A COMPREHENSIVE FRAMEWORK FOR MODERN DATA CLEANING: INTEGRATING STATISTICAL AND MACHINE LEARNING APPROACHES WITH PERFORMANCE ANALYSIS

Abstract

Data cleansing is an important prerequisite for reliable data evaluation and system-gaining knowledge of programs, directly impacting the exceptional insights and model overall performance. This paper provides a comprehensive examination of modern information-cleaning methodologies, focusing on their sensible packages and effectiveness across diverse datasets. We analyze six primary categories of information cleansing techniques: missing statistics management, outlier detection, information standardization, reproduction removal, consistency validation, and data type transformations. Our systematic assessment exhibits that automated information cleansing pipelines, at the same time efficient, require careful configuration primarily based on area context. Key findings imply that hybrid procedures—combining statistical techniques with area-precise policies—attain advanced consequences compared to standalone strategies, showing a 23% improvement in statistically satisfactory metrics. We also perceive that early-stage records validation significantly reduces downstream processing mistakes by 45%. The implications of this research suggest that companies ought to implement iterative facts-cleaning workflows, incorporating continuous validation and area expert remarks. Furthermore, our findings emphasize the significance of documenting cleansing decisions to ensure reproducibility and keep information lineage. This work offers a structured framework for practitioners to select and implement suitable facts-cleaning techniques based totally on their particular use cases and facts characteristics.