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Data preparation, which is also known as data processing, well, data pre -processing, or data cleaning, is a crucial step in the data mining process.
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It involves transforming raw data into a format suitable for analysis and modeling.
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Proper data preparation ensures that the data used for analysis is accurate, complete, and suitable for the chosen data mining technique.
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Here are some key steps involved in data preparation.
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Data collection.
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Gather raw data from various sources, including databases, spreadsheets, text files, and other data repositories.
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Data cleaning.
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Identify and handle missing values, handle outliers, resolving consistencies.
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Data integration.
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Combine data from multiple sources into a unifying data set, ensuring that variables are aligned and consistent.
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Data transformation.
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Normalize numerical features, encode categorical variables, create derived features, handle temporal data...