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What is Understanding Missing Values in Data Science about?

Missing values in datasets can arise from human error, system errors, data merging, non-response, and loss during processing, impacting analysis and decision-making. There are three types of missing data: MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random), each requiring different handling strategies. Effective management of missing values is crucial for maintaining the reliability and accuracy of data analyses.

Author

deshmukhbhumika04

Language

EN