Resume Parsing with Python and Machine Learning: Resumes are one of the most important documents that job seekers apply for a job. For recruiters, it can be an overwhelming task to go through the resume parser is a tool that extracts information from resumes and converts it into a structured format that can be easily analyzed and processed.
Here we will discuss a Python project for building a resume parser that can be used for data science jobs. We will cover the following topics:
- What is a resume parser?
- Why do we need a resume parsing for data science jobs?
- Building a resume parser in python
- Conclusion
What is a resume parser?
A resume parser is a software tool that extracts relevant information from a resume such as the candidate’s name, contact information, education, work experience, and skills. It uses natural language processing (NLP) algorithms to analyse the resume text and identify the relevant information.
Why do we need a resume parsing for data science jobs?
Data science is a field that requires a specific set of skills, knowledge, and experience. Recruiters receive a large number of resumes for data science positions, and it can be time consuming to manually go through each one. A resume parser can help streamline the hiring process by quickly extracting relevant information from resumes, allowing recruiters to focus on the most qualified candidates.
Building a Resume Parsing with Python and Machine Learning in this section, we will discuss the steps for building a resume parser using python.
Step 1: Installing the necessary libraries
The first step is to install the libraries that we will be using in our project. We will be using the following libraries:
- spaCy: A python library for natural language processing
- pandas: A python library for data manipulation and analysis
- PyPDF2: A python library for working with PDF files
You can install these libraries using pip:
Pip install spacy pandas PyPDF2
Step 2: Loading the spaCy model
The next step is to load the spaCy model that we will be using for NLP. SpaCy provides several pre-trained models for different languages. We will be using the English Language Model.
import spacy
nlp = spacy.load(‘en_core_web_sm’)
Step 3: Extracting information from the resume
We will be using the PyPDF2 library to extract text from PDF resumes. Once we have the text, we can use SpaCy to extract relevant information.
import PyPDF2
def extract_text_from_pdf(file):
with open(file, ‘rb’) as f:
pdfReader = PyPDF2.PdfReader(f)
text = ”
for page in pdfReader.pages:
text += page.extract_text()
return text
text = extract_text_from_pdf(‘resume.pdf’)
doc = nlp(text)
We can use spaCy’s built-in entities to extract relevant information from the resume:
name = None
email = None
phone = None
degree = None
university = None
experience = []
skills = []
for ent in doc.ents:
if ent.label_ == ‘PERSON’:
name = ent.text
elif ent.label_ == ‘PHONE’:
phone = ent.text
elif ent.label_ == ‘EMAIL’:
email = ent.text
elif ent.label_ == ‘DEGREE’:
degree = ent.text
elif ent.label_ == ‘UNIVERSITY’:
university = ent.text
elif ent.label_ == ‘EXPERIENCE’:
experience.append(ent.text)
elif ent.label_ == ‘SKILL’:
skills.append(ent.text)
print(‘Name:’, name
Conclusion
The resume parser project in python is a great example of how data science can be applied to solve a practical problem, using natural language processing techniques to extract relevant information from unstructured data and machine learning algorithms to classify and categorise it.