Addressing the challenges of handling missing data in Data science applications

Abstract

Missing data is a common issue in data science applications that can significantly impact the accuracy and reliability of data analysis and modeling. This paper explores the challenges associated with missing data and presents various approaches and techniques to effectively handle missing data in data science applications. We discuss both traditional statistical methods and advanced machine learning techniques that can be employed to address missing data. Additionally, we highlight the importance of understanding the underlying mechanisms causing missingness and the implications it has on data analysis. Through this paper, we aim to provide a comprehensive overview of the existing methods and methods for dealing with missing data, offering insights into their practical applicability and future research directions in this field.