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When dealing with missing data, data scientists have two options for resolving the error: imputation or data removal. For missing data, the imputation process generates credible predictions. When the fraction of missing data is low, it is most beneficial.

It is crucial to avoid any bias in machine learning models and provide reliable statistical data analysis. One of the difficulties of data analysis is dealing with missing numbers. Understanding the various types of missing data assists in making decisions about how to handle it.

To deal with missing data, data scientists employ two techniques: average imputation and common-point imputation. Average imputation fills in missing values by using the average of the replies from other data entries.

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