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Project1.html
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<!DOCTYPE html>
<html lang="en".>
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Project 1</title>
<meta name="description" content="Case study page of Project">
<link rel="stylesheet" href="Styles.css">
<script src="https://kit.fontawesome.com/44c8d01a5a.js" crossorigin="anonymous"></script>
</head>
<body>
<div id="header">
<div class="container">
<nav>
<img src="images/logo.png" class="logo">
<ul id="sidemenu">
<li><a href="#header">Home</a></li>
<li><a href="#about">About</a></li>
<li><a href="#Services">Services</a></li>
<li><a href="#Projects">Projects</a></li>
<li><a href="#Contact">Contact</a></li>
<i class="fa-solid fa-xmark" onclick="closemenu()"></i>
</ul>
<i class="fa-solid fa-bars" onclick="openmenu()"></i>
</nav>
<div class="header-text">
<h1>Resume Parsing in Python Project Objective</h1>
<p class="text-primary ">Gone are the days when recruiters used to manually screen resumes
for a long time. Sifting through thousands of candidates resumes for a job is no more a
challenging task- all thanks to resume parsers. Resume parsers use machine learning
technology to help recruiters search thousands of resumes in an intelligent manner so
they can screen the right candidate for a job interview.</p>
</div>
</div>
</div>
<section class="project-details">
<div class="main-container">
<div class="project-details__content">
<div class="project-details__showcase-img-cont">
<img
src="images/imagescredit2.png"
alt="Project Image"
class="project-details__showcase-img"
/>
</div>
<div class="project-details__content-main">
<div class="project-details__desc">
<h3 class="project-details__content-title">Project Overview</h3>
<p class="project-details__desc-para">
A resume parser or a CV parser is a program that analyses and extracts CV/ Resume data
according to the job description and returns machine-readable output that is suitable for
storage, manipulation, and reporting by a computer. A resume parser stores the extracted
information for each resume with a unique entry thereby helping recruiters get a list of
relevant candidates for a specific search of keywords and phrases (skills). Resume parsers
help recruiters set a specific criterion for a job, and candidate resumes that do not
match the set criteria are filtered out automatically.
</p>
<p class="project-details__desc-para">
This project uses Python's library, SpaCy to implement various NLP (natural language
processing) techniques like tokenization, lemmatization, parts of speech tagging, etc.
, for building a resume parser in Python. And, considering all the resumes are submitted
in PDF format, we will learn how to implement optical character recognition (OCR)
for extracting textual data from the documents. The resulting application will require
minimum human intervention to extract crucial information from a resume, such as an
applicant's work experience, name, geographical location, etc. It is one of the most
exciting NLP projects for beginners, so make sure you attempt it.
</p>
<p class="project-details__desc-para">
To solve this, our resume parser application can take in millions of resumes,
parse the needed fields and categories them. First, we train our model with these
fields, then the application can pick out the values of these fields from new resumes
being input.
</p>
<p class="project-details__desc-para">
The dataset of resumes has the following fields:
</p>
<ul class="project-details__desc-para">
<li>Location</li>
<li>Designation</li>
<li>Name</li>
<li>Years of Experience</li>
<li>College</li>
<li>Degree</li>
<li>Graduation Year</li>
<li>Companies worked at</li>
<li>Email address</li>
</ul>
</div>
<div class="project-details__tools-used">
<h3 class="project-details__content-title">Tools To Be Used</h3>
<div class="skills">
<div class="skills__skill">Python</div>
<div class="skills__skill">SQL</div>
<div class="skills__skill">Html</div>
<div class="skills__skill">css</div>
<div class="skills__skill">Java</div>
<div class="skills__skill">SpaCy</div>
</div>
</div>
</div>
</div>
</div>
</div>
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</body>