The rapid advancement of Artificial Intelligence has opened new possibilities in automating human-centric tasks such as interview evaluation. The proposed project, “Interview Evaluation System Using NLP”, aims to provide an intelligent platform that simulates real interview scenarios and evaluates user responses automatically. The system leverages Natural Language Processing (NLP) techniques to analyze textual answers and compare them with predefined ideal responses using similarity measures such as TF-IDF and cosine similarity.

The platform allows users to select a job role and attempt a set of interview questions. Based on their responses, the system evaluates answer relevance, structure, and keyword presence, and assigns a score. In addition to scoring, the system provides meaningful feedback to help users improve their communication and conceptual clarity. A graphical representation of performance is also generated to help users track their progress across multiple questions.

Unlike traditional mock interviews that require human evaluators, this system offers a scalable, cost-effective, and always-available solution. It eliminates bias, ensures consistent evaluation, and allows users to practice anytime. The project demonstrates how NLP can be effectively applied to educational and career preparation tools, enhancing self-learning and interview readiness.

KEYWORDS: NLP, TF-IDF, Interview Evaluation, Text Similarity, Automated Feedback, Performance Analysis