The process of screening resumes for job openings can be time-consuming and difficult, especially for larger organizations with a high volume of applications. To address this challenge, the aim is to design a program that will automate the screening process by using natural language processing (NLP) to analyze resumes and extract relevant data.
The first step in this process will be to use the PyPDF Python library to extract text from resumes in PDF format. This will enable the program to analyze the contents of each resume and identify relevant information such as education, work experience, and skills.
Next, the program will use NLP techniques to analyze the text and match it against the job description. This will involve identifying keywords and phrases that are relevant to the position and comparing them to the content of each resume. The program will then assign a score to each resume based on its relevance to the job description.
Once the program has screened all the resumes and assigned scores, the data will be visualized using matplotlib, a popular Python library for data visualization. This will enable recruiters and hiring managers to quickly identify the most qualified candidates and make informed decisions about which applicants to contact for further screening.
Overall, this program will significantly reduce the time and effort required to screen resumes and make the recruitment process much more efficient and effective. By leveraging the power of NLP and data visualization, organizations can improve their recruitment processes and make better hiring decisions.
