AI based resume screening

About the Project

Inspiration

The inspiration behind our project, AI-Based Resume Screening, came from the challenges recruiters face when reviewing a large number of resumes. Manually screening resumes can be time-consuming and may lead to inconsistent evaluation. We wanted to build a system that could use Artificial Intelligence to make the initial screening process faster, more efficient, and objective.

What We Learned

Through this project, we learned how AI can be applied to a real-world recruitment problem. We gained practical knowledge in Natural Language Processing (NLP), resume parsing, text preprocessing, keyword and skill extraction, and candidate matching. We also learned how to connect an AI model with a user-friendly application and how to evaluate the relevance of resumes based on job requirements.

How We Built It

We developed the system by allowing users to upload resumes and provide the required job description or skills. The system processes the resume, extracts relevant information such as skills, education, experience, and qualifications, and compares it with the job requirements. Based on the matching results, the AI generates a relevance score and helps identify the most suitable candidates.

The overall workflow can be represented as:

$$ \text{Resume} \rightarrow \text{Text Extraction} \rightarrow \text{NLP Processing} \rightarrow \text{Skill Matching} \rightarrow \text{Candidate Score} $$

We focused on creating a simple interface so that the screening process could be easily understood and used by recruiters.

Challenges We Faced

One of the main challenges was handling resumes with different formats, layouts, and writing styles. Extracting meaningful information from unstructured resume content required careful text preprocessing. Another challenge was ensuring that the system could distinguish between relevant and irrelevant skills instead of relying only on exact keyword matches.

We also faced challenges in improving the accuracy of candidate matching and integrating the AI processing with the application. These challenges helped us understand that building an AI-based system requires not only a model but also proper data processing, validation, and continuous improvement.

Overall, this project helped us understand how AI and NLP can be used to solve practical problems in recruitment and gave us valuable experience in developing an end-to-end AI-based application.