Resume Screening System Final Year using Python and Flask NLP | Source Code
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Resume Screening System Final Year using Python and Flask NLP

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Complete final-year project source code with frontend, backend, database, and setup guide. Instant download after secure payment.

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  • Complete project source files
  • Database script included
  • How-to-run guide

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Project Overview

Description, tech stack, and what is included

Full source Frontend + backend
Database .sql file
Setup guide README included

Recruitment Intelligence is a final year project built using Python Flask and NLP techniques for automated resume screening and recruitment management. This final year project connects administrators, HR teams, and candidates on a single platform. Candidates can upload resumes in PDF, DOC, or DOCX format, which are parsed and processed using NLP to extract skills and classify profiles. HR users can create job postings, screen applications, view matching scores, rank candidates, and manage hiring pipelines including shortlist, reject, select, and interview scheduling. The admin panel of this final year project manages users, HR accounts, candidates, job postings, NLP keywords, resume corpus, reports, and feedback. This project is suitable for a final year major project in Python, Flask, NLP, and recruitment automation systems.

Technical snapshot

Project
Resume Screening System Final Year using Python and Flask NLP
Stack
PYTHON
Includes
Code, DB, README
License
Academic submission
Secure CCAvenue payment · Instant download · Need help? WhatsApp us

Ready to download?Pay once · Use for submission & viva

Admin Features

Modules and controls available to administrators

  • Admin dashboard for this final year project with KPIs, recent applications, and moderation alerts
  • Brand settings with logo, footer, and contact details
  • HR account management (create, approve, activate, delete)
  • Candidate management with resume inventory and application links
  • Job category and skill taxonomy management
  • Global job posting moderation (approve/reject)
  • Resume corpus management with NLP reprocessing
  • Global application registry with pipeline override controls
  • Screening telemetry and scoring overview
  • NLP keyword management (classification, skill boost, generic)
  • Reports and analytics
  • Feedback moderation inbox
  • Admin password change

User Features

What end users can do in this application

  • Candidate registration and login in this final year project
  • Dashboard with application stats and interview reminders
  • Profile management and resume upload
  • Resume parsing and reprocessing
  • Job search and filtering
  • Job application with selected resume
  • Application tracking with status updates
  • View matching score and NLP summary
  • Withdraw application where allowed
  • Interview scheduling view
  • Feedback submission and management

Other Features

Additional capabilities included in the project

  • HR login and dashboard for this final year project
  • Company profile update
  • Job posting creation, editing, and deletion
  • Application queue management
  • Resume review and NLP re-screening
  • Candidate ranking by matching score
  • Shortlist, reject, select pipeline actions
  • Interview scheduling with details
  • Reports and feedback submission
  • Public landing page included in this final year project
  • Role-based authentication (admin, HR, candidate)
  • Resume upload support for PDF, DOC, DOCX
  • NLP-based skill extraction and classification
  • Matching score calculation
  • SQLite default database with optional override
  • Bootstrap-based UI design
  • Seed data with demo users, jobs, applications, and interviews
  • Suitable for final year project demo, viva, source code review, and report preparation

How to Run

Step-by-step setup on your laptop or PC

  • Open the final year project folder.
  • Create a virtual environment:
    python -m venv .venv
  • Activate environment:
    • Windows: .venv\Scripts\activate
    • Linux/macOS: source .venv/bin/activate
  • Install dependencies:
    pip install -r requirements.txt
  • Initialize database and seed demo data:
    python seed_database.py
  • Run the Flask server:
    python run.py
  • Open the app at:
    http://127.0.0.1:5000

Login Credentials

Default demo accounts for testing after setup

Administrator

HR Users

Candidates

License

Usage terms for academic and personal projects

Related Tags

Search terms and categories for this source code

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