LinkedIn Job Scraper - Complete with Natural Language Processing utilization for Resume & Job Description Comparisons

Today we will be looking at an implementation of the Selenium library to first scrape LinkedIn Jobs postings by Title and Location, and then return Title, Location, Description, as well as the Company who posted the Job. To do so, all you need to do is input your credentials below, as well as what Job Title you are looking for, where you are looking, and how many pages worth of scraping you would like to do on LinkedIn.

Once these jobs are scraped, we will then call on Natural Language Processing in combination with sklearn's CountVectorizer and cosine_similarity to determine how efficient our resume may be in applying for the job.

This project can be further adapted to perform analysis on the contents of the resume, as well as create visualizations, such as word clouds, to show the most common skills required of a given job; i.e. a Data Scientist likely will mention analysis, Python, and Data quite often.