AI Meets Data Science: Preprocessing and Feature Selection Reimagined

The rapid evolution of artificial intelligence (AI) has paved the way for substantial improvements in data science workflows, particularly in data preprocessing and feature selection. These two fundamental steps, which have traditionally been time-consuming and manual, significantly impact the accuracy and efficiency of machine learning models. This paper explores the potential of AI-driven optimization in automating data preprocessing and feature selection, with the goal of enhancing the quality of data and model performance. We delve into the utilization of machine learning algorithms, particularly deep learning and reinforcement learning, to refine these processes and reduce the reliance on manual intervention. The research focuses on three key aspects: automated data cleaning and transformation, AI-based feature selection, and the integration of AI techniques into existing data science tools. The findings contribute to the development of more efficient and accurate methods for handling large and complex datasets, addressing challenges in fields such as finance, medical research, and IoT data analysis.