Dealing with outliers in a dataset is a critical aspect of data preprocessing and analysis, as these anomalies can significantly distort the results and insights derived from the data.
Outliers are data points that deviate markedly from other observations in a dataset. They can arise due to various reasons such as data entry errors, measurement inaccuracies,
or natural variability in the data. Understanding how to identify and handle outliers effectively ensures that the integrity and reliability of data analysis are maintained.
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