Resumes come in various formats, making it challenging to extract crucial information. NK.com is a platform that hosts resumes and connects them with hiring managers from Fortune 500 companies. Currently, their process relies heavily on manual labor. When a resume is uploaded, a human reviewer goes through it to extract essential details such as the Name, Designation, and Place of Work.

This manual process is labor-intensive and problematic, especially when dealing with thousands of uploaded resumes and a limited workforce. To address this issue, NK.com has commissioned us to develop a resume parser that can automatically extract key elements from resumes using Machine Learning (ML) and present the results.

To aid in the development of the resume parser, NK.com has provided us with a dataset sourced from Dataturks. This dataset includes JSON files with labeled information for each resume. The labeled fields include:

• Location
• Designation
• Name
• Years of Experience
• College
• Degree
• Graduation Year
• Companies worked at
• Email address

To build resume parser model to automatically identify and extract the key elements from resumes, streamlining the hiring process for NK.com.

• Please create the directory structure in the following way to execute.

  • '/Resume_Parser'

• Install requirements with "pip install -r requirements.txt"

• Run engine.py