Text-to-SQL Conversion by using DeepLearning/Machine Learning: IntegratingNatural Language with Database Queries.

Accessing and extracting insights from databases can be challenging for non-technical usersunfamiliar with Structured Query Language (SQL). This project presents a novel solution that converts plainlanguage into SQL queries using advanced deep learning (DL) and machine learning (ML)techniques. The system enables users, including academics, business professionals, and students, tointeract with databases without needing to understand complex SQL syntax. Through a userfriendly interface, users input queries in natural language and receive accurate, real-time databaseresponses. Transformer architectures (BERT, GPT, and T5) are employed to understand languagestructure, context, and syntax, generating accurate SQL queries for complex operations involvingjoins, nested statements, and conditions. This approach democratizes data access, reduces relianceon technical staff, increases productivity, and lowers training costs. The technology fosters a datadriven culture by enabling direct interaction with databases, benefiting industries such as banking,healthcare, education, and customer service. Despite challenges in maintaining high accuracy,handling ambiguous queries, and managing diverse database schemas, extensive training on realworld datasets and robust error management are essential. The project's potential to revolutionizedata access and utilization underscores its significance in promoting informed decision-making andinnovation across various sectors