Resume Screening & Job Recommendation System

📌 Project Overview

This project aims to automate the resume screening process by matching resumes with job descriptions using basic Natural Language Processing (NLP) techniques. The system helps rank resumes based on their relevance to specific job requirements.

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🎯 Objectives

• Extract relevant skills from resumes
• Compare resumes with job descriptions
• Rank candidates based on similarity scores
• Store and manage resume data efficiently

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🛠️ Tech Stack

• Python
• NLP (TF-IDF)
• Scikit-learn
• MongoDB

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📂 Dataset

• Resume text data
• Job description text data

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⚙️ Methodology

1. Text preprocessing (cleaning, tokenization)
2. Feature extraction using TF-IDF
3. Similarity computation between resumes and job descriptions
4. Ranking resumes based on relevance score
5. Data storage using MongoDB

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📊 Output

• Ranked list of resumes for a given job description
• Similarity score indicating relevance

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📌 Key Learnings

• Basics of text preprocessing and vectorization
• Understanding similarity-based recommendation systems
• Practical application of NLP in recruitment systems

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🚀 Future Improvements

• Improve text preprocessing techniques
• Add classification-based screening
• Build a user interface for recruiters