Smart assessment & performance analysis using ML techniques

Interview preparation is a crucial step for students transitioning from academic environments to professional careers. Traditional preparation methods such as static question banks or peer-based mock interviews often lack personalization, objective evaluation, and structured feedback. This base paper presents a LLM-based mock interview platform designed to simulate a realistic interview environment and analyze candidate performance. The proposed system dynamically generates interview questions based on the resume and evaluates candidate responses using natural language processing. Candidate answers are assessed for relevance, completeness, and conceptual clarity, and detailed feedback is generated to highlight strengths and areas for improvement. Experimental observations indicate that such an automated system can provide consistent and scalable interview preparation support for students and job seekers.