AI Resume Analyzer

Inspiration

Many job seekers struggle to understand whether their resume clearly highlights their technical skills and strengths. Recruiters often use automated systems to scan resumes, but candidates rarely get feedback on how to improve them. This inspired us to build an AI-powered tool that analyzes resumes and provides suggestions to help users improve their chances of getting shortlisted.

What it does

AI Resume Analyzer is a tool that analyzes resumes uploaded in PDF format and provides useful insights. It extracts text from the resume, detects technical skills using Natural Language Processing (NLP), generates a short summary, and provides suggestions for improvement. The system helps users understand which skills are present in their resume and how they can improve it to make it more effective.

How we built it

The project was built using Python and Streamlit for the web interface. We used the PyPDF library to extract text from PDF resumes and spaCy for Natural Language Processing to analyze the resume content. The system scans the text for important technical keywords and identifies relevant skills. Based on the extracted information, the application generates a summary and provides suggestions for improving the resume.

Challenges we ran into

One of the main challenges was accurately extracting text from different resume formats. Some resumes contain complex layouts that make text extraction difficult. Another challenge was identifying skills reliably using simple keyword matching. We also worked on ensuring that the application remains lightweight and runs smoothly without requiring heavy machine learning models.

Accomplishments that we're proud of

We successfully built a working AI-powered resume analyzer that can automatically extract skills and provide useful suggestions. The application is lightweight, easy to use, and runs quickly in a browser. We are proud that the tool demonstrates how AI and NLP can be applied to solve a real-world problem faced by job seekers.

What we learned

Through this project, we learned how to apply Natural Language Processing techniques to analyze real-world documents. We also gained experience working with PDF text extraction, building interactive applications using Streamlit, and designing user-friendly tools that provide practical insights from data.

What's next for AI Resume Analyzer

In the future, we plan to add advanced features such as resume scoring, job description matching, and personalized skill recommendations. We also aim to integrate machine learning models to improve skill detection accuracy and provide deeper insights. Another improvement would be creating a more advanced dashboard with visual analytics for resume evaluation.