Project Description:

The Resume Screening and Classification System is an AI-driven platform designed to automate the evaluation of resumes. The system allows users to upload large volumes of resumes in various formats (PDF, DOCX, etc.). The platform then processes these resumes, extracting key information such as skills, experience, education, and certifications.

Key Features:

Automated Resume Upload: Users can bulk upload resumes through a simple interface.

AI-Powered Screening: The system utilizes natural language processing (NLP) and machine learning algorithms to screen and classify resumes based on job-specific criteria.

Scoring System: Each resume is scored on various parameters such as relevance to the job description, keyword matches, experience level, and more.

Resume Improvement Suggestions: The system provides personalized advice to improve the resume, such as adding specific skills, tailoring the resume for specific job descriptions, or highlighting particular experiences.

User Dashboard: A dashboard displays the scoring results, recommendations, and allows users to download or further analyze the processed resumes.

Reporting & Analytics: Generate detailed reports and analytics to help recruiters make informed decisions based on the screened resumes.

Technologies Used:

Backend: Python, Django/Flask

Frontend: React.js or Angular

Database: PostgreSQL/MySQL

NLP & Machine Learning: TensorFlow, spaCy, or BERT models for resume parsing and scoring

Cloud Storage: AWS S3 or Google Cloud Storage for storing resumes

Deployment: Docker, Kubernetes

Project Goals:

Streamline the resume screening process, saving time and reducing manual effort.

Increase the accuracy of candidate shortlisting by focusing on the most relevant resumes.

Provide actionable feedback to job seekers to enhance their resume quality and job matching prospects.