Authors - Mangapuram Sadhguna Sri, Narendra VG, Shiva Prasad Gundibail Abstract - This project tackles the inefficiency and possibility for bias in traditional resume screening. When a single job post draws hundreds of applications, manual review becomes a bottleneck. We designed an intelligent system that uses NLP and machine learning to automate this process. Our system features a complete pipeline that parses PDF resumes and converts their text into structured features using TF-IDF or Sentence Embeddings. A trained classifier then evaluates these features to predict the optimal job role for each candidate. By ranking and classifying applicants based on skill relevance, our tool allows hiring managers to bypass manual sorting and focus directly on the most promising individuals, ensuring a faster and more equitable screening process.
