Project Title:

AI-Based Resume Screening System

Project Description:

The AI-Based Resume Screening System is an intelligent recruitment tool designed to automate and streamline the candidate selection process. Leveraging Artificial Intelligence and Natural Language Processing (NLP), the system analyzes large volumes of resumes to identify the most suitable candidates based on predefined job criteria.

This system reduces manual effort by parsing resumes, extracting relevant information (such as skills, experience, education, and certifications), and ranking applicants according to their fit for the role. It eliminates human bias, ensures consistency, and significantly speeds up the hiring process.

Key Features:

• Resume Parsing: Extracts structured data (name, skills, education, experience, etc.) from various resume formats (PDF, DOCX).

• Keyword Matching: Identifies and scores resumes based on relevant keywords and job descriptions.

• Machine Learning-Based Ranking: Uses algorithms to rank candidates based on similarity to job requirements.

• Bias Detection & Fairness: Ensures fair screening by reducing gender, age, or ethnicity-based biases.

• Recruiter Dashboard: Displays candidate rankings, match scores, and resume summaries.

Technologies Used:

• Frontend: React.js / HTML5 / Bootstrap

• Backend: Python (Flask / Django)

• NLP Libraries: spaCy, NLTK, BERT

• Machine Learning: Scikit-learn / TensorFlow (for classification & ranking models)

• Database: PostgreSQL / MongoDB

• File Parsing: Apache Tika / pdfminer / docx2txt

Use Cases:

• HR departments filtering hundreds of job applications for a single vacancy.

• Job portals providing automated resume scoring to employers.

• Startups and SMEs with limited recruitment staff streamlining their hiring process.

Benefits:

• Speeds up candidate shortlisting by up to 70%.

• Reduces human bias and increases objectivity in hiring.

• Enhances accuracy in matching resumes with job roles.

• Improves candidate experience with faster response times.