In today's world, many people apply for jobs on a daily basis. Often, there are hundreds if not thousands of applications for a single job offering. The process of analyzing and scrutinizing resumes of such a large number of candidates can be a hectic and cumbersome task. Most of the companies recei
Resume Analyzer
In today's world, many people apply for jobs on a daily basis. Often, there are hundreds if not thousands of applications for a single job offering. The process of analyzing and scrutinizing resumes of such a large number of candidates can be a hectic and cumbersome task. Most of the companies receive resumes on a daily basis and this cycle repeats on every job opening. Also it is very difficult for anyone to go through hundreds of resumes documents and if he/she somehow manages to do this task there is highly unlikely that a human analyzer can detect important features of these documents, even if an organization manages to go through properly these documents there are always chances of human error. Along these errors, it is a very hectic and time consuming task to gather resumes and read them one by one. There is potential for an application that can do this task of important information extraction from these documents efficiently. A web app which will receive resumes for a job and extract the desired entities from these documents and hence saving the human effort and time. This web app will perform the above mentioned task itself and generate the useful entities.
The model will recognize the entities in resumes. Most of the time there is text in the resumes which is not required or not useful. Our model will provide only useful information to the user or analyzer after extracting the entities.
This task will be achieved by sub-domain of Natural Language Processing, known as Named Entity Recognition(NER). We will use NER to develop an application to which a user can upload resume documents in PDF or word format, our deployed model will extract entities and provide them as output
Our app will help organizations to automate the procedure and only extract the useful information or more specifically it will extract useful entities and give it to the user or the analyzer. Therefore, our model will save time and will extract important information efficiently and this task will be performed by using Natural Language Processing techniques.
Module1. Website Front-End
Module 2. Named-Entity Recognition module
We will use Natural Language Processing techniques to train a deep learning model. This deep learning model will take the resume (in pdf or word format) as input and extract the required entities from it. The user can then view the extracted entities saving his time and effort.
Tools & Technology
Tools:
Visual Studio Code
MS Word
MS PowerPoint
Jupyter Notebook
PyCharm
Technology:
HTML
CSS
Javascript
Bootstrap
SQL
Python
Prodigy
Spacy
Django/Flask
Django REST Framework
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Prodigy | Equipment | 1 | 63000 | 63000 |
| Total in (Rs) | 63000 |
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