DataAssist: A Machine Learning Approach to Data Cleaning and Preparation

TL;DR

DataAssist is an automated platform that uses machine learning to streamline data cleaning and preparation, significantly reducing time spent on these tasks before modeling.

Contribution

It introduces a comprehensive, ML-informed data cleaning and analysis pipeline that integrates visualization, annotation, anomaly detection, and preprocessing.

Findings

Reduces data cleaning time by over 50%.

Provides an integrated pipeline for exploratory analysis and cleaning.

Applicable across various fields like economics and forecasting.

Abstract

Current automated machine learning (ML) tools are model-centric, focusing on model selection and parameter optimization. However, the majority of the time in data analysis is devoted to data cleaning and wrangling, for which limited tools are available. Here we present DataAssist, an automated data preparation and cleaning platform that enhances dataset quality using ML-informed methods. We show that 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.