During the data mining cycle, which process focuses on removing missing or inaccurate information from the dataset?

During the data mining cycle, which process focuses on removing missing or inaccurate information from the dataset?

• A. Machine learning
• B. Parsing
• C. Cleaning
• D. Predictive modeling

Answer: (C)

Cleaning data, or data cleansing, is the step that ensures data quality by identifying and correcting or removing missing, inaccurate, or inconsistent information in a dataset. This is essential before any analysis or modeling because models rely on accurate data; missing values can lead to biased results, and errors or duplicates can distort findings. This process includes handling missing values, correcting typos, standardizing formats, and resolving duplicates, so the dataset is reliable for the subsequent steps. Machine learning involves selecting algorithms and training models, not fixing data quality. Parsing focuses on breaking down and structuring data from raw text or formats. Predictive modeling uses prepared data to build models that forecast outcomes; it doesn’t inherently fix data quality.

Cleaning data, or data cleansing, is the step that ensures data quality by identifying and correcting or removing missing, inaccurate, or inconsistent information in a dataset. This is essential before any analysis or modeling because models rely on accurate data; missing values can lead to biased results, and errors or duplicates can distort findings. This process includes handling missing values, correcting typos, standardizing formats, and resolving duplicates, so the dataset is reliable for the subsequent steps.

Machine learning involves selecting algorithms and training models, not fixing data quality. Parsing focuses on breaking down and structuring data from raw text or formats. Predictive modeling uses prepared data to build models that forecast outcomes; it doesn’t inherently fix data quality.