Resume Matcher with LinkedIn Job Scraper

🚀 Overview

This open-source project provides a LinkedIn Job Scraper combined with a Resume Matcher that evaluates how well a given resume aligns with job descriptions. It helps job seekers find relevant job postings and assess their suitability using a TF-IDF-based similarity model.

🔧 Features

• 🔍 LinkedIn Job Scraper: Extracts job listings based on a keyword search.
• 📊 Resume Matching: Uses TF-IDF and cosine similarity to compare job descriptions with a user's resume.
• 🏆 Skill Analysis: Identifies missing skills from the resume compared to the job description.
• 🖥 Streamlit Interface: A user-friendly web UI for easy interaction.

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📁 Project Structure

resume-matcher/
│── frontend/                # Frontend-related code and UI components  
│   ├── app.py               # Main application script for the frontend  
│   ├── ui_components.py     # UI-related functions and components  
│   ├── utils.py             # Utility functions for frontend operations  
│── .gitignore               # Files and folders to be ignored by Git  
│── LICENSE                  # License information for the project  
│── linkedin_jobs.json       # JSON file containing scraped LinkedIn job data  
│── main.py                  # Main script which runs the streamlit application via cmd 
│── README.md                # Project documentation and instructions  
│── requirements.txt         # List of dependencies required for the project  
│── resume_matcher.py        # Script for matching resumes with job descriptions  
│── scraper.py               # Script for scraping job listings from LinkedIn  

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🛠 Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/yourusername/resume-matcher.git
cd resume-matcher

2️⃣ Install Dependencies

Ensure you have Python installed (>=3.7), then install required libraries:

pip install -r requirements.txt

3️⃣ Run the Application

• in your command prompt, run :

python -u main.py

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📜 How It Works

🕵️ 1. Scraping LinkedIn Jobs

• The scraper.py fetches job listings based on a given keyword.
• It requires the correct selectors from the HTML of Linkedin
• The extracted data includes:
  • Job Title
  • Company Name
  • Job Description

🎯 2. Resume Matching

• The resume_matcher.py applies TF-IDF and cosine similarity to measure how well a resume aligns with a job description.
• Outputs:
  • Match Score (0-1 scale)
  • Missing Skills (skills in the job description but absent in the resume)

🖥️ 3. Streamlit UI

The frontend/app.py provides a web-based UI for user-friendly interaction.

• Upload a resume in PDF or TXT format (PDF support is not fully functional yet).
• Enter a job keyword to search LinkedIn.
• View job listings ranked by match score.
• Use the advanced search function (currently not working).

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🫵 Your Part In This

• The project currently opens the browser physically which can be avoided
• Some of the css selectors for scraping the job listings are missing without which the scraper wont function
• You'll have to dive into the inspect menu in your browser for Linkedin to find those selectors
• The selectors could be the class or the data test id
• optionally : You could always imporve the interface and the model performance, as well as make the scraper target more specific areas of interest