-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathSeemore.html
213 lines (206 loc) · 12.4 KB
/
Seemore.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
<!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>
<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>
<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>
<script>
var tablinks = document.getElementsByClassName("tab-links");
var tabcontents = document.getElementsByClassName("tab-contents");
function opentab(tabname){
for(tablink of tablinks){
tablink.classList.remove("active-link");
}
for(tabcontent of tabcontents){
tabcontent.classList.remove("active-tab");
}
event.currentTarget.classList.add("active-link");
document.getElementById(tabname).classList.add("active-tab");
}
</script>
<script>
var sidemenu = document.getElementById("sidemenu");
function openmenu(){
sidemenu.style.right = "0";
}
function closemenu(){
sidemenu.style.right = "-200px";
}
</script>
</section>
</section>
</body>