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In software engineering, it is now necessary to divide needs into functional and non-functional categories to ensure proper operation and overall performance. This study aims to distinguish between functional (F) and non-functional (NF) requirements for application software and classify them accordingly. A portion of the data has been obtained from online resources. In this study, data is cleaned using normalization steps and then proceeded with subsequent steps such as text preprocessing and vectorization. Bag of Words, Term Frequency-Inverse Document, Featurization and Machine Learning Models, ROC and AUC curves, Bi-Grams and n-Grams in Python, Word2Vec, and confusion matrix were covered. Early detection of NFRs allows us to make initial design decisions. Machine Learning algorithms were applied to identify and classify functional and non-functional requirements for application software development based on user requirements. This paper aims to find out which ML algorithm and which model of NLP gives better accuracy and best performance for binary classification. In this study, the combination of BoW and Multinomial Naive Bayes provides the best performance for binary classification. This paper classifies functional Requirement (FR) and non-functional requirements (NFRs) and assists the software developer, software designer, testers, etc., in developing applications. This paper is also helpful for faster software development and delivery. This step facilitates the creation of an SRS document and avoids unnecessary work and complexity during the requirement analysis phase. [ABSTRACT FROM AUTHOR]

Requirement analysis serves as a link between end-users and the software development team in software development. User requirements are nothing more than their desires, which software must fulfill for them to be satisfied. The processes involved in software development are planning, data gathering, data analysis, design, programming, testing, implementation, and maintenance processes. Planning is the first step in any software development project. Human intervention is required during these processes because they are extremely complex. Several types of knowledge are required during the software development process, including problem domain, programming domain, and programming language knowledge. To create a single solution, it is sometimes necessary to bring together different types of knowledge. The aim of software development is to satisfy and fulfill all users' needs through software, which is the primary goal of all software development. A problem during the requirement analysis phase will cause the entire SDLC process to be halted. It is because the SDLC is cyclic. The SDLC requirement analysis phase is extremely important to mitigate this risk. Because of the high quality of software requirements, it is necessary to have high-quality software requirements. Specifically, this paper aims to discuss the application of artificial intelligence techniques for requirement analysis in the context of software development projects. For this research, end-user requirements are investigated and analyzed and then transformed into AI techniques, among other things. This paper aims to identify requirements problems during the requirement analysis and provide AI techniques to overcome these problems as quickly as possible. [ABSTRACT FROM AUTHOR]