linkedin-auto-scraper 0.2.1

A tool to scrape infomation of people in a particular based on their job title and location

LinkedIn Auto Scraper

LinkedIn Auto Scraper is a command-line application designed to scrape cleaned information from LinkedIn profiles based on job titles and locations. The app offers two main commands: login and scrape. Once you've logged in using the login command, you can perform continuous scraping without the need to log in again, thanks to the authentication caching feature.

Table of Contents

• Installation
• Usage
  • Login
  • Scrape
• Contributing
• License

Installation

1. Ensure you have Python 3.x installed.

2. Clone this repository:

• git clone https://github.com/your-username/linkedin-auto-scraper.git

• Navigate to the project directory:

• cd linkedin-auto-scraper

• Install the required dependencies

  pip install -r requirements.txt

Usage

Login

To use the app, you need to log in first. This step is required for authentication purposes. Run the following command:

bash

linkedin-auto-scraper login

This command lets you log in to your LinkedIn account. Follow the prompts to provide your LinkedIn credentials. Your authentication will be cached to allow continuous scraping without repeated logins.

Scrape

Once you're logged in, you can start scraping LinkedIn profiles based on job titles and locations. Run the following command:

bash

linkedin-auto-scraper scrape

By default, the app will search for profiles related to the HR field. You can customize the search by using the following options:

• --search (-s): Specify the search parameter (default: "hr").
• --location (-l): Use it to search for profiles in a specific location (default: None).
• --excel: This option is required and indicates whether to generate an Excel file with the scraped data (default: no-excel).

Example usage:

bash

linkedin-auto-scraper scrape --search software engineer --location San Francisco --excel

Follow the prompts to enter the desired job title and location. The app will scrape and display cleaned information from LinkedIn profiles matching your criteria.

Contributing

Contributions are welcome! If you'd like to contribute to the project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature/bugfix: git checkout -b feature-name
3. Commit your changes: git commit -m "Description of changes"
4. Push to the branch: git push origin feature-name
5. Create a pull request.

License

This project is licensed under the [MIT License]