Benchmarking Robust Machine Learning Models Under Data Imperfections in Real-World Data Science Scenarios

Machine learning systems deployed in real-world environments frequently encounter data imperfections such as noise, missing values, class imbalance, and distribution shifts. Despite substantial progress in model development, most evaluation protocols rely on clean benchmark datasets, creating a gap between laboratory performance and operational reliability. Existing robustness studies often focus on isolated perturbation types or single model families, lacking a unified benchmarking framework. This study proposes a structured and reproducible benchmarking methodology to systematically evaluate model robustness under controlled data degradation scenarios. Multiple classical machine learning algorithms and deep learning models were assessed across diverse benchmark datasets. Controlled perturbations—including feature noise, label corruption, missingness mechanisms, imbalance ratios, and covariate shifts—were introduced at progressive levels. Performance was evaluated using predictive metrics, robustness degradation rate (RDR), and computational efficiency, with statistical validation across repeated experimental runs. Results indicate that ensemble-based methods consistently achieved