Resume Screening System
This project is aimed at building a system to automate the process of screening resumes and categorizing them based on their relevance to a job description. The system parses resumes, extracts key information, and uses machine learning algorithms to rank candidates.
Project Overview: The project consists of the following components:
Data Collection and Preprocessing:
Resumes are collected from various sources in different formats such as PDF and DOCX. Text content is extracted from the resumes and standardized for further processing.
Key Information Extraction:
Skills, experience, and other relevant information are extracted from the resumes using natural language processing (NLP) techniques.
Job Description Parsing:
Job descriptions are parsed to identify key requirements such as skills, experience level, and educational qualifications.
Matching and Ranking:
Extracted information from resumes is compared against the job description to calculate relevance scores for each candidate.
Machine learning algorithms are used to rank candidates based on their relevance to the job.
Evaluation and Visualization:
The performance of the system is evaluated using metrics such as accuracy and classification reports.
Visualization tools such as word clouds are used to visualize the key information extracted from resumes.
Usage:
Place your resumes in the resumes folder.
Define the job description in a text file named job_description.txt.
Run the main scripts to parse resumes, extract information, and rank candidates.