Automated Resume Screening System

Automated Resume Screening System using Machine Learning (With Dataset)

AI & Machine LearningCSSMIT

Abstract

Automated Resume Screening System is an open-source AI & Machine Learning project. Automated Resume Screening System using Machine Learning (With Dataset). Used recommendation engine techniques such as Collaborative , Content-Based filtering for fuzzy matching job description with multiple resumes. It is built using CSS, Machine Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Used recommendation engine techniques such as Collaborative , Content-Based filtering for fuzzy matching job description with multiple resumes.

A web app to help employers by analysing resumes and CVs, surfacing candidates that best match the position and filtering out those who don't.

2. Objective

Automated Resume Screening System using Machine Learning (With Dataset)

This project demonstrates how CSS, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

CSSMachine Learning

5. System Requirements

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

  • A modern web browser
  • VS Code or any code editor
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/JAIJANYANI/Automated-Resume-Screening-System.git
cd Automated-Resume-Screening-System

Full setup instructions are in the project README.

7. Future Enhancements

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

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by JAIJANYANI 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.

View Source on GitHubDownload ZIP

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