Data Imputation Methods - Handling Missing Values: Reviewing data imputation methods for handling missing values in datasets to prevent bias and improve predictive performance
Keywords:
Data Imputation, Missing ValuesAbstract
This research paper provides a comprehensive review of data imputation methods for handling missing values in datasets. Missing data is a common issue in various fields, including healthcare, finance, and social sciences, which can lead to biased results and reduced predictive performance if not handled properly. The paper examines the importance of addressing missing data, discusses the types and causes of missingness, and reviews popular imputation methods. These methods include traditional approaches such as mean imputation, median imputation, and regression imputation, as well as more advanced techniques such as k-nearest neighbors (KNN) imputation, multiple imputation, and matrix factorization-based imputation. The paper also discusses the advantages and limitations of each method and provides guidelines for selecting the most appropriate imputation method based on the characteristics of the dataset and the research objectives. Finally, the paper concludes with a discussion of future research directions in data imputation methods.
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