LinkedIn Job Apply Automation

This project automates job scraping and job applications on LinkedIn. It utilizes Selenium for browser automation, handles Easy Apply workflows, and applies dynamic filtering based on user-configurable criteria.

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Table of Contents

1. Overview
2. Project Structure
3. Requirements
4. Setup
5. Usage
6. Configuration
7. Key Features
8. Error Handling
9. Known Issues
10. Future Improvements

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Overview

The LinkedIn Job Apply Automation project helps in:

1. Scraping job postings from LinkedIn based on predefined roles, filters, and locations.
2. Processing each job post to determine eligibility (filtering blocked companies, keywords, and experience requirements).
3. Automating the Easy Apply process or logging External Apply links for manual applications.

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Project Structure

LinkedIn-Apply-Automation/
├── config.py         # Contains user settings and job search configurations
├── helpers.py        # Utility functions for automation
├── Scrape.py         # Script to scrape job links
├── apply_jobs.py     # Automates the application process
├── Resumes/          # Directory for resumes (default and generated)
├── CSV/              # Stores job links and processed IDs
└── README.md         # Project documentation

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Requirements

• Python 3.8 or higher
• Google Chrome Browser
• Selenium
• undetected_chromedriver
• Additional Python packages: numpy, requests, pyautogui

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Setup

1. Clone the repository:

   git clone https://github.com/your-repo/linkedin-apply-automation.git
   cd linkedin-apply-automation

2. Install the required libraries:

   pip install -r requirements.txt

3. Update config.py with your settings:

   • Preferred roles, experience levels, and locations.
   • User-specific details like contact information and LinkedIn profile.
   • Resume paths and other configurations.

4. Set up a Chrome profile:

   • Modify setup_driver() in helpers.py to include your Chrome profile path.

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Usage

1. Scrape Job Listings

Run the Scrape.py script to collect job IDs based on search criteria:

python Scrape.py

Output:

• The script saves filtered job links in CSV/collected_links_run.csv.

2. Automate Job Applications

Run the apply_jobs.py script to apply for the jobs:

python apply_jobs.py

Output:

• The script processes the jobs and automates Easy Apply applications.
• External job URLs are saved to CSV/external_URL.csv.

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Configuration

The config.py file allows you to customize all settings:

General Settings

• additional_time: Extra delay for slow networks.
• current_run_csv: File path to store current job links.

Query Settings

• query_params: Filters for LinkedIn job search (distance, experience, etc.).
• roles: Job titles to search for (e.g., Machine Learning Intern, Python Developer).

Filters

• BLOCKED_COMPANY_NAMES: Companies to exclude.
• BLOCKED_DESCRIPTION_WORDS: Keywords to skip job descriptions.

User Details

• Personal information, resume path, years of experience, work authorization, and salary expectations.

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Key Features

1. Scraping LinkedIn Jobs

   • Mimics human-like scrolling and interaction to collect job listings.
   • Supports pagination to fetch jobs across multiple pages.

2. Dynamic Filtering

   • Filters blocked companies, keywords, unpaid internships, and high-experience roles.

3. Easy Apply Automation

   • Detects and automates the Easy Apply process.
   • Uploads resumes dynamically based on the job description via a webhook.

4. External Apply Logging

   • Saves links for jobs that redirect to external applications.

5. Resume Customization

   • Uses a webhook to fetch tailored resumes for each application.

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Error Handling

• Network Errors: The script retries connections automatically.
• Missing Elements: If LinkedIn page structure changes, missing elements are logged and skipped.
• Duplicate IDs: Duplicate job IDs are automatically removed from CSV files.

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Known Issues

1. LinkedIn may temporarily block your account if excessive automation is detected.
   Solution: Use undetected_chromedriver and human-like delays (random_wait).
2. External apply processes are logged but not automated.

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Future Improvements

1. Add support for cover letter customization.
2. Automate external applications where possible.
3. Enhance error handling for CAPTCHA or multi-step applications.

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Author

Prakash Maheshwaran

• LinkedIn: Prakash Maheshwaran
• Portfolio: prakash.dynoxglobal.com

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Disclaimer

This tool is for personal use only. Automating applications on LinkedIn may violate their terms of service. Use responsibly.

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