Rapid Application Development: A Rapid Automation Framework

Machine learning is widely used in many fields such as healthcare, finance, education, and automation. But developing a machine learning system usually takes a lot of time and effort. Tasks like understanding the problem,collecting data, cleaning data, and preparing datasets are done manually and individually; these tasks also requires technical knowledge. Hence, it makes machine learning difficult for non-technical users to develop and use, and it also it slows down the development process. This paper introduces a Rapid Application Development (Software Development) framework that helps simplify and speed up the early stages of machine learning development. The system enables users to specify their requirements in the form of a simple, natural language prompt. A Small Language Model (SLM) is used to extract important keywords from the prompt. These keywords are then passed to collect the relevant data from websites and public APIs. The collected data is then checked, cleaned, and stored. By following the Rapid Application Development principles, the proposed system reduces manual work, saves time, and provides a modular and scalable solution. The RAD ML framework makes machine learning model development easier, faster, and more accessible to students, researchers, and real-world applications.