![]() Many techniques are used to perform each of these tasks, where each technique is specific to a user’s preference or problem set. ![]() Data cleaningĭata cleaning refers to techniques to ‘clean’ data by removing outliers, replacing missing values, smoothing noisy data, and correcting inconsistent data. To make the process easier, data preprocessing is divided into four stages: data cleaning, data integration, data reduction, and data transformation. ![]() To ensure high-quality data, it’s crucial to preprocess it. Some other features that also affect the data quality include timeliness (the data is incomplete until all relevant information is submitted after certain time periods), believability (how much the data is trusted by the user) and interpretability (how easily the data is understood by all stakeholders).
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