Smart AI Resume Analyzer

Abstract

1. Introduction

Smart AI Resume Analyzer is your all-in-one tool to analyze, optimize, and craft resumes that stand out, helping you land your dream job.

Stars Forks Issues Repo Size Open PRs Closed PRs Contributors Open Source

View Demo · Set-Up (Installation) · Project Report Built with by Het Patel A Project Submitted for the partial fulfilment of the degree BCA at Parul University during academic year 2024-25

2. Objective

📄 Smart Resume AI is a powerful tool designed to revolutionize your job application process. With features like 📚 professional template building, 🔍 ATS-friendly analysis, and 🎯 AI-driven optimization, it ensures your resume stands out to recruiters. Smart Resume AI provides everything you need to craft a tailored, eye-catching resume. 🚀

This project demonstrates how C can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Arduino IDE / PlatformIO or a C compiler (gcc)
• Target board where applicable
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Hunterdii/Smart-AI-Resume-Analyzer.git
cd Smart-AI-Resume-Analyzer

1. Clone the repository:
2. Create a Virtual Environment(Optional)

git clone https://github.com/Hunterdii/resume-analyzer-ai.git
   cd Smart-AI-Resume-Analyzer

python -m venv venv

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by Hunterdii and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.