DataAssist: A Machine Learning Approach to Data Cleaning and Preparation

Automated machine learning (ML) tools have primarily focused on model selection and parameter optimization, leaving a gap in the area of data cleaning and preparation. This paper presents DataAssist, an automated data preparation and cleaning platform that enhances dataset quality using ML-informed methods. DataAssist provides a pipeline for exploratory data analysis and data cleaning, including generating visualization for user-selected variables, unifying data annotation, suggesting anomaly removal, and preprocessing data. The exported dataset can be readily integrated with other autoML tools or user-specified model for downstream analysis. Our data-centric tool is applicable to a variety of fields, including economics, business, and forecasting applications saving over 50% time of the time spent on data cleansing and preparation. This paper aims to fill the gap in the literature by providing a comprehensive tool for data cleaning and preparation, which is often overlooked in the current ML landscape. We also present a comparative analysis of DataAssist with other existing technologies, demonstrating its superior performance and efficiency.