DATA QUALITY CHALLENGES AND PREPROCESSING TECHNIQUES FOR ROBUST MACHINE LEARNING PIPELINES

Jessica Kirstin F Journal

DATA QUALITY CHALLENGES AND PREPROCESSING TECHNIQUES FOR ROBUST MACHINE LEARNING PIPELINES

Authors

Jessica Kirstin F Journal

Machine Learning Data Engineer, United Kingdom.

Keywords:

Data Quality,, Machine Learning Pipelines, Data Preprocessing, Missing Data, Data Imbalance, Noise Reduction, Data Cleaning, Data Integration

Synopsis

The exponential growth of data has intensified the importance of high-quality data preprocessing to ensure robust machine learning (ML) pipelines. Despite advanced model architectures, the integrity of input data remains a critical determinant of model performance and generalization. This paper reviews the prevailing data quality challenges, outlines state-of-the-art preprocessing techniques, and evaluates their roles in contemporary ML workflows. Emphasis is placed on data completeness, consistency, accuracy, and handling noisy, imbalanced, and heterogeneous datasets. The study also contextualizes these challenges with reference to recent advances in ML tooling and automation. Literature is reviewed to establish a baseline understanding, followed by current perspectives that align with industrial-scale AI systems. diagrams and tables highlight the comparative effectiveness of preprocessing approaches and common quality pitfalls across domains.

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