SMART-IMPUTE: A Time-Efficient, ANN-Based Algorithm for Practical Imputation with Empirical and Theoretical Validation

This paper presents a solution to one of the most persistent bottlenecks in data science: the slow and expensive process of handling missing data. Data teams are constantly forced into an undesirable trade-off, choosing between simple methods that are fast but statistically naive, and robust methods (like MICE or KNN) that are accurate but computationally prohibitive, bringing iterative workflows to a halt. This research introduces and validates Smart-Impute, a novel \"High-Performance Robust Imputer\" architected to resolve this dilemma. It is designed for practitioners who need both state-of-the-art accuracy and high-speed performance. The key findings presented are: Massive Performance Gains: We provide empirical proof from benchmarks on real-world datasets, demonstrating that Smart-Impute is up to 12.2x faster than the standard KNN imputer, turning hour-long processes into minutes. Superior Scalability: We deliver a formal mathematical proof of Smart-Impute's superior O(N log N) time complexity, ensuring its performance scales gracefully as datasets grow. State-of-the-Art Robustness: We demonstrate how the algorithm's architecture natively handles the mixed data types and high-cardinality features that are common in real-world enterprise data. The result is a practical, workhorse algorithm that saves valuable time, increases team productivity, and enables more agile data analysis without sacrificing statistical integrity. This repository contains the full research paper detailing the algorithm, its theoretical proofs, and its empirical validation.