Job Description Keywords Extractor

JobDescription-Keywords-Extractor aims to extract important keywords (topics) from any given job description posted online for better search engine optimization (SEO) using Topic Modeling techniques. It can also be used to build a Resume Screening Tool where any given resume would be matched against the topics extracted from JD_Keywords_Extractor.

AI & Machine LearningJupyter NotebookMIT

Abstract

Job Description Keywords Extractor is an open-source AI & Machine Learning project. JobDescription-Keywords-Extractor aims to extract important keywords (topics) from any given job description posted online for better search engine optimization (SEO) using Topic Modeling techniques. It can also be used to build a Resume Screening Tool where any given resume would be matched against the topics extracted from JD_Keywords_Extractor. It is built using Jupyter Notebook. 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

JobDescription-Keywords-Extractor aims to extract important keywords (topics) from any given job description posted online for better search engine optimization (SEO) using Topic Modeling techniques. It can also be used to build a Resume Screening Tool where any given resume would be matched against the topics extracted from JD_Keywords_Extractor.

Dataset used for building this model was obtained from Kaggle @ https://www.kaggle.com/madhab/jobposts

2. Objective

JobDescription-Keywords-Extractor aims to extract important keywords (topics) from any given job description posted online for better search engine optimization (SEO) using Topic Modeling techniques. It can also be used to build a Resume Screening Tool where any given resume would be matched against the topics extracted from JD_Keywords_Extractor.

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

4. Technology Stack

Jupyter Notebook

5. System Requirements

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

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/indranildchandra/JobDescription-Keywords-Extractor.git
cd JobDescription-Keywords-Extractor

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 indranildchandra 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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