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Data cleaning is a critical step in the data science and big data workflow, aimed at ensuring that the datasets used for analysis and modeling are accurate, consistent, and reliable. In real-world scenarios, raw data often comes with inconsistencies, errors, duplicates, or missing values due to human error, system glitches, or integration of multiple data sources. The data cleaning process typically involves several stages, including detection of anomalies, handling missing values, correcting inconsistencies, and validating the integrity of data. The goal is to transform raw, noisy data into a structured format that can improve the performance of predictive models and support accurate decision-making. The initial step in data cleaning is data profiling, which involves analyzing datasets to identify the types of variables, ranges, distributions, and the _______ ____ _______ _____ _______ _____ _______.
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