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Data cleaning focuses on resolving inconsistencies, filling missing values, removing duplicates, and handling outliers to ensure the dataset is accurate, complete, and reliable. High-quality data is critical for generating meaningful insights and avoiding analytical errors. For example, incorrect or incomplete customer information in a sales dataset could lead to flawed marketing strategies. Techniques such as imputation, deduplication, and outlier treatment ensure the dataset is ready for analysis. Clean data enables better decision-making and enhances the credibility of data-driven insights. Why Other Options Are Incorrect: • A: Data transformation involves reformatting or scaling data, not cleaning it. • C: Cleaning prepares data for visualization but is not specifically aimed at visualization. • D: Standardization may occur during cleaning but is not its sole purpose. • E: While validation is related to accuracy, cleaning focuses more broadly on quality improvement.
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