Advanced Job Portal Using Python WebApp: Resume Analysis & Scoring with NLP & LSTM-Based Machine Learning

Job Portal Using Python

Overview

Job Portal Using Python is an advanced recruitment system that helps make the hiring process faster and smarter by automatically screening and scoring resumes. It is built with Python using the Django framework and uses Natural Language Processing (NLP) along with LSTM-based Machine Learning models to compare resumes with job descriptions and give them a relevance score.The system has two main user roles — Employers and Candidates — each with their own dashboard. Employers can post jobs, review applications, schedule interviews, and see detailed resume analysis results. Using NLP, the app extracts key skills, qualifications, and experiences from resumes, helping recruiters make better, data-driven hiring decisions.

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Introduction

In today’s competitive job market, recruiters face the challenge of reviewing hundreds of resumes for a single job post. This project addresses that challenge by integrating automated resume screening with advanced NLP techniques and LSTM deep learning models to classify and score resumes based on their match to job requirements.

Employers can post jobs, create interview quizzes, and manage applications, while candidates can upload resumes, apply for jobs, and take interviews — all within a simple, intuitive interface.

Available Features

For Employers:

• Employer account creation and dashboard access.
• Ability to post jobs with detailed job descriptions.
• Creation of interview quizzes for posted jobs.
• View and evaluate candidate applications with relevance scores.

For Candidates:

• Candidate account creation and profile management.
• Upload resumes in multiple formats (PDF, DOCX, etc.).
• Search for available job postings and apply.
• Take interview quizzes for applied jobs.

Resume Analysis:

• Resume parsing with NLP techniques (Tokenization, Lemmatization, and TF-IDF Vectorization).
• LSTM-based classification to assess resume relevance.
• Detailed scoring based on job description match and quiz performance.

General Features:

• Secure authentication system for both employers and candidates.
• Scalable backend with Django and database flexibility (SQLite or MySQL).
• Background task processing with Celery and Redis.
• Clean, responsive web interface using HTML, CSS, and JavaScript.

Technology Stack

• Backend: Django (Python)
• Frontend: HTML, CSS, JavaScript
• Database: SQLite (default) or MySQL (optional)
• NLP Libraries: NLTK, spaCy
• Machine Learning: TensorFlow, Keras (LSTM Model)
• Task Processing: Celery, Redis

NLP and Machine Learning Process

1. Resume Parsing & Preprocessing
   • Tokenization: Splitting text into individual words/tokens.
   • Lemmatization: Converting words to their base forms.
   • Vectorization: Transforming text into numerical vectors using TF-IDF.
2. LSTM-Based Classification
   • Deep learning model trained on labeled datasets of resumes and job descriptions.
   • Learns patterns and skills relevant to specific job roles.
   • Outputs a relevance score indicating match percentage.
3. Scoring Mechanism
   • Final score combines resume-job match and interview quiz performance.
   • Higher-scoring candidates are prioritized for employer review.

Installation

1. Navigate to the project directory cd Job Portal Using Python-Resume-Analysis-Scoring-NLP-ML
2. Create and activate virtual environment python -m venv env source env/bin/activate # For Windows: env\Scripts\activate
3. Install dependencies pip install -r requirements.txt
4. Configure the database
   • SQLite (default) requires no changes.
   • For MySQL, update settings.py with database credentials.
5. Run migrations python manage.py migrate
6. Start server python manage.py runserver

Usage

• For Employers: Post jobs, manage applications, view scored resumes.
• For Candidates: Upload resumes, search jobs, apply, and take interview quizzes.
• For Admins: Manage users, job posts, and overall system settings.

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