data into a clean, consistent format that algorithms can work with. Removing duplicates and handling missing values are classic steps in preprocessing because they modify the data to reduce noise and ensure the modeling algorithm can learn effectively. Preprocessing is the umbrella term that includes cleaning, normalization, encoding, imputation, scaling, and sometimes feature engineering. Data cleaning, while related, describes cleaning up messy data more narrowly—fixing errors, removing duplicates, and correcting inconsistencies—so it sits under preprocessing rather than replacing it. Data refers to the information itself, not the transformation process, and tool creation is not about preparing data for modeling. Hence, the best fit is preprocessing.