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Data collection is the initial step in the data preprocessing phase, involving the gathering of raw data from various sources such as databases, spreadsheets, APIs, or web scraping. This raw data often contains noise, inconsistencies, and errors that need to be addressed to ensure its usability. Data cleaning involves identifying and correcting (or removing) errors and inaccuracies in the data. This may include handling outliers, correcting typos, standardizing formats, and ensuring data consistency across different sources. The goal is to produce a dataset that is as accurate and complete as possible, providing a solid foundation for subsequent analysis.
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