01 Synopsis
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M.E. Project Report
M.E. Resume Screening System Final Year Project Report
Complete M.E. final-year project report with documentation, diagrams and viva-ready structure. Instant PDF & Word download — plagiarism-free and faculty-aligned.
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02 Pre Defined Project Report
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
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.
Login Credentials
Administrator
- Username:
admin - Email:
[email protected] - Password:
admin123
HR Users
[email protected]/hr123[email protected]/hr123
Candidates
- Multiple demo users (e.g.,
[email protected],[email protected]) - Password:
cand123
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- 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
- 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
- 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
- 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
- Windows:
- 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
README Included
Setup Support Available
Administrator
- Username:
admin - Email:
[email protected] - Password:
admin123
HR Users
[email protected]/hr123[email protected]/hr123
Candidates
- Multiple demo users (e.g.,
[email protected],[email protected]) - Password:
cand123