Email parser python example how to#

NLP Tools and Techniques You Will Master in this SpaCy Resume Parser Project The dataset of resumes has the following fields: First, we train our model with these fields, then the application can pick out the values of these fields from new resumes being input. This resume parser uses the popular python library - Spacy for OCR and text classifications. To solve this, our resume parser application can take in millions of resumes, parse the needed fields and categorise them. It is one of the most exciting NLP projects for beginners, so make sure you attempt it. The resulting application will require minimum human intervention to extract crucial information from a resume, such as an applicantвЂ™s work experience, name, geographical location, etc.

Email parser python example how to#

And, considering all the resumes are submitted in PDF format, you will learn how to implement optical character recognition (OCR) for extracting textual data from the documents. This project uses PythonвЂ™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. Resume Parsing in Python Project Objective This is a waste of time, money, and productivity for the company. Either they need many people to do this or they miss out on qualified candidates.

How tiring, right? Well, there is an easy way out: building a Resume Parsing Application that takes resumes as input, then extracts and analyses all the valuable information from it.В Recruiters and HR teams in companies have a tough time scanning thousands of qualified resumes. Now, since this company didnвЂ™t provide the candidates with a resume format, it is your job to analyze each resume manually. Your task is to prepare a list of candidates suitable for the software engineer role. Imagine working as an intern in a companyвЂ™s Human Resource Department, and you have been provided with a massive pile of about 1000 resumes.