Automated Data-Quality Assessment and Intelligent Feature Engineering for Large-Scale Predictive Analytics

Abstract

Reliable predictive analytics depends not only on the capacity of learning algorithms but also on the quality, semantic consistency, and predictive usefulness of input data. This paper develops a conceptual, modular framework that combines automated data-quality assessment with intelligent feature engineering for large-scale tabular analytics. The proposed pipeline profiles completeness, validity, uniqueness, consistency, timeliness, and distributional stability; applies risk-aware remediation; constructs statistically and semantically meaningful features; and uses leakage-safe validation to select transformations. A multi-objective optimization formulation balances predictive utility, computational cost, feature stability, and quality risk. An implementation protocol for distributed processing, experiment tracking, and monitoring is specified. To demonstrate reporting and interpretation, a transparent illustrative numerical scenario compares four pipeline configurations across accuracy, F1 score, AUROC, and processing time. These figures are hypothetical examples, not empirical findings. The framework contributes an auditable design that links data-quality decisions to downstream model reliability and provides testable hypotheses for future benchmarking on real datasets.