This project aims to improve hiring process by automating resume screening and providing comprehensive analysis using Big data and data analytics techniques. My goal is to save valuable time and improve the efficiency and accuracy of candidate evaluation.
One of the key challenges in resume screening is extracting relevant information from PDF files. To tackle this, I implemented a robust data extraction pipeline that intelligently parses the PDF files and extracts essential details such as candidate skills, experience, and education. By automating this process, I eliminated the need for manual data entry, saving valuable time and reducing human error.
The system uses advanced Mathematical manipulations and with uses of NLP algorithms to processing extract key information from resumes, including skills, experience, and education. It eliminates the need for manual screening, allowing recruiters to focus on higher-value tasks.
The system performs a thorough analysis of candidate qualifications, generating a detailed summary that highlights their strengths and areas of expertise. This analysis enables recruiters to make informed decisions quickly.
I am have developed an intuitive dashboard that presents visual insights derived from the resume data. The dashboard includes various graphs, such as the distribution of matching keywords, percentage distribution of candidate qualifications, and the distribution of resumes based on PDF filenames. These visualizations provide recruiters with valuable information at a glance.
As an added convenience, our system generates a concise summary file in PDF format for each candidate. This summary condenses the essential details from the resume, making it easy to reference and compare during the selection process.