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In any data analysis workflow, cleaning and preparing the data is often one of the most crucial steps. This process involves handling missing values, correcting data types, dealing with duplicates, and potentially removing outliers.
In any dataset, especially large ones, missing data is a common occurrence and can occur due to various reasons: errors during data collection, changes in the data source, or even by design (e.g., survey non-response). The way you handle missing data can significantly affect your subsequent analysis and results.
