Automated Data Preparation through Deep Learning: A Novel Framework for Intelligent Data Cleansing and Standardization

Abstract

This article presents a comprehensive framework for automating data preparation and cleansing processes using artificial intelligence techniques. The proposed approach combines supervised and unsupervised learning methods with natural language processing to address common data quality challenges, including inconsistencies, missing values, and format standardization. By integrating deep neural networks for pattern recognition, ensemble methods for enhanced accuracy, and knowledge graphs for domain-specific expertise, the framework demonstrates significant improvements in both data quality and processing efficiency compared to traditional manual approaches. The system's architecture incorporates multiple layers of validation and quality assurance mechanisms, ensuring robust and reliable outputs while reducing human intervention in the data preparation pipeline. Experimental results across various datasets and use cases indicate substantial reductions in processing time and improved accuracy in anomaly detection and correction, while maintaining scalability for large-scale implementations. This article contributes to the growing field of automated data science by providing a scalable, intelligent solution that enables data scientists and analysts to focus on higher-value analytical tasks while ensuring consistent and high-quality data preparation.