I created a webscraper 🕸️ tool to fetch indeed data. It will return job title, company name, job id, url of the job, salary(if present) and whole description of respective job.

Install required dependencies in your project folder.

pip install -r requirements.txt

Make sure you have Chrome ⬇️ latest version installed in your system. This step creates scraped_job_file.csv, however, you won't have job descriptions.

Recommended: Clean your scraped_job_file.csv for duplicate values, before running this code.

This step extracts job_description and assign them to the respective rows. It will take good amount of time, go grab a coffee ☕. O/P is updated scraped_job_file.csv with merged job description.

I did not implement multi threading 🧵 (which would have otherwise saved you a lot of time), because of time and limited knowledge. Feel free to fork this repo and implement. Good luck. 🤓

Implement a pretrained NER model and extract information such as programming languages included, type of work(remote, hybrid, in-person), salaries from description column. Or, you can use simple logic to match respective words.