Paper Title
EXPLORATORY DATA ANALYSIS WITH GPT: GENERATING DATABASE QUERIES USING NATURAL LANGUAGE FOR ENHANCED BUSINESS DECISION-MAKING

Abstract
Abstract - The rapid growth of data in businesses has accentuated the need for effective and accessible methods of data analysis. This research paper investigates the potential of using Generative Pre-trained Transformer (GPT) models in enabling exploratory data analysis (EDA) within a business, through the generation of database queries from natural language inputs. By leveraging GPT's ability to understand and process natural language, we aim to bridge the communication gap between business teams and databases, reducing the dependency on data teams and expediting decision-making processes. The study focuses on the architecture, implementation, and evaluation of a GPT-based system that can accurately generate structured database queries in response to natural language inquiries. Through practical application and experimentation, this research highlights the transformative potential of GPT for enhancing EDA accessibility and efficiency within a business context. Keywords - (Natural Language Processing) NLP, Generative Pre-Trained Transformer (GPT), Business Intelligence, Exploratory Data Analysis (EDA), Database Queries