Resume Screening Project Guide

Recruiters spend countless hours manually screening resumes, often leading to slow hiring cycles and missed talent. With hundreds of resumes for a single position, identifying qualified candidates quickly becomes overwhelming. Traditional keyword-based methods are often too rigid. Automating resume screening using NLP allows recruiters to efficiently filter and rank resumes, reducing hiring time while maintaining quality. This project teaches document classification, information extraction, and natural language understanding.

Using Natural Language Processing, machine learning models can parse resumes, extract relevant information, and rank candidates based on job descriptions. Keyword matching, skill extraction, semantic similarity, and section classification enable a deep understanding of resumes beyond simple keyword searches. By training classification models and building intelligent scoring systems, you can automate a major HR workflow, bringing efficiency, objectivity, and scalability to recruitment processes.

The system accepts resumes in PDF or text formats, parses them into structured fields like skills, education, and experience. Machine learning models compare these extracted features with the target job description, scoring resumes based on relevance. Additional NLP tasks like named entity recognition (NER) and semantic similarity enhance screening quality. The top-ranked candidates are presented to recruiters through an intuitive dashboard, minimizing manual effort and maximizing hiring efficiency.

• Collect a dataset of resumes and associated job descriptions for training and testing.
• Preprocess text: extract important sections like Education, Skills, and Experience.
• Vectorize resume text and job descriptions using TF-IDF or transformer embeddings like BERT.
• Train similarity or classification models to rank resumes based on relevance.
• Deploy the system with a resume upload portal and automatic shortlisting feature.

• SpaCy NLP Library
• HuggingFace Transformers
• Open Source Resume Parser Libraries