Generative AI-Driven Data Transformation for Enhanced Machine Learning Performance (Student Abstract)

Abstract

Machine Learning (ML) models have significant potential across research and industry to enable data-driven insights and decision-making. Their performance relies on input data quality, but real-world datasets often contain imperfections, making data preprocessing essential yet time-consuming. Our research proposes a proof-of-concept model using Generative Artificial Intelligence (GenAI) to analyze and transform data for supervised ML classification. The results from the GenAI models will be compared with traditionally preprocessed data to evaluate effectiveness. Preliminary results indicate that incorporating GenAI models into the preprocessing pipeline show potential in improving ML's classification performance.