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Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. It involves cleaning, transforming, and organizing data to make it suitable for analysis. Data preprocessing is a crucial step in the data science pipeline as it helps to improve the quality of data, reduce errors, and increase the accuracy of machine learning models. The process involves several steps such as data cleaning, data transformation, data integration, data reduction, and data discretization. Data cleaning involves removing or correcting errors, missing values, and inconsistencies in the data. Data transformation involves converting data into a suitable format for analysis. Data integration involves combining data from multiple sources. Data reduction involves reducing the size of the data while preserving its integrity. Data discretization involves converting continuous data into discrete categories.
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