An AI-Driven Resume Analyser with Job Recommender and Portfolio Code Generator

Abstract

JobHelper is an AI-driven platform designed to optimize and simplify the job application process. By generating professional resumes through customizable templates, the platform ensures that job seekers effectively present their skills and experience. In addition to resume creation, JobHelper analyzes and matches user profiles with relevant job opportunities, improving their chances of landing suitable roles. Furthermore, the platform automatically generates professional portfolio websites by providing HTML, CSS, and JavaScript code, allowing users to easily showcase their work. JobHelper\'s integrated approach aims to enhance job-seeking success and elevate users\' professional presence.

Introduction

The text describes JobHelper: An AI-Driven Resume Analyzer with Job Recommender and Portfolio Code Generator, an intelligent platform designed to simplify and improve the job application process using Artificial Intelligence (AI). The system helps users create professional resumes, analyze their resumes for skills and errors, receive personalized job recommendations, and generate portfolio websites automatically.

The platform consists of four main modules:

1. Resume Analysis Module
   This module extracts text from resumes uploaded in formats like PDF and DOCX using tools such as PyPDF2, python-docx, and Tesseract OCR. Natural Language Processing (NLP) libraries like spaCy and NLTK analyze the content to identify skills, qualifications, and job roles. Grammar and syntax errors are detected using Grammarly API or LanguageTool, improving resume quality and professionalism.
2. Job Recommendation Module
   Based on the extracted skills and user profile, the system recommends suitable job opportunities using machine learning algorithms such as cosine similarity and collaborative filtering. Job data is obtained through APIs like Indeed or LinkedIn, ensuring users receive relevant and personalized job suggestions.
3. Resume Generation Module
   Generative AI models such as GPT-4 and Hugging Face Transformers are used to create professional resumes automatically. Users can customize templates, edit content, preview resumes in real time, and export them in formats like PDF or DOCX.
4. Portfolio Code Generation Module
   The platform automatically generates responsive portfolio websites using HTML, CSS, JavaScript, and Bootstrap templates. User information is dynamically inserted into the templates, and the complete code is provided as a downloadable ZIP file.

The system uses a React.js frontend for an interactive user interface and a Node.js or Django backend for data processing and AI integration. MongoDB or MySQL databases are used for efficient data management, while deployment is supported on cloud platforms like AWS or Google Cloud for scalability and reliability.

The project also evaluates performance using metrics such as precision, recall, F1-score, mean reciprocal rank (MRR), and user satisfaction scores to ensure accuracy and effectiveness.

Conclusion

The project provides a comprehensive solution for job seekers to analyze their resumes and give them suggestions on what skills should they add and also suggests some courses along with the resume ATS score. The system uses a combination of NLP and machine learning techniques to provide accurate results. The system has the potential to improve job search outcomes and provide a competitive edge to job seekers. The AI-driven resume Analyzer with Job Recommender and Portfolio Code Generator is a comprehensive solution designed to streamline the job application process for modern job seekers. By integrating advanced technologies such as NLP, machine learning, and generative AI, the platform offers a range of features that address the key challenges faced by job seekers, from resume optimization to job matching and portfolio creation

References

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Copyright

Copyright © 2026 Md Laraib, Ansari Ashique Ali, Aman Khan, Khan Amaan, Mr. Ehtesham Hussain. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.