This project has been done with a view to help job-seekers find jobs at their nearest locations easily.
The technique used in the implementation of this project is called “web-scraping”. Using web-scraping, we can access the contents of any webpage and display it in a format that we like.Here, the job details from a popular jobsite have been scraped using Python and the necessary libraries such as beautifulsoup, openpyxl and requests. These details have then been automatically arranged in an Excel spreadsheet, without any manual entry.
Hence, instead of scrolling through several lists of jobs, the details of each job are made in a more readable format through the spreadsheet. Each job can be compared easily. It is also possible to apply for the desired job directly by clicking the apply link provided in the spreadsheet. Whenever the webpage gets updated, we get a new spreadsheet with the updated data. Hence, the data accessed is always up to date.
Finally, this spreadsheet is imported to Google Maps and the exact location of each company listed is mapped. This helps users locate the company easily and also gives a view of the overall concentration of jobs in and around his/her current location. In this project, we'll be scraping data from the job site www.monsterindia.com
- Python 3.6+
- Microsoft Excel
- Any standard web browser
- Before running the code in the python file, you may need to install the libraries mentioned in the description.
- You'll need a proper internet connection for this to work.
- Copy the code from the python file given and run it in your editor.
- You may change the name of the .xlsx file given in the code.
- Once you've run the code, you'll see the job titles, names of the companies and other important details listed out.
- Also, a .xlsx file (with the same name as the one given in the program) will be created. This contains the details all job vacancies listed in an Excel spreadsheet.
- The links in the spreadsheet take the user directly to the job application page of the site and a map with the job locations of each listed job vacancy.
Feel free to contribute to this project to further develop it to scrape multiple job sites.